Research

Back to Homepage
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
No items found.
Research
MOIS Linked MOIST GRASSHOPPER / Homeland Justice / KarmaBelow80 / Handala Hackers / Campaigns and Evolution

Explore the evolution of MOIS-linked actors Homeland Justice, Karma, and Handala. Analysis of destructive malware, surveillance integration, and the 2026 Stryker incident.

Executive Overview

The evidence examined across this analysis spanning U.S. government reporting, private-sector threat intelligence research, passive DNS and infrastructure enrichment, and longitudinal review of archived web and Telegram content supports a high-confidence assessment that the personas Homeland Justice, Karma, and Handala do not represent discrete or ideologically independent hacktivist groups. Rather, they constitute a coordinated, MOIS-aligned cyber influence ecosystem operating under multiple branded identities that serve distinct but complementary operational roles.

This assessment is supported by multiple converging lines of evidence, including clear temporal continuity, operational consistency, infrastructure linkage, and behavioral alignment. Activity transitions seamlessly from Homeland Justice operations targeting Albania in 2022 to Karma campaigns against Israeli entities in late 2023, and subsequently to Handala-branded operations from 2024 onward. Across these phases, the actors consistently employ a repeatable pattern of intrusion, data exfiltration, disruptive or destructive action, and rapid public disclosure through controlled infrastructure. This is reinforced by shared or cross-referenced domains, persistent use of Telegram for amplification and coordination, and common hosting and obfuscation strategies. The personas also exhibit consistent rhetorical framing, target selection logic, and methods of psychological coercion. Taken together, these indicators support the conclusion that these identities function as operational layers applied to a single underlying capability, enabling segmentation of audiences and messaging while maintaining continuity of tradecraft. This modular branding approach aligns with broader state-aligned cyber operations that leverage multiple personas to project decentralization while masking centralized control.

Since its emergence in 2022, the campaign has evolved from a destructive intrusion operation into a multi-functional cyber influence framework. The initial Albania operation combined long-term compromise with ransomware-style encryption, disk wiping, and public attribution, already indicating that technical disruption was paired with narrative objectives. Over time, the campaign expanded to incorporate espionage, persistent access, structured data exfiltration, and coordinated hack-and-leak activity designed to shape perception and behavior. The addition of surveillance capabilities, particularly those leveraging Telegram-based command-and-control, marks a further shift toward continuous monitoring and transnational repression targeting both institutions and individuals. In its current form, the campaign represents a cohesive and adaptive system in which intrusion, disruption, surveillance, and information operations are integrated into a unified strategy aligned with MOIS objectives, capable of applying sustained pressure across both cyber and cognitive domains.

Ministry of Intelligence and Security (MOIS) Connection

The operational ecosystem encompassing Handala, Homeland Justice, and the persona cluster associated with Karma and KarmaBelow80 is most coherently understood not as a loose federation of ideologically aligned actors, but as a structured, state-directed campaign operating under the authority of Iran’s Ministry of Intelligence and Security (MOIS). When viewed through the lens of command-and-control, tradecraft consistency, and synchronized effects, the activity attributed to these brands reflects the hallmarks of an intelligence service executing coordinated cyber operations in support of national objectives rather than independent or purely proxy-driven behavior.

At the center of this structure is the reported involvement of Seyed Yahya Hosseini Panjaki, an individual assessed to be affiliated with MOIS and linked to its internal security and counter-terrorism apparatus. The significance of this attribution lies less in the identity of the individual operator and more in what it implies structurally. His role represents a command-level function within an institutional hierarchy, indicating that these cyber operations are subject to formal tasking, oversight, and strategic alignment. This shifts the analytical framing away from contractor-driven or semi-deniable activity and toward a model in which operations are integrated into the broader intelligence mandate of the Iranian state.

Within this framework, the distinct public-facing identities of Handala, Homeland Justice, and KarmaBelow80 function as operational veneers rather than discrete entities. Each brand aligns with a specific subset of MOIS objectives while drawing from a shared pool of capabilities, infrastructure, and tradecraft. Handala’s activity is most closely aligned with psychological and information operations, characterized by curated leaks, narrative shaping, and the deliberate amplification of politically resonant material. The timing and framing of these disclosures indicate coordination with broader messaging goals, suggesting that the technical intrusion component is only one phase of a larger influence cycle.

Homeland Justice, by contrast, represents the disruptive and punitive arm of this ecosystem. Its operations, particularly those conducted against Albanian government infrastructure, demonstrate a full-spectrum intrusion lifecycle in which long-term access is leveraged to enable data exfiltration, destructive deployment, and overt attribution. The combination of wiper activity, ransomware-style encryption, and coordinated public messaging reflects a model of calibrated escalation designed to impose both operational and reputational costs on the target. This is consistent with MOIS mandates involving internal security and retaliatory action against perceived adversaries.

The Karma and KarmaBelow80 personas introduce an additional layer of flexibility into the ecosystem. Rather than being tied to a single operational profile, these identities appear to function as adaptive interfaces that can be deployed across different phases of an operation. They enable the same underlying capability set to be presented under different contextual narratives, enhancing deniability while maintaining continuity of effect. This is particularly relevant in environments where attribution pressure is high, as it allows operators to fragment their public footprint without fragmenting their operational infrastructure.

The coherence across these actor clusters is most evident in the structure of their operations. Intrusions frequently follow a repeatable progression: initial access is established through credential compromise or exploitation of exposed services, followed by the deployment of webshells or other persistence mechanisms. Once footholds are secured, actors conduct internal reconnaissance and lateral movement using enterprise-scale tooling, enabling them to map the target environment and identify high-value data stores. Exfiltration is then carried out in a controlled manner, often staged to support subsequent public release. The final phase varies depending on strategic intent, ranging from silent intelligence collection to destructive action or coordinated leak publication.

What distinguishes this ecosystem is the degree to which these phases are integrated and interchangeable. The same intrusion can evolve from a covert surveillance operation into a disruptive attack or an influence campaign without requiring a fundamental shift in tooling or access. This reflects the modular architecture described earlier, but at an organizational level it also implies centralized capability management. MOIS oversight provides the mechanism through which these modules can be allocated, combined, and sequenced in accordance with mission objectives.

The involvement of a command-level figure such as Panjaki provides a unifying explanation for this consistency. It accounts for the alignment between technical operations and information effects, the disciplined escalation observed in target engagements, and the reuse of infrastructure and tooling across ostensibly separate actor brands. It also explains the resilience of the ecosystem. Disrupting one public-facing identity or infrastructure cluster does not degrade the underlying capability, because those assets are components of a larger, centrally managed system.

From an analytical standpoint, this structure necessitates treating Handala, Homeland Justice, and KarmaBelow80 as manifestations of a single operational apparatus rather than independent threat actors. Their differences are functional rather than organizational, reflecting the segmentation of roles within a coordinated campaign. The strategic value of this model lies in its flexibility: MOIS can conduct espionage, disruption, and influence operations in parallel, or transition between them as conditions dictate, all while maintaining a coherent operational footprint.

This convergence of command authority, modular capability, and multi-domain execution underscores the maturation of MOIS cyber operations into a fully integrated instrument of state power. It is not simply the presence of advanced tooling or destructive capability that defines this ecosystem, but the way in which those capabilities are orchestrated under centralized direction to produce layered, cumulative effects across technical and informational domains.

Campaign Expansion and Evolution

Initial Emergence in Albania (2022)

Homeland Justice[.]org website

The campaign first became publicly visible during the 2022 attacks against the Government of Albania, which established both its technical baseline and its enduring operational model. Iranian state actors operating under the Homeland Justice persona achieved initial access approximately fourteen months prior to public disclosure by exploiting an internet-facing Microsoft SharePoint vulnerability. This early foothold indicates a deliberate pre-positioning phase, consistent with long-dwell intrusion strategies observed across MOIS-aligned operations.

Following initial compromise, the actors transitioned into a structured post-exploitation workflow designed to ensure persistence, expand access, and map the target environment. Webshells were deployed on compromised servers, providing durable and low-friction access while enabling command execution without reliance on large malware payloads. From this foothold, operators conducted systematic internal reconnaissance, enumerating network topology, identifying key systems, and mapping trust relationships across the enterprise.

Credential harvesting was a central component of this phase. Through a combination of mailbox access, credential dumping, and account manipulation, the actors obtained privileged credentials that enabled lateral movement and escalation. Movement across the environment was conducted using standard administrative protocols, including Remote Desktop Protocol (RDP), Server Message Block (SMB), and File Transfer Protocol (FTP), allowing activity to blend with legitimate administrative traffic and reducing the likelihood of early detection.

The compromise of Microsoft Exchange infrastructure further expanded access. By leveraging Exchange, the actors were able to access and manipulate mailboxes, create or modify accounts, and extract large volumes of sensitive communications. This email corpus provided both intelligence value and material for later disclosure, aligning with the campaign’s hack-and-leak model.

Data exfiltration occurred in parallel with lateral expansion, with operators systematically staging and extracting large datasets from across the environment. Only after sufficient access, intelligence collection, and data acquisition had been achieved did the actors transition to the destructive phase. This sequencing – extended pre-positioning, comprehensive collection, and delayed disruption – demonstrates a disciplined operational approach in which technical compromise is leveraged to maximize both intelligence yield and downstream psychological impact.

Establishment of the Operational Model

The impact phase of the Albania operation combined ransomware-style encryption with destructive wiping, employing tools such as GoXML.exe and cl.exe, supported by propagation utilities and raw disk access drivers that enabled direct manipulation of underlying storage. These capabilities were deployed in a coordinated manner to maximize operational disruption, impair system recovery, and degrade institutional functionality. The sequencing of encryption followed by wiping reflects a deliberate approach designed not only to deny access to systems and data, but to ensure lasting damage and complicate remediation efforts.

More significant than the tooling itself, however, was the deliberate integration of public-facing infrastructure into the attack lifecycle. The Homeland Justice persona was used to claim responsibility, disseminate messaging, and frame the operation within a broader political and ideological narrative. Websites and Telegram channels functioned as controlled dissemination platforms through which the actors published statements, amplified claims, and selectively exposed information. This layer transformed what would otherwise have been a destructive cyber incident into a visible and ongoing influence operation.

This approach established a foundational operational model in which technical compromise and information operations were inseparably linked. Cyber intrusion and destruction served as enabling mechanisms for narrative exploitation, with the ultimate objective extending beyond disruption to include coercion, reputational damage, and behavioral influence. In this model, the value of the operation was realized not solely through the technical impact, but through the controlled release of information and the shaping of perception in the aftermath of the attack.

Continued Activity and Tooling Refinement (2023)

In late 2023, the Homeland Justice campaign re-emerged with renewed activity targeting Albanian entities, demonstrating clear continuity in both target selection and operational methodology. This phase reinforced that the earlier Albania operations were not isolated incidents, but part of a sustained and adaptive campaign. The actors maintained their focus on politically relevant targets while reapplying the same core model of intrusion followed by destructive impact, indicating both persistence of intent and retention of operational access or capability.

During this period, the introduction of the No-Justice Wiper marked a refinement in destructive tooling. Designed for rapid and irreversible disruption, the malware emphasized system incapacitation, including preventing successful operating system boot. The use of signed binaries suggests an increased emphasis on evasion and trust abuse, while PowerShell-based propagation reflects a growing reliance on native system capabilities to enable flexible and low-friction deployment. Together, these developments indicate an evolution toward more efficient, harder-to-detect operations while preserving the campaign’s core objective of high-impact disruption.

Geographic Expansion and Rebranding: Karma Phase (2023-2024)

Following the Israel-Hamas conflict in October 2023, the campaign expanded geographically and adopted the Karma persona.

Despite this rebranding, the underlying tradecraft remained consistent. Operations targeted Israeli organizations and employed a hybrid approach combining custom tooling with publicly available utilities, including bespoke webshells, credential validation tools, reverse SSH tunneling, and destructive mechanisms such as BiBi Wiper.

Actors increasingly relied on hands-on-keyboard techniques, including manual file deletion and disk formatting, prioritizing speed and operational impact. Evidence from this phase suggests a division of labor between intrusion and destructive operators, indicating a modular and coordinated ecosystem.

Maturation and Specialization: Handala Phase (2024 Present)

Handala-hack.tw 2026

The expansion of the Handala infrastructure set with the inclusion of handala-hack[.]ps and, more importantly, handala-hack[.]tw, provides a clearer view into how the actor operationalizes its domain layer over time. These domains are not isolated artifacts. They are part of a repeatable system in which naming conventions, narrative timing, and platform coordination matter more than persistence of any single asset. The repeated appearance of the handala-hack string across multiple TLDs indicates that the domain itself is not intended to endure. It is intended to be recognized, replaced, and reactivated, carrying forward an identity that survives takedown actions and jurisdictional pressure.


The .tw variant is particularly instructive when placed in historical context. Earlier iterations of the ecosystem relied heavily on .to infrastructure, which has long been associated with abuse-tolerant hosting and low-friction registration. The shift into .ps and .tw reflects both symbolic and operational adaptation. The .ps domain carries clear political signaling aligned with the actor’s ideological framing, reinforcing the Palestinian narrative embedded throughout the campaign. By contrast, handala-hack[.]tw appears to serve a different function: jurisdictional dispersion and operational redundancy. Taiwan’s namespace does not inherently carry the same ideological weight, which suggests its use is pragmatic rather than symbolic. In effect, the actor is separating message-layer signaling (.ps) from resilience-layer infrastructure (.tw).

When mapped against the historical leak cadence observed across the full archive, these domains align with distinct phases of campaign activity. Early in the lifecycle, Handala has relied on single-domain publication points tied to specific claim sets. These initial leaks focused on individual targets, often framed as penetrations of named Israeli intelligence or defense figures. The content emphasized access mailboxes, communications, and internal correspondence without attempting to demonstrate systemic reach. Domains in this phase acted as announcement boards, each tied to a discrete narrative event.

As the campaign matured, the scale of claims expanded. The archive shows repeated assertions of large-volume email exfiltration, often in the range of tens of thousands to over one hundred thousand messages. These claims were accompanied by broader institutional framing, suggesting not just individual compromise but organizational penetration. It is in this phase that domain rotation becomes more pronounced. Rather than maintaining a single persistent site, the actor begins to distribute content across multiple similarly branded domains, each capable of hosting or referencing new disclosures. The emergence of domains like handala-hack[.]to and handala-redwanted[.]to reflect this shift toward functionally differentiated nodes, one for breach claims, another for intimidation or doxxing.

The introduction of handala-hack[.]tw appears to correspond with the later stages of this evolution, where the campaign moves beyond exposure into strategic signaling and maximalist claims. Posts associated with this period increasingly reference infrastructure targeting, large-scale destructive capability, and systemic access. Claims such as multi-petabyte data wipes or pre-mapped critical infrastructure targets emerge alongside continued email leak narratives. The domain layer, in this context, becomes less about hosting data and more about anchoring the claim itself. The presence of a new domain signals a new phase of activity, regardless of whether the underlying data is verifiable.

Historically, each wave of leaks follows a recognizable pattern. A new or resurfaced domain appears, often with the familiar handala-hack naming structure. Within a short time window, posts are published asserting compromise of a high-value target. These posts are then amplified through Telegram channels now including identifiers such as @HANDALA_INTEL and further propagated via X accounts. The domain serves as the canonical reference point, but the operational impact is generated through distribution. Even when domains are seized or taken offline, the narrative persists because it has already been exported to other channels.

The content associated with these domains consistently emphasizes three categories of disclosure. The first is email data, which remains the dominant theme across the archive. Whether targeting individuals like Eran Ortal or broader institutional mailboxes, the actor repeatedly frames access to communications as evidence of deep penetration. The second category is identity and contact data, including phone numbers and membership lists, often used in campaigns against dissidents or civilian networks. The third is strategic or infrastructural intelligence, where the actor claims to possess detailed knowledge of critical systems such as water and electricity networks. Each category serves a distinct psychological function: exposure, intimidation, and deterrence.


The inclusion of corporate targets, such as Stryker Corporation, marks another important shift visible in the historical record. Earlier phases of the campaign were tightly focused on Israeli state and intelligence entities. Later phases expand outward, incorporating Western corporate actors to demonstrate global reach. The claims associated with these targets are often the most extreme, including assertions of large-scale data destruction. Whether or not these claims are technically accurate is secondary to their narrative role. They signal that the actor’s reach is not confined to a single geography or sector.

Across all these phases, the domain layer including handala-hack[.]tw remains structurally consistent. The sites themselves are simple, often WordPress-based, with minimal technical sophistication. They do not host malware, nor do they expose command-and-control infrastructure. Instead, they function as narrative anchors, providing a stable URL that can be cited, shared, and referenced across platforms. The real operational activity occurs elsewhere, in the intrusion layer (which remains opaque) and the amplification layer (Telegram and X). The domain is simply the point where those layers intersect publicly.

What emerges from the full dataset, now augmented by the newly observed domains, is a clear pattern: Handala’s infrastructure is designed to be expendable, but its identity is designed to persist. Domains are created, used, and abandoned. Telegram channels are taken down and reconstituted. Yet the naming conventions handala-hack, HANDALA_ and the narrative structure remain constant. This allows the actor to survive disruption without losing coherence. Each new domain, including handala-hack[.]tw, is not a fresh start but a continuation of an ongoing campaign.

In historical context, the leaks themselves should be understood not as isolated incidents but as components of a sustained psychological operation. Early disclosures establish credibility, mid-phase leaks expand perceived capability, and later claims introduce strategic and deterrent messaging. The domain infrastructure evolves in parallel, moving from single-use publication points to a distributed, rotating set of narrative nodes. The result is a system in which the appearance of a new domain is itself a signal, an indication that the next cycle of claims, amplification, and psychological effect is underway.

Ultimately, the addition of handala-hack[.]tw does not represent a new capability. It represents the continued refinement of an existing model. The actor does not depend on any specific domain to achieve its objectives. Instead, it relies on the predictable regeneration of infrastructure combined with consistent narrative execution. In that sense, the domain is not the asset. The campaign is.

Adverse Effects and Real-World Impact of Handala Leak Operations

Analysis of the Handala archive, corroborated with external reporting, demonstrates a consistent divergence between claimed impact and verified operational consequences. While the group presents its activities as large-scale, destructive cyber intrusions, the observable real-world effects fall into a narrower set of categories: operational disruption (rare), exposure and reputational damage (common), and psychological or coercive effects (systematic).

The most clearly substantiated case of material operational impact is the attack against Stryker Corporation. Reporting indicates that the intrusion disrupted core business functions, including order processing, manufacturing, and shipment operations, with recovery extending over multiple days. The incident also reportedly resulted in the remote wiping of tens of thousands of devices, affecting employees across multiple regions and, in some cases, impacting personally owned devices enrolled in enterprise systems. This represents a genuine destructive and operational cyber event, distinguishing it from the majority of Handala’s activity. The scale and severity of this incident further triggered a law enforcement response, including domain seizures targeting Handala infrastructure, indicating that the event crossed the threshold from influence activity into infrastructure-level concern.

A second category of confirmed impact involves high-profile personal data exposure, exemplified by the breach of Kash Patel. In this case, the publication of personal emails, images, and documents created reputational harm and potential counterintelligence risk, even though the exposed material was not assessed as containing sensitive government information. The significance of this event lies less in technical compromise and more in its function as a public humiliation and signaling operation, consistent with Handala’s broader objectives of intimidation and reputational pressure against Western officials.

Other reported incidents fall into a more ambiguous category. The claimed attack against Hebrew University of Jerusalem, involving tens of terabytes of wiped and exfiltrated data, represents a credible, but not fully independently verified, destructive event. While multiple secondary sources describe the incident, there is limited primary confirmation of the full scale of impact. This pattern — where claims are partially supported but not conclusively validated — is characteristic of the Handala ecosystem and complicates direct attribution of operational consequences.

In contrast, some claims appear to have produced primarily reputational or narrative effects without technical confirmation. The alleged compromise of Verifone, for example, was publicly denied by the company, with no evidence of disruption or data loss. In such cases, the adverse effect is not system compromise but forced defensive communication, in which the targeted organization must respond to public allegations, thereby amplifying the narrative regardless of its validity.

A substantial portion of the archive consists of operations targeting individuals within the Israeli intelligence and security ecosystem, including Sima Shine, Ilan Steiner, Deborah Oppenheimer, and Eran Ortal. In these cases, the adverse effects are consistently limited to exposure of alleged communications, reputational damage, and intelligence-related pressure. Although large-scale email leaks are claimed, there is no strong independent evidence of downstream operational disruption, institutional failure, or policy impact. These incidents function primarily as hack-and-leak influence operations, designed to erode trust and project vulnerability rather than to degrade capability.

The campaign also includes a distinct category of identity exposure and intimidation, illustrated by the targeting of VahidOnline. The leak of tens of thousands of user identities and phone numbers associated with dissident networks constitutes a form of digital repression, exposing individuals to harassment, surveillance, or potential physical risk. Unlike corporate or institutional targets, the impact here is distributed across a population, amplifying fear and discouraging participation in opposition or media activities.

Beyond digital exposure, the archive and supporting reporting indicate a pattern of coercive escalation into the physical domain. Handala has been linked to doxxing campaigns against individuals such as defense-sector employees, including alleged exposure of personal details of engineers associated with defense contractors. These actions are often accompanied by explicit threats, transforming cyber activity into psychological coercion with potential real-world implications. Even when the accuracy of the leaked data is uncertain, the act of publication itself generates fear and imposes a defensive burden on victims.

Taken together, these cases demonstrate that Handala’s operational impact is best understood across three tiers. At the highest tier are rare but significant operational disruptions, such as the Stryker incident, which produce measurable effects on systems and services. At the intermediate tier are verified exposures of personal or organizational data, which create reputational and intelligence risks but do not necessarily disrupt operations. At the lowest tier are narrative-driven claims and unverified leaks, which nonetheless generate psychological and informational effects by forcing responses and sustaining uncertainty.

The overall pattern supports a clear analytical conclusion: Handala’s effectiveness does not depend on consistent technical success. Instead, it derives from the ability to convert a limited number of real or plausibly real intrusions into a sustained campaign of perception management, intimidation, and coercive signaling. The majority of adverse events observed are therefore not technical in nature, but psychological and reputational, aligning closely with the broader doctrine of cyber-enabled influence operations.

Parallel Surveillance and Influence Operations

Expansion into Surveillance (2023-2026)

In parallel with its destructive and hack-and-leak operations, the campaign expanded significantly into surveillance and transnational repression beginning in late 2023 and continuing through 2026. This shift represents a broadening of operational scope from institutional disruption to targeted monitoring of individuals, particularly dissidents, journalists, activists, and members of opposition networks. Rather than relying solely on network exploitation, the actors adopted a more tailored approach centered on social engineering and user-level compromise, indicating both improved targeting intelligence and a strategic intent to exert pressure beyond traditional cyber domains.

Access in this surveillance branch is typically achieved through trojanized applications masquerading as legitimate software, including messaging tools, password managers, and media utilities. These lures are often aligned with the expected behavior and digital environment of the target, suggesting prior reconnaissance and profiling. Upon execution, these files deploy staged malware chains that establish persistence and initiate communication with operator-controlled infrastructure. The second-stage implants are modular and designed for continuous data collection, including screen capture, audio interception (with specific capability to monitor conferencing platforms), file harvesting, and credential access. Data is often staged locally, compressed, and prepared for exfiltration in a manner that minimizes detection while maximizing collection efficiency.

A defining characteristic of this surveillance capability is its use of Telegram as a command-and-control channel, leveraging the legitimate Telegram API to transmit instructions and exfiltrated data. This approach allows malicious traffic to blend with normal user activity, complicating network-based detection while simultaneously enabling rapid, distributed control of infected hosts. In some cases, the same platform is used for both covert communication and overt messaging, reinforcing the campaign’s broader integration of technical and informational operations. Taken together, this surveillance expansion demonstrates a transition toward a persistent, person-centric operational model, in which intrusion, monitoring, and psychological pressure are applied in tandem to influence both institutional behavior and individual decision-making.

Telegram as Dual-Use Infrastructure

Telegram occupies a central and multifaceted role within this ecosystem, functioning simultaneously as a covert command-and-control (C2) channel and an overt platform for messaging, amplification, and audience engagement. This dual-use design is not incidental; it reflects a deliberate operational choice to consolidate multiple functions, control, communication, and influence within a single, widely trusted platform. By embedding operational activity within a legitimate and globally used service, the actors reduce their reliance on dedicated infrastructure while increasing resilience against disruption. At the same time, the platform’s scale and accessibility allow it to serve as a high-capacity distribution mechanism for narrative content, enabling rapid dissemination of messaging to both targeted and broad audiences.

From a technical perspective, the use of Telegram as C2 is enabled through abuse of the Telegram Bot API, which allows malware to communicate with operator-controlled bots over encrypted channels that are indistinguishable from normal application traffic. This design significantly complicates detection, as network telemetry alone is often insufficient to differentiate benign from malicious use. Implants can issue commands, upload exfiltrated data, and receive tasking through standard API calls, effectively turning Telegram into a low-cost, low-visibility control infrastructure. Because Telegram traffic is commonly permitted in enterprise and personal environments, this approach provides a reliable communication channel that blends seamlessly into expected user behavior, reducing the likelihood of interception or blocking.

Concurrently, Telegram channels associated with the campaign function as public-facing dissemination nodes, distributing propaganda, operational claims, and references to leaked data. Channels such as those linked to the Homeland Justice persona serve as hubs where messaging is curated, amplified, and framed within ideological or political narratives. The presence of archive files, named data releases, and coordinated messaging indicates that these channels are not passive outlets, but active components of the operational workflow. This convergence of covert C2 and overt communication within the same platform effectively bridges the technical and informational domains, allowing the actors to move seamlessly from intrusion and data collection to public exposure and psychological influence, all within a unified infrastructure.

Malware and Operational Evolution

Evolutionary Overview

The campaign demonstrates a progression from discrete, high-impact destructive events into a modular and adaptive operational toolkit capable of supporting a wide range of objectives across multiple target sets. Early activity, particularly during the Albania operations, was centered on singular, coordinated events in which long-term access culminated in ransomware-style encryption, wiping, and public attribution. Over time, however, these capabilities were not abandoned; instead, they were retained and integrated into a broader operational framework that supports espionage, surveillance, disruption, and influence operations in parallel.

This evolution is distinctly additive rather than substitutive. Earlier destructive tools and techniques  such as disk wiping, scripted propagation, and webshell-based persistence remain in active use, while newer capabilities have been layered on top. These include modular malware implants for surveillance, Telegram-based command-and-control, enterprise-scale tooling for network enumeration and control, and structured leak infrastructure for public disclosure. The result is a toolkit that can be dynamically assembled based on operational requirements, allowing actors to shift between stealthy collection, overt disruption, and psychological operations without fundamentally altering their underlying tradecraft.

The modular nature of this toolkit also enables operational flexibility and resilience. Components can be deployed independently or in combination, depending on the target environment and desired outcome. For example, an intrusion may begin as a surveillance operation, transition into data exfiltration, and culminate in either destructive action or controlled leak publication, all using elements of the same toolkit. This layered approach reduces dependency on any single capability and allows the campaign to adapt to defensive pressures, infrastructure disruption, or shifting strategic priorities while maintaining continuity of effect.

Phase I: Destructive Intrusion Model

The Albania campaign established a repeatable model centered on prolonged, covert access followed by synchronized destructive impact and overt attribution. Operators achieved initial entry well in advance of the impact phase, maintained persistence through webshells and credential reuse, and conducted systematic reconnaissance and lateral movement across the environment. During this period, they harvested credentials, mapped network topology, and accessed email systems, enabling large-scale data exfiltration and the prepositioning of tools required for coordinated execution. This extended preparation phase indicates a deliberate emphasis on operational depth and positioning, rather than opportunistic disruption.

The transition to the impact phase was tightly orchestrated. Encryption and wiping components were deployed in sequence to maximize disruption, degrade recovery options, and ensure sustained operational impact across affected systems. The use of propagation mechanisms and administrative access allowed the actors to execute these actions broadly and nearly simultaneously, amplifying the scale of disruption. This was not a simple ransomware event; it was a destructive operation designed to disable systems, disrupt services, and create immediate strategic effect, particularly within government infrastructure.

Equally important was the deliberate and immediate move to public attribution and narrative control. Under the Homeland Justice persona, the actors claimed responsibility, released messaging, and framed the attack within a political and ideological context. This transformed the operation from a purely technical incident into a hybrid cyber–influence event, where the technical damage served as the foundation for broader psychological and reputational impact. The Albania campaign thus established a durable operational model: gain long-term access, prepare the environment, execute coordinated destruction, and rapidly exploit the event through controlled public disclosure to achieve strategic influence.

Phase II: Iterative Destructive Refinement

The introduction of the No-Justice Wiper reflects a clear refinement in the actor’s destructive capability, emphasizing speed, reliability, and operational efficiency. Compared to earlier tooling that combined encryption and wiping in a more sequential and resource-intensive manner, the No-Justice variant appears optimized for rapid execution and immediate impact. Its design prioritizes system incapacitation, including corruption of critical structures required for operating system startup, thereby preventing recovery through conventional means. This shift indicates a move toward direct, irreversible disruption, reducing the time between execution and effect while minimizing the opportunity for defensive response.

At the same time, the use of signed binaries demonstrates an increased focus on evasion through trust abuse. By leveraging code-signing mechanisms, the actors are able to bypass or reduce scrutiny from endpoint protection systems that rely on signature-based trust models. This reflects a more sophisticated understanding of defensive controls and suggests that the tooling has been adapted based on prior detection and mitigation efforts. The combination of trusted execution and destructive functionality allows the malware to operate with a lower likelihood of immediate detection, increasing the probability of successful deployment across multiple systems.

The reliance on PowerShell-based propagation and execution further underscores a transition toward living-off-the-land techniques. By utilizing native system capabilities, the actors reduce their dependency on large or complex malware payloads, enabling more flexible and stealthy deployment within compromised environments. PowerShell scripts can be rapidly modified, obfuscated, and distributed using existing administrative channels, allowing for efficient lateral spread and coordinated execution. Together, these elements – streamlined destructive logic, trust-based evasion, and native execution – illustrate a broader evolution toward leaner, more adaptable tooling that enhances both effectiveness and survivability within contested network environments.

Phase III: Hybrid Operational Model

The Karma phase introduced a distinctly hybrid operational approach that combined bespoke tooling with hands-on-keyboard techniques and widely available utilities, enabling flexible execution across heterogeneous environments. Rather than relying exclusively on custom malware, operators blended lightweight webshells and purpose-built components with native administrative tools and publicly available software. This reduced development overhead, shortened deployment time, and allowed rapid adaptation to differences in target infrastructure  whether on-premises Windows domains, Linux servers, or mixed environments while preserving the ability to execute high-impact actions.

A defining feature of this phase was the increased emphasis on manual tradecraft and living-off-the-land techniques. Operators leveraged standard system utilities and administrative protocols such as RDP for lateral movement, built-in command-line tools, and common utilities like SDelete or disk formatting to perform destructive actions without introducing large, easily detectable binaries. Custom elements, including webshells (e.g., Karma Shell) and credential validation tools (e.g., do.exe), were used selectively to maintain access and verify privileges, while publicly available tools such as reGeorg enabled post-compromise control. This blend allowed operators to pivot quickly within compromised networks, execute tasks with minimal friction, and evade signature-based defenses by masking activity as legitimate administration.

The result was a modular, operator-driven execution model that prioritized flexibility, speed, and reliability over stealth alone. By combining custom implants with manual techniques and off-the-shelf tools, the actors could tailor operations to the specific constraints of each target, escalate privileges, propagate across systems, and initiate disruption with minimal dependency on a single capability. This hybridization also improved resilience: if one tool or method was detected or blocked, operators could readily substitute alternatives without disrupting the overall operation. In effect, the Karma phase marked a transition toward a more adaptable and scalable approach, capable of sustaining coordinated campaigns across diverse technical environments while maintaining alignment with the campaign’s broader objectives of disruption and influence.

Phase IV: Multi-Vector Destruction and Enterprise Tooling

Under the Handala persona, operations expanded into a coordinated, multi-method destruction model that reflects a clear increase in operational maturity and planning discipline. Rather than relying on a single payload or technique, actors began employing parallel destructive mechanisms, including custom wipers, PowerShell-based recursive deletion, and encryption via legitimate tools such as VeraCrypt. These methods were often executed in tandem across multiple systems, ensuring redundancy in effect and significantly reducing the likelihood of recovery. This approach demonstrates a shift from opportunistic disruption to deliberate, layered impact engineering, where multiple techniques reinforce one another to guarantee system failure and data loss.

At the same time, the incorporation of enterprise-scale tooling enabled the actors to operate more effectively within complex network environments. Tools such as NetBird facilitated persistent internal connectivity and remote control across segmented networks, while ADRecon provided comprehensive visibility into Active Directory structures, users, and permissions. This combination allowed operators to map target environments in detail, identify high-value systems, and coordinate execution across domains with greater precision. The use of Group Policy and administrative scripting further enabled centralized deployment of destructive actions, amplifying scale and synchronizing impact across large portions of the network.

These developments collectively indicate a transition toward a structured, scalable operational model capable of sustaining complex campaigns within enterprise environments. The reliance on both custom and legitimate tools, combined with coordinated execution and network-wide visibility, reflects an evolution beyond isolated incidents into systematic, organization-level disruption capability. Under Handala, the campaign demonstrates not only technical sophistication, but also an increased ability to integrate access, control, and destruction into a cohesive and repeatable operational framework aligned with broader strategic objectives.

Phase V: Surveillance Integration

Telegram-based malware campaigns introduced a persistent monitoring layer that materially expanded the campaign’s scope beyond episodic disruption into continuous intelligence collection. Initial access is commonly achieved through trojanized applications tailored to the target’s context, such as messaging clients, password managers, or media tools suggesting prior reconnaissance and profiling. Once executed, staged loaders deploy modular implants that establish persistence and enable ongoing telemetry collection, including screen capture, keystroke or clipboard capture, file harvesting, and, in some cases, audio interception of conferencing applications. Data is typically staged locally, compressed, and queued for exfiltration, allowing operators to control the cadence of collection and minimize anomalies that might trigger detection.

A defining feature of this capability is the use of Telegram’s Bot API as command-and-control, which allows implants to communicate over encrypted channels indistinguishable from normal Telegram traffic. This design provides a resilient, low-cost infrastructure that blends into expected network behavior and is difficult to block without disrupting legitimate use. Operators can issue tasking, retrieve data, and manage multiple hosts via bot commands, effectively turning Telegram into a distributed control plane. Because Telegram is widely permitted across enterprise and personal environments, this approach increases reliability while reducing dependence on bespoke infrastructure that is more easily identified and taken down.

Operationally, this surveillance layer supports transnational repression by enabling targeted, person-centric campaigns against dissidents, journalists, and opposition figures. Continuous monitoring yields sensitive personal and organizational insights that can be selectively disclosed, used to intimidate, or leveraged to shape narratives in subsequent leak operations. This tight coupling between covert collection and overt exposure allows actors to move seamlessly from surveillance to psychological pressure, aligning technical activity with influence objectives. The result is a persistent, adaptive capability that extends the campaign’s reach from network disruption to sustained coercion of individuals and communities across borders.

Phase VI: Convergence (Stryker-Era Operations)


Recent activity, particularly the Stryker incident (March 2026), demonstrates a clear convergence of destructive, surveillance, and influence capabilities into a unified operational model, while also marking a significant evolution in how these effects are delivered. Unlike earlier phases that relied heavily on malware deployment within compromised networks, emerging reporting indicates that Handala-linked actors achieved administrative access to enterprise management infrastructure, specifically Microsoft Intune, and used it as a force-multiplier for both disruption and scale. (Cyber Magazine)

In the Stryker case, investigators and multiple reports suggest that attackers compromised an Intune administrative account or management console, allowing them to issue remote commands directly to enrolled endpoints. Intune, as a cloud-based endpoint management platform, is designed to enforce policies, deploy software, and remotely wipe devices. By gaining privileged access to this system, the actors were able to bypass traditional malware deployment entirely and instead execute a centralized wipe command across tens of thousands of devices simultaneously. Reports indicate that as many as 80,000–200,000 devices, including laptops and mobile endpoints, were wiped, while approximately 50 terabytes of data were exfiltrated prior to the destructive action. (TechRadar)

This technique represents a fundamental shift in operational tradecraft. Rather than relying on endpoint-level persistence and execution, the actors targeted the control plane of the enterprise itself. With access to Intune, they effectively inherited the organization’s own administrative authority, allowing them to push destructive actions at scale with minimal friction and high reliability. As one analysis noted, once inside such a system, “an adversary… just need[s] to press a button,” highlighting how legitimate enterprise tooling can be weaponized for immediate, large-scale impact.

The implications of this approach are significant. First, it dramatically reduces the need for detectable malware artifacts, complicating traditional detection strategies that rely on endpoint indicators. Second, it enables near-instantaneous, synchronized disruption across globally distributed infrastructure, as seen in the simultaneous impact across dozens of countries in the Stryker event. Third, it allows actors to combine data exfiltration, destructive wiping, and public attribution within a tightly compressed timeline, reinforcing the campaign’s hack-and-leak model while increasing operational tempo. (Tenable)

Critically, this evolution does not replace earlier capabilities but integrates with them. The same ecosystem that previously relied on wipers, PowerShell scripts, and Telegram-based command-and-control now demonstrates the ability to pivot into identity and access compromise at the enterprise management layer, effectively collapsing the distinction between intrusion, execution, and impact. In this model, surveillance capabilities provide intelligence and targeting, administrative compromise enables execution at scale, and influence infrastructure  websites and Telegram amplify the effects through public messaging and data release.

Taken together, the Stryker incident illustrates the campaign’s progression into a fully converged operational framework, where destructive, surveillance, and influence capabilities are no longer sequential phases but simultaneous, interdependent components. The abuse of Intune marks a notable escalation in both technical sophistication and strategic impact, demonstrating that the actors are not only adapting their tooling, but are increasingly targeting the centralized control mechanisms of modern enterprise environments to achieve rapid, large-scale disruption aligned with broader geopolitical objectives.

Operational Model: Hack-and-Leak as Psychological Operations

The Homeland Justice and Handala campaigns are best understood as state-directed hack-and-leak operations engineered for psychological impact, in which technical intrusion serves as a means to produce exploitable narratives rather than an end in itself. From their earliest manifestation, these operations have been structured to convert access into influence: compromise enables collection, collection enables disclosure, and disclosure is shaped to achieve coercive or reputational effects. This framing distinguishes the activity from financially motivated ransomware or purely clandestine espionage, positioning it instead within a model of cyber-enabled information warfare aligned with state objectives.

From the Albania campaign onward, data theft has been systematically paired with controlled, curated public disclosure through actor-operated infrastructure, including websites and Telegram channels such as @Homeland Justice1. These platforms function as dissemination nodes where messaging is crafted, amplified, and aligned with political narratives. The release of stolen material, often selective, staged, or thematically framed, is designed to maximize audience impact, reinforce claims of legitimacy, and sustain attention over time. In this sense, the infrastructure is not merely a repository for leaked data, but an active component of the operational workflow, bridging the gap between technical compromise and public perception.

@Homeland Justice1 Telegram Channel

Within this model, destructive actions serve primarily to amplify visibility and urgency, creating conditions that heighten the impact of subsequent disclosures. Wiping, encryption, and service disruption draw attention to the incident and signal capability, but the strategic value is realized through narrative amplification of how the event is presented, interpreted, and circulated. The transition to the Handala persona reflects a further refinement of this approach, with increased segmentation of infrastructure to support distinct functions such as leak publication, propaganda, and targeted exposure of individuals. This specialization indicates a maturing operational framework in which success is measured less by persistence or financial return, and more by the ability to shape perception, apply pressure, and influence behavior across both institutional and individual targets.

Infrastructure and Domain Ecosystem

The infrastructure supporting the Homeland Justice and Handala campaigns reflects a deliberate, layered architecture designed to separate public-facing operations from backend control while enabling specialized functions across the ecosystem. Core domains such as Homeland Justice[.]org, handala-hack[.]to, handala-redwanted[.]to, and karmabelow80[.]org operate as visible nodes for messaging, leak publication, and intimidation, serving as the primary interface through which the actors communicate with both targets and broader audiences. These platforms are used to disseminate propaganda, frame narratives, and release or reference stolen data, transforming technical intrusions into publicly consumable events aligned with the campaign’s psychological objectives.

Behind this visible layer, additional domains such as homelandjustice[.]cx and Homeland Justice[.]ru likely function as alternate or backend infrastructure, supporting operational continuity and resilience. This separation suggests an architecture in which public-facing assets can be replaced or rotated without disrupting underlying capabilities, thereby reducing exposure while maintaining persistence. Within this system, each domain appears to serve a distinct and purpose-driven role, contributing to a modular framework that supports narrative framing, data publication, and targeted exposure. This functional segmentation reinforces the broader operational model, enabling the actors to coordinate technical compromise with controlled disclosure and messaging in a cohesive and scalable manner.

Tactics, Techniques, and Procedures (TTPs)

The campaign demonstrates a high degree of consistency in its tactics, techniques, and procedures (TTPs) across all observed phases, reflecting a mature and repeatable operational playbook. Initial access is typically achieved through a combination of exploitation of internet-facing services and targeted social engineering depending on the operational context. In earlier phases, actors leveraged vulnerabilities in externally exposed systems such as Microsoft SharePoint or Exchange to gain footholds within enterprise environments. In parallel, particularly in later surveillance-oriented activity, access has been obtained through user-centric compromise, including phishing and the delivery of trojanized applications tailored to specific targets. This dual approach allows the actors to flexibly pursue either broad network intrusion or highly targeted individual access depending on mission requirements.

Once access is established, persistence is maintained through a mix of webshell deployment and registry-based mechanisms, enabling continued control over compromised systems even in the face of remediation efforts. Webshells, often deployed on IIS or similar web servers, provide durable remote access and are frequently reused or redeployed as needed. Registry modifications and scheduled tasks are used to ensure execution at startup or at defined intervals, supporting long-term presence within the environment. Lateral movement is conducted using standard administrative protocols such as RDP, SMB, and Windows Management Instrumentation (WMI), often leveraging harvested credentials to blend activity with legitimate administrative behavior. This reliance on native protocols reduces the need for specialized tooling and helps evade detection by appearing consistent with normal network operations.

Credential access is a critical component of the campaign and is achieved through both credential harvesting and memory dumping techniques. Actors extract credentials from configuration files, email systems, and browser stores, while also leveraging native Windows components such as rundll32 and comsvcs.dll to dump LSASS memory and obtain plaintext credentials or hashes. These credentials are then used to escalate privileges and expand access across the network. Execution throughout the campaign frequently relies on PowerShell and command-line utilities, reflecting a strong preference for living-off-the-land techniques. PowerShell scripts are used for payload delivery, lateral movement, and destructive actions, and can be easily obfuscated or modified to evade detection while maintaining operational flexibility.

Data exfiltration is conducted using a combination of traditional methods and platform abuse, depending on the target environment and desired level of stealth. Files are typically staged locally, compressed, and transferred using standard protocols such as HTTP(S), FTP, or cloud storage services. In more advanced phases, particularly those involving surveillance, the actors leverage Telegram-based exfiltration, using the platform’s API to transmit data through encrypted channels that blend with legitimate traffic. This approach provides both resilience and deniability, as it reduces dependence on dedicated command-and-control infrastructure and leverages widely permitted network activity.

The impact phase integrates multiple destructive techniques, including disk wiping, file encryption, and manual system destruction, often executed in a coordinated manner across numerous endpoints. Custom wipers, PowerShell-based deletion scripts, and legitimate tools such as disk formatting utilities or encryption software are used in combination to maximize damage and complicate recovery. In some cases, actors manually execute commands to delete critical files or disable services, reinforcing the overall impact. This phase is frequently followed by immediate public disclosure, with the actors claiming responsibility and releasing messaging or data through controlled infrastructure. This rapid transition from technical action to public exposure is a defining characteristic of the campaign, linking operational execution directly to its broader objective of psychological influence and coercion.

Evolution of Personas

The progression from Homeland Justice to Karma and ultimately Handala reflects deliberate rebranding rather than a change in the underlying actor set. Core tradecraft, targeting logic, infrastructure patterns, and operational sequencing remain consistent, indicating continuity of capability and control. Homeland Justice was tightly aligned with the Albania campaign, emphasizing retaliation and coercive political messaging. As operations expanded, particularly after the Israel-Hamas conflict, the Karma persona enabled repositioning within a broader ideological frame while preserving the same methods. Handala represents a further evolution toward a durable, scalable identity suited for sustained, multi-theater activity.

These personas function as operational “skins” layered over a stable technical and organizational foundation. Each is tailored to specific audiences and narratives: Homeland Justice to Albanian political dynamics and the MEK (Mojahedin-e-Khalq), Karma to anti-Israeli messaging, and Handala to broader symbolic framing applicable across conflicts. This segmentation optimizes psychological resonance while complicating attribution by creating the appearance of distinct groups.

Multiple personas also manage exposure and risk. Branding shifts allow actors to distance current activity from prior campaigns, reset perception, and adapt to changing conditions without abandoning infrastructure or tradecraft. Parallel operations can run under different identities, reinforcing perceived decentralization. Despite this, consistent use of hack-and-leak workflows, Telegram and leak sites, and similar tooling confirms these are not separate entities but components of a unified, centrally directed ecosystem.

Strategic Assessment

These campaigns represent a state-directed, cyber-enabled influence capability that aligns closely with the operational doctrine of Iran’s Ministry of Intelligence and Security (MOIS), in which cyber operations are employed not solely for intelligence collection or disruption, but as instruments of coercion, signaling, and psychological pressure. The integration of intrusion, disruption, and narrative manipulation into a single operational system reflects a deliberate strategy in which technical access is leveraged to produce effects in the information domain. In this model, network compromise enables data acquisition and operational positioning; disruption amplifies visibility and urgency; and controlled disclosure shapes perception, imposes reputational cost, and pressures decision-making. These elements are not sequential but interdependent, forming a cohesive framework designed to influence both institutional behavior and individual actors across geopolitical contexts.


Recent infrastructure activity over the last several weeks provides direct, empirical support for this assessment and demonstrates that this capability remains active, adaptive, and operationally synchronized. Between 19 March and 23 March 2026, the actor cluster executed a compressed domain registration burst, provisioning at least eight new domains across all three personas: Handala, Karma/KarmaBelow80, and Homeland Justice. The majority of these domains are Handala-branded, including handala-hack[.]pro, handala-hack[.]shop, handala-hack[.]tw, handala-redwanted[.]cc, and handala-redwant[.]to, indicating that Handala remains the primary outward-facing operational identity. In parallel, the registration of karmabelow80[.]biz, karmabelow80[.]st, and notably Homeland Justice[.]info demonstrates that legacy personas are being actively reconstituted rather than retired.

This pattern is analytically significant. It indicates that personae evolution within this ecosystem is not linear but additive and concurrent. The actors are not transitioning from one identity to another; instead, they are maintaining multiple branded layers simultaneously, enabling them to pivot narratives, distribute operational risk, and complicate attribution. The near-simultaneous reactivation of Homeland Justice alongside active Handala expansion suggests deliberate attribution shaping and historical continuity signaling, reinforcing the perception of a persistent, ideologically driven campaign lineage.

The temporal characteristics of this activity further reinforce its operational intent. The tight clustering of registrations within a five-day window is consistent with pre-operational staging or infrastructure regeneration following disruption rather than routine domain churn. The inclusion of both “hack”-labeled domains and “redwanted”-style domains within this burst indicates parallel preparation for both intrusion-linked branding and leak-and-shame operations, which are central to this ecosystem’s influence model. This aligns with prior observed behavior in which compromised data is rapidly operationalized for public dissemination and psychological effect.

Comparable operational patterns can be observed in other state-aligned ecosystems, including Russian influence campaigns such as those associated with the Doppelgänger campaigns and hack-and-leak operations attributed to GRU-linked actors, and DPRK multi-cluster activity, where distinct operational units specialize in intrusion, financial operations, or disruption. However, the Homeland Justice / Handala ecosystem is distinguished by its consistent and tightly coupled integration of hack-and-leak operations with overt psychological messaging. Whereas Russian operations often separate intrusion from amplification, and DPRK activity frequently prioritizes financial or espionage outcomes, this campaign persistently merges technical compromise with immediate public attribution, curated disclosure, and ideological framing. The latest domain registrations reinforce this distinction by showing that infrastructure supporting both compromise and narrative dissemination is provisioned in parallel, not sequentially.

Accordingly, this activity should not be interpreted as a series of isolated incidents or campaigns, but as a persistent, evolving capability embedded within a broader state strategy. The newly observed domain registrations demonstrate that this capability can be rapidly reconstituted, expanded, and rebranded on demand, even in the face of prior takedowns or disruptions. The reuse of naming conventions, the continuity of tradecraft, the simultaneous operation of multiple personas, and the structured expansion of domain infrastructure all indicate an enduring operational framework rather than ad hoc activity. This capability can be activated, scaled, or redirected in response to changing geopolitical conditions, allowing it to remain relevant across multiple theaters and target sets. As such, it represents a sustained mechanism through which the state can project influence, apply pressure, and shape narratives in the cyber and information domains over time.

A.1 Albania Campaign (Homeland Justice)

Timeframe: ~May 2021–September 2022 (Initial Access → Impact)

Destructive / Encryption Malware

File Type Hash (MD5) Notes
GoXML.exe Ransomware / Encryptor bbe983dba3bf319621b447618548b740 Primary encryption payload
cl.exe Disk Wiper 7b71764236f244ae971742ee1bc6b098 Raw disk overwrite
mellona.exe Propagation Tool 78562ba0069d4235f28efd01e3f32a82 Lateral movement support

Webshells (Persistence)

File Type Hash (MD5) Notes
Error4.aspx Webshell 81e123351eb80e605ad73268a5653ff3 Initial persistence
ClientBin.aspx Webshell a9fa6cfdba41c57d8094545e9b56db36 IIS-based control
Pickers.aspx Webshell 8f766dea3afd410ebcd5df5994a3c571 Additional access vector

Supporting / Staging Tools

File Type Hash (MD5) Notes
disable_defender.exe Defense Evasion 60afb1e62ac61424a542b8c7b4d2cf01 AV disable
win.bat Script 1635e1acd72809479e21b0ac5497a79b Execution chain
win.bat (variant) Script 18e01dee14167c1cf8a58b6a648ee049 Variant
bb.bat Script 59a85e8ec23ef5b5c215cd5c8e5bc2ab Deployment
rwdsk.sys Driver 8f6e7653807ebb57ecc549cef991d505 Raw disk access
Goxml.jpg Decoy / Payload Carrier 0738242a521bdfe1f3ecc173f1726aa1 Masquerading artifact

A.2 Albania Follow-On Campaign (No-Justice Wiper)

Timeframe: Late 2023–Early 2024

Destructive Malware

File Type Hash (SHA-256) Notes
Ptable.exe / NACL.exe Disk Wiper 36cc72c55f572fe02836f25516d18fed1de768e7f29af7bdf469b52a3fe2531f Signed binary; prevents OS boot

Supporting Scripts

File Type Notes
p.ps1 PowerShell propagation script Lateral spread
zip.zip Archive Payload staging

A.3 Israel Campaign (Karma / Void Manticore)

Timeframe: October 2023–Mid 2024

Custom Tooling

Tool Type Notes
Karma Shell Webshell Disguised as error page
do.exe Credential validation tool Domain admin check
reGeorge Webshell Post-auth access

Destructive / Wiper Activity

Tool Type Notes
BiBi Wiper (Windows/Linux) Disk Wiper Multi-platform destruction
SDelete Secure deletion tool Living-off-the-land
format utility Disk formatting Manual destruction

A.4 Handala Campaign (Destructive Operations)

Timeframe: 2025–2026

Primary Wipers

File Type Hash (MD5) Notes
handala.exe Custom Wiper 5986ab04dd6b3d259935249741d3eff2 Core destructive payload
PowerShell Wiper Script 3cb9dea916432ffb8784ac36d1f2d3cd Recursive deletion

Supporting / Abuse of Legitimate Tools

Tool Type Hash (MD5) Notes
VeraCrypt Installer Encryption tool 3236facc7a30df4ba4e57fddfba41ec5 Destructive encryption
NetBird Installer Networking 3dfb151d082df7937b01e2bb6030fe4a Lateral connectivity
NetBird Networking e035c858c1969cffc1a4978b86e90a30 Persistence

A.5 MOIS Telegram C2 Malware Campaign

Timeframe: Fall 2023–2026

Stage 1 (Initial Access / Lures)

File Type Hash (MD5)
Telegram_Authenticator.exe Loader B9086413E7B6A0C6A11C25D14C22615F
KeePass.exe Loader 7402F2F9263782A4C469570035843510
Pictory_premium_ver9.0.4.exe Loader 1E6B601F733BC40EAA58916986BFC5B9
WhatssApp.exe Loader (filename observed, hash unknown)

Stage 2 (Persistence / Exfiltration)

File Type Hash (MD5) Notes
RuntimeSSH.exe Backdoor EBDD9595B79B39F53909D862499DBC94 Reverse SSH
RuntimeSSH.exe (variant) Backdoor E51FF37FB431767DCDEC0B5E6D2A786A Variant
MicDriver.exe Surveillance D70EBF20E3D697897BAD5BEBF72EA271 Audio capture
MicDriver.dll Support F8B5554808428291ACC65D1FD2EFE01C
MsCache.exe Data theft 3E7A2FCEF1D038D05B20148C573A6499 Cache extraction
winappx.exe Execution 481C5B5E69A08C3DF206C59FD8DDC0DC
smqdservice.exe Persistence 7E23FFADB664B0E53D821478A249D84C

Supporting Artifacts

File Type Hash (MD5)
rantom.txt Data artifact A3394EF7FFA7E88B2E7EFAEE4617FE04
rantom.txt (variant) Data artifact 2965817D063F1E8F9889F9126443D631

Command-and-Control

api.telegram.org

A.6 Cross-Campaign Observations

Malware Evolution Pattern

Phase Characteristic
2022 Custom ransomware + wiper combo
2023 Signed wipers + propagation scripts
2024 Hybrid manual + custom destruction
2025–2026 Modular wipers + LOTL tooling + mesh networking
Parallel Telegram-based surveillance malware

Key Trends

  • Increasing reliance on living-off-the-land tools
  • Use of signed binaries for evasion
  • Shift toward multi-method destruction
  • Expansion into surveillance + repression tooling
  • Persistent integration with information operations

Appendix B – MITRE ATT&CK Matrix by Campaign Phase

his appendix presents a matrix-style mapping of the Homeland Justice / Karma / Handala ecosystem across the MITRE ATT&CK Enterprise framework, broken down by campaign phase. It highlights how capabilities evolved while maintaining continuity across tactics.

B.1 Albania Campaign (2022) – Homeland Justice

Tactic Techniques
Initial Access T1190 Exploit Public-Facing Application
Execution T1059 Command Interpreter, T1059.001 PowerShell
Persistence T1505.003 Web Shell
Privilege Escalation T1078 Valid Accounts
Defense Evasion T1070 Indicator Removal
Credential Access T1003.001 LSASS Memory
Discovery T1087 Account Discovery
Lateral Movement T1021.001 RDP, T1021.002 SMB
Collection T1114.002 Remote Email Collection
Command & Control T1105 Ingress Tool Transfer
Exfiltration T1041 Exfiltration Over C2
Impact T1486 Data Encryption, T1561.001 Disk Wipe, T1485 Data Destruction

B.2 No-Justice Wiper Phase (Late 2023)

Tactic Techniques
Execution T1059.001 PowerShell
Persistence T1078 Valid Accounts
Defense Evasion T1218 System Binary Proxy Execution, T1036 Masquerading
Lateral Movement T1021 Remote Services
Command & Control T1105 Ingress Tool Transfer
Impact T1561.001 Disk Wipe, T1485 Data Destruction, T1490 Inhibit Recovery

B.3 Karma Phase (Israel Operations 2023–2024)

Tactic Techniques
Initial Access T1190 Exploit Public-Facing Application
Execution T1059, T1059.001 PowerShell
Persistence T1505.003 Web Shell
Privilege Escalation T1078 Valid Accounts
Defense Evasion T1070 File Deletion, T1562 Impair Defenses
Credential Access T1003.001 LSASS Memory
Discovery T1018 Remote System Discovery
Lateral Movement T1021.001 RDP, T1021.002 SMB
Collection T1005 Data from Local System
Command & Control T1105 Ingress Tool Transfer
Exfiltration T1041 Exfiltration Over C2
Impact T1485 Data Destruction, T1561 Disk Wipe

B.4 Handala Phase (2024–Present)

Tactic Techniques
Initial Access T1190, T1566 Phishing
Execution T1059.001 PowerShell, T1218.011 Rundll32
Persistence T1547 Boot/Logon Autostart
Privilege Escalation T1078 Valid Accounts
Defense Evasion T1036 Masquerading, T1562 Impair Defenses
Credential Access T1003.001 LSASS, T1555 Credential Stores
Discovery T1087 Account Discovery, T1069 Permission Groups
Lateral Movement T1021 RDP/SMB
Collection T1005 Local Data
Command & Control T1105 Tool Transfer
Exfiltration T1041 Exfiltration
Impact T1485 Data Destruction, T1486 Encryption, T1490 Inhibit Recovery

B.5 Telegram Surveillance Campaign (2023–2026)

Tactic Techniques
Initial Access T1566 Phishing, T1204 User Execution
Execution T1059.001 PowerShell
Persistence T1547 Registry Run Keys
Defense Evasion T1036 Masquerading
Credential Access T1555 Credential Stores
Discovery T1087 Account Discovery
Collection T1113 Screen Capture, T1123 Audio Capture, T1005 Data Collection
Command & Control T1071.001 Web Protocols (Telegram API)
Exfiltration T1041 Exfiltration via C2
Impact (Indirect – psychological/repression rather than system destruction)

B.6 Persona / Influence Infrastructure Layer

Tactic Techniques
Resource Development T1583.001 Domains, T1583.003 VPS
Establish Accounts T1585.001 Social Media Accounts
Stage Capabilities T1608 Upload/Stage Data
Command & Control T1102 Web Service (Telegram as platform)

B.7 Cross-Phase ATT&CK Heat Map (Summary)

Tactic Consistency Level
Initial Access High
Execution (PowerShell / CLI) Very High
Persistence High
Credential Access Very High
Lateral Movement Very High
Collection High
Command & Control High
Exfiltration High
Impact Very High
Influence / Persona Ops Unique / Defining


APPPENDIX C Leaks Impact

Victim Leak (Relative Timeline) Claimed Data Confirmed Adverse Event Impact Type Confidence
Stryker Corporation Late (T10) Large-scale data + "wipe" Operational disruption to manufacturing, ordering, and shipments; systems restoration required; ~80,000 devices reportedly wiped Operational + destructive HIGH
Kash Patel External (not in TW mirror but linked campaign) Emails, personal data Public exposure of personal emails and documents; reputational and counterintelligence risk Exposure / reputational HIGH
Hebrew University of Jerusalem Late (parallel campaign) 40–48 TB wiped, 23 TB exfil (claimed) Claimed destructive attack; partial reporting, no strong independent confirmation of full scale Operational (claimed) MEDIUM
Verifone Mid–late (external claim) Payment system compromise (claimed) Company denied breach; no confirmed disruption Reputational only LOW
VahidOnline Mid (T5) ~180,000 users + phone numbers Doxxing and exposure of identities; intimidation risk to dissident network Identity exposure / intimidation MEDIUM-HIGH
Sima Shine Mid (T4) ~100,000 emails (claimed) Public leak claims; reputational and intelligence exposure; no confirmed operational disruption Exposure / reputational MEDIUM
Ilan Steiner Early–Mid (T3) ~50,000 emails (claimed) Public leak claims; financial/internal exposure narrative; no confirmed secondary impact Exposure / reputational MEDIUM
Deborah Oppenheimer Early (T2) Private communications (claimed) Public exposure claims; limited external corroboration of downstream effects Exposure / reputational LOW-MEDIUM
Eran Ortal Early (T1) Strategic documents (claimed) Narrative exposure of military planning; no confirmed operational consequence Exposure / narrative LOW-MEDIUM
Israeli Security Institutions (aggregate) Mid–Late (T7) ~50,000+ emails (claimed) Systemic compromise narrative; no confirmed service disruption or institutional failure Exposure / perception MEDIUM
Mossad-linked "Treasury" Mid–Late (T6) Financial/internal documents (claimed) Corruption/financial exposure narrative; no confirmed operational impact Narrative / reputational LOW-MEDIUM
Israeli Water Infrastructure Late (T8) Target database (claimed) No confirmed breach; deterrence signaling only Strategic signaling LOW
Israeli Energy Grid Late (T9) Target database (claimed) No confirmed breach; deterrence messaging Strategic signaling LOW
Lockheed Martin engineers (Israel) External campaign Personal data (dox) Doxxing + threats; intimidation campaign; limited validation of dataset accuracy Physical intimidation MEDIUM
Learn More
Research
Handala: MOIS Linked Cyber Influence Ecosystem Threat Intelligence Assessment

Discover how Handala, Homeland Justice, and Karma function as a unified MOIS-linked cyber influence ecosystem. This threat intelligence assessment reveals how Iran uses "hack-and-leak" operations to weaponize perception over technical complexity.

Operational Structure and Attribution

The activity attributed to Homeland Justice, Karma/KarmaBelow80, and Handala is most accurately assessed as a single, coordinated cyber influence ecosystem aligned with Iran’s Ministry of Intelligence and Security (MOIS; وزارت اطلاعات جمهوری اسلامی ایران), rather than a collection of independent hacktivist groups. These personas function as interchangeable operational veneers applied to a consistent underlying capability. Their purpose is not to reflect organizational separation, but to enable segmentation of messaging, targeting, and attribution while preserving continuity of infrastructure and tradecraft.

The use of the name “Handala” itself reinforces the ideological framing of the campaign. Handala (حنظلة) is a well-known Palestinian symbol created by cartoonist Naji al-Ali, depicting a barefoot child who has turned his back on the world in protest of injustice and dispossession. Within the context of this cyber campaign, the adoption of the Handala identity serves to anchor operations within a broader “resistance” narrative, signaling alignment with anti-Israeli and anti-Western themes while providing a culturally resonant and emotionally charged brand for influence operations.

Across all observed phases, the actors exhibit clear temporal continuity, shared infrastructure patterns, and a repeatable operational workflow. The persistence of these elements, despite rebranding, indicates centralized direction and capability management. The use of multiple identities is therefore best understood as a mechanism for narrative flexibility and operational deniability, rather than evidence of distinct actor groups.

Evolution of the Operational Model

The campaign first became visible under the Homeland Justice brand during the 2022 Albania operations, which established its foundational model: long-term access, structured data exfiltration, destructive or disruptive action, and immediate public disclosure. From the outset, technical operations were tightly coupled with messaging, indicating that disruption alone was not the objective. Instead, cyber activity was used to enable narrative exploitation and psychological impact.

Homeland Justice 2023 aka Handala Albanian Operations

Subsequent phases reflect an additive evolution rather than a replacement of capabilities. The Karma phase introduced a hybrid execution model combining custom tooling, publicly available utilities, and hands-on-keyboard tradecraft. This increased operational flexibility and reduced reliance on bespoke malware. The Handala phase further expanded this model into a multi-vector framework integrating destruction, surveillance, and influence operations. The addition of Telegram-based command-and-control and surveillance tooling marked a shift toward persistent, person-centric targeting, extending the campaign’s reach beyond institutions to individuals.

Convergence of Capabilities

Recent activity demonstrates a convergence of previously distinct operational components into a unified framework. Intrusion, surveillance, disruption, and influence are no longer sequential phases, but simultaneous and interdependent functions. The Stryker incident illustrates this evolution, where large-scale data exfiltration, enterprise-level disruption through administrative control systems, and immediate narrative amplification were executed in a tightly integrated manner.

This shift reflects a broader transition away from malware-centric operations toward identity and access compromise at the control-plane level, enabling rapid, scalable impact with minimal reliance on detectable artifacts. It also demonstrates an increased ability to align technical execution with strategic messaging in near real time.

Infrastructure and Amplification Model

The ecosystem is supported by a layered infrastructure designed to separate operational functions while maintaining resilience. Public-facing domains and Telegram channels act as dissemination and amplification nodes, where messaging is curated, claims are published, and stolen data is selectively exposed. These platforms are integral to the operational workflow, bridging the gap between technical compromise and public perception.

Twitter April 2026 Amplification acct

Telegram Amplification Accounts Over Time

Infrastructure is intentionally ephemeral. Domains are frequently rotated, and personas are rebranded or reactivated as needed. However, naming conventions, messaging patterns, and distribution channels remain consistent, allowing the campaign to maintain coherence despite disruption. This results in a system where infrastructure is disposable, but identity and narrative persist.

Operational Effects and Impact

The observable impact of this ecosystem reveals a consistent divergence between claimed and verified outcomes. While the actors present their operations as large-scale destructive intrusions, confirmed system-level disruption is relatively rare. Instead, the majority of activity produces data exposure, reputational damage, and psychological pressure, often targeting both institutions and individuals.

Media hype cycle of low hanging fruit hack of FBI director’s 2009 email account

Sensationalized Reward Offer for Trump or Netanyahu 2026

Many claims remain partially verified or unverified, yet still generate significant downstream effects. Organizations are compelled to investigate and respond, media coverage amplifies the narrative, and uncertainty is sustained. In practice, the perception of compromise often produces effects equivalent to confirmed compromise, enabling the actors to achieve disproportionate impact relative to their demonstrated technical capability.

Role of Telegram and Surveillance Integration

Telegram plays a central role within this ecosystem as both a command-and-control channel and a public dissemination platform. By leveraging a widely trusted service, the actors reduce infrastructure overhead and increase operational resilience. Malware can communicate with operator-controlled bots using encrypted channels indistinguishable from legitimate traffic, while Telegram channels simultaneously serve as hubs for messaging and amplification.

The integration of surveillance capabilities further expands the campaign’s scope. Trojanized applications and user-targeted lures enable persistent monitoring of individuals, particularly dissidents and opposition networks. This allows the actors to move seamlessly from covert collection to overt exposure, reinforcing the link between technical activity and psychological pressure.

Strategic Assessment

This ecosystem represents a state-directed instrument of cyber-enabled influence, in which technical operations are tightly integrated with narrative manipulation and media amplification dynamics to achieve coercive and strategic effects. Intrusion enables access, access enables collection, and collection enables controlled disclosure. However, the decisive phase is the conversion of that disclosure into a high-visibility narrative event. Incidents such as the compromise of Kash Patel demonstrate how relatively limited technical access can be operationalized through the modern news cycle, where rapid reporting, social media propagation, and secondary analysis amplify the perceived scale and significance of the breach. In this model, the hype cycle is not incidental; it is a core component of the operation, transforming modest compromises into strategic effects.

The maintenance of multiple concurrent personas, the rapid regeneration of infrastructure, and the consistent integration of cyber and information operations indicate a mature and adaptive capability optimized for this environment. These personas allow the actors to continuously seed new events into the information ecosystem, while disposable domains and Telegram channels ensure persistence of messaging even as infrastructure is disrupted. Each operation is effectively designed as a trigger for a predictable amplification loop: initial claim, media pickup, public discourse, and institutional response. This loop imposes reputational and operational costs on targets regardless of the underlying technical depth.

As a result, the system can be activated, scaled, or redirected in response to geopolitical conditions with minimal reliance on sustained intrusion capability. Its effectiveness lies in the ability to synchronize cyber activity with the tempo of the information environment, using the hype cycle to magnify impact across multiple theaters and target sets. In practical terms, this means that perception, attention, and narrative momentum are treated as operational objectives on par with access and disruption, allowing the actors to remain effective even when technical outcomes are limited.

Conclusion

Homeland Justice, Karma, and Handala should be treated as components of a unified operational apparatus, not discrete threat actors. Their effectiveness does not derive from sustained technical superiority or advanced intrusion tradecraft, but from their ability to fuse low-to-moderate cyber capability with disciplined psychological and informational operations to create a cohesive and scalable system.

Across observed incidents, the underlying modus operandi is consistent with opportunistic, identity-layer compromise rather than sophisticated exploitation. Initial access is frequently achieved through relatively low-complexity methods such as password guessing, credential stuffing, phishing, exploitation of weak or reused credentials, and poor security hygiene in externally exposed services. Even in higher-impact cases such as Stryker Corporation, the available indicators suggest that compromise likely originated from weak identity and access controls or misconfigured management infrastructure, rather than novel vulnerabilities or advanced malware deployment. This aligns with a broader pattern in which targets are selected not for hardened defenses, but for accessible attack surfaces and exploitable operational gaps.

In this sense, these actors operate closer to low-tier intrusion crews or access brokers in their technical execution. However, what differentiates them is not how they gain access, but what they do with it. Limited footholds – often no more than a compromised account, exposed dataset, or peripheral system – are systematically transformed into hack-and-leak operations designed for maximum psychological and media impact. Small or ambiguous datasets are framed as large-scale breaches; partial access is presented as systemic compromise; and unverified claims are released in ways that ensure rapid amplification.

This is where the integration with influence operations becomes decisive. The ecosystem relies heavily on timing, narrative construction, and media exploitation to convert low-level technical events into high-visibility incidents. The breach and leak involving Kash Patel is illustrative: a compromise of a personal account technically limited in scope was rapidly elevated into a widely covered event, generating disproportionate attention relative to its technical impact. This reflects a deliberate strategy in which the news cycle functions as an extension of the operation, amplifying reach and reinforcing perceived capability.

Targets are therefore often targets of opportunity, selected for their symbolic value, media relevance, or potential to generate secondary effects. The objective is not persistent access or long-term control, but event generation creating moments that can be exploited for narrative gain. Each operation is structured to trigger a predictable response cycle: disclosure, media coverage, public reaction, and institutional response. This cycle imposes real costs on victims and defenders, regardless of the underlying technical depth of the compromise.

The result is a model in which technical simplicity coexists with strategic effectiveness. Low-level intrusions, when paired with coordinated amplification and ambiguity, produce outcomes typically associated with more advanced actors. The distinction between hacking and influence is therefore not incidental but intentional. Cyber activity provides the entry point, but the primary objective is the shaping of perception, the erosion of confidence, and the projection of capability.

This approach reflects a broader evolution in state-aligned cyber operations. Rather than investing exclusively in high-end capabilities, actors can achieve comparable strategic effects by combining accessible intrusion techniques with sophisticated information operations. In this framework, success is measured not by the depth of compromise, but by the ability to control the narrative surrounding that compromise.

Accordingly, Homeland Justice, Karma, and Handala should be understood not as elite intrusion actors, but as hybrid operators leveraging low-cost cyber access to generate high-impact psychological effects. Their significance lies in demonstrating that, in the current information environment, perception can be weaponized as effectively as technical capability. Furthermore, it demonstrates that even modest breaches can be scaled into strategic events when amplified through media and narrative control.

Learn More
Research
DPRK Malware Modularity: Diversity and Functional Specialization

Explore the DPRK’s modular malware architecture. Analyze how North Korea uses compartmentalized toolchains for espionage, crypto theft, and strategic signaling.

Executive Summary

North Korea’s cyber program has evolved into a deliberately fragmented malware ecosystem, optimized for mission specialization, operational resilience, and attribution resistance. Analysis of multiple vendor, government, academic, and secondary reporting confirms that what appears externally as a “fracture” is, in practice, a mature portfolio model: parallel malware development pipelines aligned to discrete strategic objectives.

This structure enables the DPRK to conduct simultaneous espionage, revenue generation, and disruptive operations without cross-contaminating tooling, infrastructure, or exposure. Compartmentalization and diversity is therefore assessed as a feature of program maturity, not decentralization or degradation.

Strategic Drivers

The current compartmentalization and diversity of North Korea’s malware ecosystem is not an accidental byproduct of growth or internal disorder; it is a rational response to sustained and cumulative strategic pressure. Over more than a decade, international sanctions have progressively constricted the regime’s access to hard currency, elevating cyber operations from an auxiliary intelligence function to a core mechanism of economic survival. At the same time, increasingly coordinated law-enforcement actions and intelligence disclosures have reduced the lifespan of individual campaigns, forcing DPRK operators to assume that any exposed tool, infrastructure cluster, or technique will eventually be neutralized.

This pressure has been compounded by the repeated public exposure of specific malware families and campaign narratives. Once-effective tools are now rapidly fingerprinted, attributed, and disseminated across defensive communities, collapsing their operational utility. Parallel to this, target environments particularly in finance, technology, and government have become more defensively mature, with improved telemetry, faster incident response cycles, and greater cross-sector information sharing. In aggregate, these factors have raised the cost of persistence and reduced the viability of monolithic, long-lived malware platforms.

In response, the DPRK has adapted by restructuring its cyber program around principles of resilience rather than longevity. Malware development and operations are increasingly compartmentalized, both technically and organizationally, ensuring that exposure in one mission area does not cascade across the entire program. Toolchains are treated as consumable assets: designed to be burned, replaced, and reconstituted with minimal strategic loss. This loss-tolerant posture enables multiple teams to operate in parallel, pursuing espionage, revenue generation, and disruptive objectives simultaneously without competing for the same infrastructure or codebase.

Crucially, this model also maximizes ambiguity. By separating tooling, infrastructure, and operational patterns along mission lines, the DPRK complicates attribution and slows defender decision-making. What emerges is not compartmentalization and diversity as weakness, but compartmentalization and diversity as control: a cyber apparatus engineered to absorb pressure, survive exposure, and continue functioning even as individual components are repeatedly stripped away.

Compartmentalized Malware Architecture

Espionage Oriented Malware Track

The espionage-oriented malware track represents the most traditional and strategically conservative pillar of the DPRK cyber program. Its purpose is not disruption or immediate financial return, but the quiet, sustained extraction of intelligence from institutions that shape policy, security planning, and strategic decision-making. Targets are selected for their informational value rather than their economic utility, encompassing government ministries, defense contractors, academic research centers, think tanks, and organizations operating at the margins of policy formation.

Operations within this track are characterized by restraint and patience. Activity is deliberately low-noise, with operators prioritizing extended dwell time over rapid exploitation. Initial access is leveraged to establish durable footholds that enable credential harvesting, mailbox surveillance, and systematic document collection. Once embedded, the objective is to observe, monitor, and siphon information continuously, often for months or years, with minimal operational disruption to the victim environment. Destructive actions and monetization are intentionally avoided, as they increase detection risk and prematurely terminate access.

Technically, this restraint is reflected in the tooling. Malware associated with espionage missions favors script-heavy loaders, most commonly PowerShell or VBS that blend into normal administrative activity and reduce the need for large, easily detected binaries. Backdoors are frequently memory-resident, minimizing on-disk artifacts and complicating forensic recovery. Initial access commonly relies on weaponized documents or carefully crafted lures tailored to the professional context of the target, reinforcing the emphasis on social engineering over exploit development.

Once access is established, trusted cloud services are routinely abused for command-and-control and staging. By operating through platforms already embedded in enterprise workflows, operators obscure malicious traffic within legitimate usage patterns and benefit from the implicit trust afforded to major service providers. This approach further reduces operational noise while extending persistence in environments with increasingly mature perimeter defenses.

This espionage track is most commonly associated with Kimsuky, which has long been assessed as a primary intelligence-collection component within the DPRK cyber ecosystem. Its campaigns exemplify the regime’s preference for slow, methodical access to high-value information streams, reinforcing the view that espionage remains a foundational mission even as financial and disruptive cyber operations expand alongside it.

Financial Operations Malware Track

The financially oriented malware track reflects the most adaptive and economically consequential arm of the DPRK cyber program. Its overriding purpose is revenue generation: converting access into currency in order to blunt the effects of international sanctions and directly fund regime priorities, including strategic weapons development. Unlike espionage operations, success in this track is measured not in persistence or insight, but in speed, scale, and yield.

Operations in this category are characterized by a markedly faster tempo. Campaigns are designed to move quickly from initial access to monetization, accepting shorter dwell times and higher exposure risk in exchange for financial return. Targeting is broad and opportunistic, with a pronounced focus on cryptocurrency exchanges, blockchain developers, decentralized finance platforms, and the software supply chains that underpin them. Rather than selecting victims for their strategic influence, operators select ecosystems where a single compromise can yield outsized financial gain or cascade into downstream access.

This operational urgency is mirrored in infrastructure management. Hosting, domains, and delivery mechanisms are treated as disposable, with rapid churn used to stay ahead of takedowns and blacklist propagation. Infrastructure longevity is not a priority; instead, it is optimized for brief windows of effectiveness before inevitable exposure. This burn-and-replace mindset distinguishes financial campaigns from the more conservative espionage track and underscores their role as an economic instrument rather than a long-term intelligence platform.

Technically, tooling within this track is purpose-built for theft. Wallet stealers and browser injectors are used to intercept credentials, private keys, and transaction workflows directly at the user layer. Clipboard hijacking exploits habitual behaviors to silently redirect cryptocurrency transfers. Increasingly, operators have demonstrated sophistication in compromising trust boundaries within the developer ecosystem itself, embedding malicious code into open-source packages or trojanizing software updates relied upon by exchanges and development teams. By inserting malware upstream, they convert trusted tooling into a scalable access vector.

Compromise of exchange infrastructure and developer environments further amplifies impact, allowing attackers to move laterally across platforms, users, and assets with minimal additional effort. These techniques reflect a deep understanding of how modern financial and crypto ecosystems are built and where their implicit trust assumptions can be subverted.

This revenue-focused track is most commonly associated with Lazarus Group, which has evolved from a primarily espionage-linked actor into a central pillar of the DPRK’s sanctions-evasion strategy. Its operations illustrate how malware has been weaponized not just as a tool of intrusion, but as a mechanism of state finance, tightly coupled to the regime’s broader strategic objectives.

Disruptive / Coercive Malware Track

The disruptive and coercive malware track represents the most overt and politically expressive component of the DPRK cyber program. Unlike espionage or financially motivated operations, its primary purpose is not persistence or profit, but strategic signaling. These operations are designed to demonstrate capability, impose costs, or deliver retaliation during periods of heightened geopolitical tension, serving as a cyber analogue to more traditional forms of state messaging and coercion.

Operationally, this track prioritizes impact over longevity. Dwell times are intentionally short, as operators expect rapid detection once payloads are deployed. Rather than avoiding attention, these campaigns are constructed to generate it, producing effects that are immediately visible to victims, governments, and, in some cases, the broader public. Tooling and infrastructure are treated as expendable, with a clear willingness to burn assets in exchange for a decisive, time-bound outcome.

The technical execution of these operations reflects this mindset. Payloads frequently take the form of wipers or ransomware-like tools capable of inflicting widespread disruption across enterprise environments. Once initial access is achieved, operators emphasize rapid lateral movement to maximize reach before containment measures can be enacted. Domain-wide execution is a common objective, enabling simultaneous impact across large portions of a target organization and amplifying both operational and psychological effect.

Timing is a critical element. Deployments are often aligned with external political, military, or diplomatic events, reinforcing the interpretive link between the cyber operation and broader state intent. This temporal coordination strengthens the signaling function of the attack, ensuring that the disruption is read not as isolated cybercrime, but as an intentional act within a wider strategic context.

This disruptive track is most commonly associated with Andariel, which has been linked to campaigns emphasizing sabotage, rapid execution, and overt impact. Within the fragmented DPRK malware ecosystem, this track functions as the regime’s blunt instrument: less subtle than espionage, less financially focused than theft, but uniquely suited to delivering unmistakable signals when strategic conditions demand it.

Cross-Track Technical Invariants

Despite the visible compartmentalization and diversity of tooling and operations, analysis across the full body of known malware reporting reveals a set of persistent unifying elements that cut across mission lines. These commonalities indicate that divergence at the payload and campaign level does not equate to independence at the development or strategic level. Instead, they point to shared standards, reuse patterns, and centralized oversight shaping how disparate malware tracks are built and deployed.

At the technical layer, recurring cryptographic routines and packing styles appear across otherwise distinct malware families. While implementations are often modified to frustrate signature-based detection, the underlying design choices remain recognizable, suggesting common developer playbooks or shared internal libraries. Similarly, loader architectures show strong familial resemblance: lightweight initial components designed to stage or decrypt secondary payloads, reused across campaigns with incremental variation rather than wholesale redesign.

Infrastructure analysis reinforces this picture. Even as domains and servers are rapidly rotated at the campaign level, overlap persists at lower layers of the stack, including registrars, hosting providers, and preferred geographic regions. This reuse reflects both operational convenience and institutional familiarity, revealing constraints and preferences that are difficult to fully obfuscate even in a fragmented model.

Perhaps most importantly, all tracks continue to rely heavily on social engineering as the primary initial access vector. Whether the objective is espionage, financial theft, or disruption, operators consistently exploit human trust rather than novel technical exploits. This dependence underscores a strategic assessment that human-mediated access remains more reliable, scalable, and adaptable than vulnerability-driven intrusion, particularly against increasingly hardened technical defenses.

Once access is achieved, there is a consistent preference for operating within trusted ecosystems. Cloud platforms, developer tooling, and collaboration services are repeatedly abused for command-and-control, staging, or lateral movement. By embedding malicious activity within environments already sanctioned and trusted by enterprises, operators reduce detection risk and leverage the implicit legitimacy of widely used services.

Taken together, these patterns demonstrate that compartmentalization and diversity exists primarily at the operational and payload level, not at the level of governance or development philosophy. The DPRK malware ecosystem is best understood as a collection of specialized instruments built from a common toolkit, governed by shared standards and strategic direction, even as execution diverges to meet distinct mission objectives.

Why Compartmentalization and Diversity Matters

Operationally, compartmentalization and diversity confers a high degree of resilience on the DPRK cyber program. Because malware families, infrastructure, and delivery mechanisms are compartmentalized by mission, the exposure or neutralization of one toolchain has limited impact beyond its immediate operational context. When a specific malware family is detected, attributed, and burned, the loss is contained; parallel mission tracks continue to function largely unaffected. This loss tolerance allows operators to assume compromise as a routine condition rather than an exceptional failure, encouraging aggressive use of tooling without risking systemic degradation of the broader program.

This resilience is reinforced by deliberate attribution friction. Divergent malware families, distinct infrastructure clusters, and varying tradecraft across campaigns complicate efforts to collapse activity into a single coherent actor model. Defenders and analysts are forced to disentangle overlapping indicators, slowing attribution and increasing uncertainty about scope and intent. Campaign clustering becomes more difficult as shared characteristics are diluted by intentional variation, while residual commonalities remain subtle enough to require sustained analytic effort to identify.

At the policy level, this ambiguity has concrete effects. Unclear attribution complicates decision-making around response options, escalation thresholds, and public messaging. When activity cannot be cleanly assigned to a single actor or mission set, responses tend to be slower, more cautious, and less coordinated. In this way, compartmentalization and diversity functions not only as a technical or operational safeguard, but as a strategic instrument shaping how adversary actions are interpreted while also constraining the speed and confidence with which states and organizations can respond.

Parallel Execution

Compartmentalization and diversity enables the DPRK cyber program to operate on multiple fronts simultaneously without the internal friction that would otherwise arise from shared tooling, infrastructure, or operational dependencies. By separating malware families and operational workflows along mission lines, distinct teams can pursue diplomatic, financial, and technological targets in parallel, each optimized for its own objectives and risk profile. This structure avoids the bottlenecks and trade-offs inherent in monolithic campaigns, where a single exposure can force a pause or redesign across all activity.

Against diplomatic and policy-oriented targets, espionage-focused operations can proceed patiently, maintaining long-term access and information flow without being disrupted by the higher-noise activities of financial theft or disruptive attacks. At the same time, financially motivated campaigns can move aggressively against cryptocurrency exchanges, developer communities, and related infrastructure, burning tooling and infrastructure as needed without jeopardizing sensitive intelligence footholds elsewhere. Disruptive operations, when activated, can deliver rapid and visible impact without revealing or contaminating the quieter channels of access maintained in parallel.

This separation of concerns allows the DPRK to treat its cyber operations as a portfolio of independent but strategically coordinated efforts. Each mission track operates according to its own tempo, tolerance for exposure, and technical requirements, yet all contribute to overarching state objectives. The result is a cyber apparatus capable of sustained, multi-domain engagement across diplomatic, economic, and technological domains without mutual interference or cascading operational risk.

Defender Implications

The fragmented structure of the DPRK malware ecosystem fundamentally alters the detection problem for defenders. Static malware signatures degrade rapidly as tooling is routinely modified, re-packed, or replaced altogether. Even when individual samples are successfully identified, their utility is short-lived, offering only fleeting defensive value before variants emerge. Similarly, campaign-level indicators of compromise once effective for clustering activity no longer generalize across operations, as distinct mission tracks deliberately minimize shared surface indicators.

As a result, malware-focused detection in isolation is increasingly insufficient. Focusing on payloads alone risks missing the broader operational context in which access is gained, maintained, and exploited. In a segmented model, the absence of a known malware signature does not imply the absence of DPRK activity; it may simply reflect a different mission track employing different tooling, infrastructure, or delivery mechanisms.

Effective defense therefore requires a shift in priorities. Behavioral analytics become critical for identifying anomalous patterns of access, execution, and data movement that persist regardless of specific malware families. Identity and access monitoring is particularly important, as many DPRK operations across espionage, financial, and disruptive tracks depend on credential abuse and trusted account usage rather than exploit-driven compromise. Strengthening security around supply chains and developer ecosystems is equally essential, given the regime’s demonstrated willingness to compromise upstream tooling to achieve scalable access. Cloud telemetry correlation, spanning authentication events, API usage, and cross-service activity, provides the visibility necessary to detect abuse within trusted platforms.

Organizations that frame DPRK activity too narrowly by treating it exclusively as espionage or, alternatively, as financial cybercrime risk creating analytical blind spots. The segmented nature of the threat means that focusing defenses on a single “type” of activity can leave other mission tracks undetected. Instead, a holistic approach, grounded in behavior, identity, and ecosystem trust relationships, is required to account for the full breadth of DPRK cyber operations.

Malware compartmentalization and diversity in the Broader APT Landscape

The deliberate burn-and-replace approach observed in DPRK malware campaigns is not without precedent among advanced state-aligned threat actors. However, comparative analysis shows that while similar tactics exist elsewhere, the degree of institutionalization and mission coupling seen in DPRK operations is unusually pronounced.

Several other APT actors have adopted rapid malware turnover, modular tooling, and payload rotation to evade detection and extend campaign viability under defensive pressure.

Russian intelligence–linked actors, such as APT29, have repeatedly evolved malware families over time, transitioning from early Duke variants to successive, distinct frameworks. These shifts demonstrate intentional tool refresh cycles designed to defeat signature-based detection, but they largely occur within a single strategic mission space of long-term espionage rather than across parallel, economically distinct objectives.

Similarly, APT28 has historically rotated between multiple malware families across campaigns, adapting tooling to geopolitical context and operational exposure. While this reflects a willingness to abandon burned tools, the activity remains more campaign-reactive than structurally segmented.

Chinese-linked APT41 presents a closer analogue in that it has demonstrably conducted both state-aligned espionage and financially motivated operations, often with overlapping personnel and infrastructure. APT41’s use of supply-chain compromise, rapid tool replacement, and diverse malware frameworks mirrors aspects of the DPRK model. However, public reporting indicates less rigid separation between mission toolchains, with greater reuse across objectives.

Iranian actors such as Charming Kitten likewise exhibit frequent shifts in malware payloads and delivery mechanisms, particularly in response to exposure. These changes improve survivability but do not rise to the level of a fully articulated portfolio model; tool churn here appears tactically driven, rather than strategically compartmentalized.

Finally, disruptive-focused Russian activity attributed to Sandworm demonstrates an extreme willingness to burn tooling entirely, particularly in wiper and destructive campaigns. However, this behavior is episodic and event-driven, rather than embedded in a standing, multi-mission cyber architecture.

Below is a comparative table showing how DPRK actors stand relative to other major nation-state APT actors (Russia, China, and Iran) in terms of tool churn, mission separation, and burn tolerance. This is based on multiple public sources outlining state-aligned cyber capabilities, campaign evolution, and malware practices.

Comparative Table   APT Malware Strategy & Burn Dynamics

Attribute DPRK
e.g., Lazarus / Kimsuky / Andariel
Russia
e.g., APT29 / Gamaredon / Sandworm
China
e.g., APT41 / ShadowPad Actors
Iran
e.g., APT33 / OilRig / Infy
Tool Churn / Malware Refresh High — Frequent tool replacement across multiple distinct malware families; new tooling expected as exposed. Part of intentional program design (burn/rebuild). Moderate — Malware families evolve (e.g., MiniDuke → OnionDuke → CosmicDuke), but changes are often adaptive rather than systematic churn.
Wikipedia
Moderate to High — Some modular platforms (ShadowPad) persist with evolving variants; APT41 leverages diverse malware and reuses components across operations.
SentinelOne
Low to Moderate — Generally stable toolsets with iterative updates; malware families deployed repeatedly across campaigns rather than replaced entirely.
Picus Security
Mission Separation (multiple distinct operational streams) High — Clear mission-aligned malware portfolios (espionage, financial, disruption) acting concurrently. Low–Moderate — Primarily espionage and disruption; mission roles are contextual but not structurally separated as distinct portfolios.
Wikipedia
Moderate — APT41 uniquely combines espionage + financial operations, but toolsets are often reused between missions.
TerraZone
Low — Focused primarily on espionage; mission separation is less pronounced.
Picus Security
Burn Tolerance (willingness to discard tools) Very High — Tool loss anticipated and baked into design; "burn and replace" is normative. Moderate — Tools are refreshed when detection risk becomes too high, but not as a planned operational norm. Moderate — Tools evolve to evade detection; often reused rather than fully discarded; some long-lived frameworks. Low–Moderate — Tools persist across campaigns; not typically discarded unless externally exposed.
Malware Modularity High — Early-stage loaders, persistence, and mission payloads frequently have distinct and individual modules. High — Uses modular backdoors and plugin architectures (e.g., Cozy Bear's Duke variants).
Wikipedia
High — Both modular backdoors (ShadowPad) and custom/third-party tools used.
SentinelOne
Moderate — Modular in some groups (e.g., OilRig's PowerShell modules) but less generalized than for large nation actors.
Picus Security
Cross-Campaign Reuse of Family Low — Malware families are mission distinct and often unique to a given operational track. Moderate — Reuse of older frameworks with evolution; variants often retain lineage.
Wikipedia
Moderate to High — Some core backdoors reused across different campaign objectives.
SentinelOne
High — Smaller toolsets reused across multiple campaigns with minor updates.
Picus Security
Integration with Financial Crime Explicit — Financial malware track is part of the core strategy to generate revenue. Rare — Russian state groups typically avoid financially focused malware as a strategy. Present — APT41 engages in some financially motivated activity alongside state espionage.
TerraZone
Minimal — Iranian actors mostly focus on espionage or disruption, not economic theft or revenue generation.

Analytic Distinction: Why the DPRK Model Is Different

What distinguishes the DPRK cyber program is not the existence of malware rotation itself, but how completely burn-and-replace logic is integrated into program design.

Across other APT ecosystems, rapid malware turnover is typically:

  • A response to detection,
  • Confined to a single mission domain, or
  • Implemented unevenly across campaigns.

By contrast, DPRK operations demonstrate:

  • Standing parallel malware portfolios, not ad-hoc replacements,
  • Mission-aligned toolchains (espionage, revenue, disruption),
  • Acceptance of tool loss as routine, not exceptional,
  • And centralized strategic coordination despite decentralized execution.

This places DPRK activity closer to an industrialized cyber production model, where malware is treated as a consumable input rather than a prized asset.

In contrast, espionage tooling is expected to retain its emphasis on low-noise persistence. Malware supporting intelligence collection will continue to prioritize stealth, credential abuse, and cloud-based living-off-the-land techniques that enable extended dwell times even in increasingly monitored environments.

Taken together, these trends indicate that compartmentalization and diversity is not a transitional phase but a durable feature of the DPRK cyber program. As defensive pressure increases, diversification by mission will deepen, further entrenching a model built to absorb exposure, frustrate attribution, and sustain operations across multiple strategic domains.

Summary Findings

The DPRK malware ecosystem is not simply more prolific or more chaotic than that of its peers; it is more deliberately structured at a fundamental, programmatic level. Where many advanced persistent threat actors treat malware as a semi-durable asset to be preserved and refined over time, the DPRK treats malware as an inherently expendable input. Tool exposure is not regarded as a failure state; it is an assumed outcome. As a result, operational planning begins from the premise that any given toolchain will eventually be detected, attributed, and neutralized.

This assumption fundamentally reshapes how the DPRK designs and deploys cyber capabilities. Malware is engineered for utility within a limited lifespan rather than for long-term survivability. Development pipelines emphasize speed, modularity, and replaceability over elegance or longevity. When a tool is burned, it is not mourned or patched indefinitely; it is discarded and superseded, often by a parallel or already-prepared alternative. In this sense, compartmentalization and diversity is not a defensive reaction to disruption, but the default state of the ecosystem.

By contrast, many other APT actors burn tools reluctantly and reactively. Russian, Chinese, and Iranian groups typically rotate malware families after exposure, but such decisions are often tied to specific campaigns or incidents. The underlying assumption remains that tools should persist as long as possible, evolving incrementally to preserve prior investment. The DPRK departs from this logic entirely. Its cyber operations reflect an acceptance that persistence at the tool level is illusory, and that strategic continuity must instead be achieved through organizational design and operational redundancy.

Seen in comparative context, DPRK cyber operations are therefore best understood not as an anomaly, but as a mature instantiation of a broader trend among advanced threat actors pushed to its logical extreme by unique economic and political constraints. Persistent sanctions, direct linkage between cyber activity and state revenue, and sustained international scrutiny have compressed the DPRK’s tolerance for operational pause or degradation. Under these conditions, a cyber program built around long-lived platforms would be brittle. A program built around compartmentalization and diversity, parallel execution, and consumable tooling is resilient.

Malware diversity, rapid churn, and concurrent mission execution are not symptoms of disorder or indiscipline. They are the visible mechanics of a system engineered to function under constant pressure, where exposure is continuous and inevitability assumed. In this model, coherence does not reside in individual tools, but in strategy: centralized intent, mission-aligned portfolios, and an operational architecture designed to endure even as its individual components are repeatedly destroyed.

APPENDIX A: Representative DPRK Malware IOCs  

Government-Published Malware Variants & Names

These malware families have been documented in U.S. government malware reports and advisories associated with North Korean state actors (often referred to collectively as HIDDEN COBRA by U.S. agencies): (CISA)

  • BLINDINGCAN – Remote access tool used to maintain persistence and network exploitation. (CISA)
  • COPPERHEDGE – Manuscrypt family variant attributed to North Korean APT targeting exchanges/crypto ecosystems. (CISA)
  • TAINTEDSCRIBE – Full-featured beaconing implant used by DPRK actors. (CISA)
  • PEBBLEDASH – North Korean beaconing implant family. (CISA)
  • BISTROMATH – Remote access implant with multiple versions observed. (CISA)
  • SLICKSHOES – Dropper with beaconing capabilities. (CISA)
  • CROWDEDFLOUNDER – Beaconing payload with packing protections. (CISA)
  • HOTCROSSIANT – Full-featured beaconing implant. (CISA)
  • ARTFULPIE – Downloader implant that decodes and executes secondary payloads. (CISA)
  • BUFFERLINE – Full-featured beaconing implant. (CISA)
  • ELECTRICFISH – Proxy malware for tunnelled traffic. (CISA)
  • BADCALL – Proxy server malware with Fake TLS methods. (CISA)
  • Joanap – RAT enabling botnet management and secondary payload execution. (Wikipedia)

Note: CISA malware analysis reports (MARs) frequently include sample hashes, file Thatnames, network indicators, and signatures for these variants. (CISA)

Appendix B:   Malware Linked Activities and Attribution Context

Cryptocurrency-Facilitating Malware

  • AppleJeus – Malware family used to facilitate cryptocurrency theft, often distributed under the guise of fake trading platforms or wallets. (CISA)

Operational Artifacts & TTP Context

While specific IOCs vary by incident and campaign, the following patterns are relevant to detection and triage:

  • Botnet infrastructure IPs associated with DDoS and proxy relays used by DPRK actors. (CISA)
  • Credential harvesting and session token theft in spearphishing campaigns (e.g., mobile-delivered QR code phishing vectors). (Internet Crime Complaint Center)
  • Proxy and beaconing communication over Fake TLS or tunneled channels seen in BADCALL/ELECTRICFISH series. (CISA)

Appendix C: Known Malware Families by Associated Actor

Malware Family Common Attribution / Actor Lineage Source
BLINDINGCAN DPRK state-linked APT variants CISA
Manuscrypt / COPPERHEDGE Currency theft and exchange targeting CISA
AppleJeus Cryptocurrency facilitation malware CISA
Joanap / Brambul ecosystem RAT + worm infrastructure tied to Hidden Cobra / HIDDEN COBRA Wikipedia
Multiple beaconing implants (TAINTEDSCRIBE, CROWDEDFLOUNDER, etc.) DPRK APT variants CISA

Appendix D:  Additional IOC Sources and Hunting References

For operational deployment, consult the following sources with downloadable IOC datasets:

  • CISA North Korea State-Sponsored Threat Advisories   Includes malware reports, sample hashes, and network indicators. (CISA)
  • Unit42 Threat Assessment  North Korean Groups Malware Arsenal   Contains telemetry on recent malware families across OS platforms. (Unit 42)
  • Acronis TRU Alliance DPRK Malware Infrastructure Mapping   Includes IOCs and hunting guidance for Lazarus and Kimsuky clusters. (Acronis)

Appendix E: Exemplar File Hashes by “Hydra Head” (SHA256)

F.1 Head 1   LABYRINTH CHOLLIMA (Espionage / Industrial, logistics, defense)

Primary exemplars (CrowdStrike community-tracking hashes): (CrowdStrike)

  • Dozer   7dee2bd4e317d12c9a2923d0531526822cfd37eabfd7aecc74258bb4f2d3a643 (CrowdStrike)
  • Brambul   d2359630e84f59984ac7ddebdece9313f0c05f4a1e7db90abadfd86047c12dd6 (CrowdStrike)
  • Joanap   4fe3c853ab237005f7d62324535dd641e1e095d1615a416a9b39e042f136cf6b (CrowdStrike)
  • KorDLL Bot   73edc54abb3d6b8df6bd1e4a77c373314cbe99a660c8c6eea770673063f55503 (CrowdStrike)
  • Koredos   a795964bc2be442f142f5aea9886ddfd297ec898815541be37f18ffeae02d32f (CrowdStrike)
  • Hawup RAT   453d8bd3e2069bc50703eb4c5d278aad02304d4dc5d804ad2ec00b2343feb7a4 (CrowdStrike)
  • Hoplight   05feed9762bc46b47a7dc5c469add9f163c16df4ddaafe81983a628da5714461 (CrowdStrike)
  • Manuscrypt   dced1acbbe11db2b9e7ae44a617f3c12d6613a8188f6a1ece0451e4cd4205156 (CrowdStrike)
  • HTTPHoplight   ceccb2339088fa2d6337082704bbf67f84eeb0d0b60ce5ab0ab7e1824002fa4c (CrowdStrike)
  • OpenSSL Downloader   f749c7e84809ffc3939eaed06ad90e15b0e11375f98d7348c0aa1bf35d3f0b8e (CrowdStrike)
  • UnderGroundRAT   f9586fdf4e0a65b17ee32bc3c3f493a055409abde373720d594d27fd24adffa0 (CrowdStrike)
  • NedDnLoader   512877c98fd83cd51bb287da4462b44f9d276d7ce51890f4ded1b915a6d2d5e1 (CrowdStrike)
  • Stackeyflate   d2e743216d17e97c8d1913d376d46095b740015f26a3c62a05e286573721d26c (CrowdStrike)
  • HiberRAT   58f2972c6a8fc743543f7b8c4df085c5cf2c6e674e5601e85eec60cd269cfb3c (CrowdStrike)
  • WinWebDown   fc885b323172106ab6f2f0cc77b609987384a38e3af41ad888d5389610d29daf (CrowdStrike)
  • FudModule   cbd1634cf7c638f2faf5e3ec79137db6704ec9de8df798fc46aeeed38de3da9b (noted as shared with GOLDEN) (CrowdStrike)

Supplemental “legacy DPRK MAR-derived” hashes (bridging set; keep as non-exclusive DPRK nexus):
Use these as heritage/overlap indicators for “DPRK malware ecosystem” rather than hard-binding them to LABYRINTH specifically.

  • BLINDINGCAN (multiple SHA256)
  • BISTROMATH (multiple SHA256)
  • SLICKSHOES, CROWDEDFLOUNDER, BUFFETLINE, BADCALL (SHA256)
    (These remain useful as “DPRK malware portfolio” IOCs, but they are not the cleanest proof of the three-unit split without additional clustering work.)

PRESSURE CHOLLIMA (High-payout crypto theft operations)

Primary exemplars (CrowdStrike community-tracking hashes): (CrowdStrike)

  • Scuzzyfuss   b9f6a9d4f837f5b8a5dc9987a91ba44bc7ae7f39aa692b5b21dba460f935a0ae (CrowdStrike)
  • MataNet   357c9daf6c4343286a9a85a27bc25defdc056877ce1be2943d2e8ede3bce022c (CrowdStrike)
  • SwDownloader   a61ecbe8a5372c85dcf5d077487f09d01e144128243793d2b97012440dcf106e (CrowdStrike)
  • SparkDownloader   9ba02f8a985ec1a99ab7b78fa678f26c0273d91ae7cbe45b814e6775ec477598 (CrowdStrike)
  • TwoPence Electric   081804b491c70bfa63ecdbe9fd4618d3570706ad8b71dba13e234069648e5e48 (CrowdStrike)
  • MagikCookie   1579347265f948f9646931335d57e7960fe65dd429394be84b4ae15bca73dfde (CrowdStrike)
  • StatusSymbol   666c50b8b772101b0e2e35ff1de52a278c2727027b54858e457571d296fec50b (CrowdStrike)
  • GhostShip   56e51244e258c39293463c8cf02f5dddb085be90728fab147a60741cf014aa4d (CrowdStrike)
  • AlertConf   e0aa5ef3af26681a8c8b46d95656580779d0ff3c2fe531b95a59ee918686e443 (CrowdStrike)

GOLDEN CHOLLIMA (Baseline revenue / consistent tempo, fintech & crypto)

Primary exemplars (CrowdStrike community-tracking hashes): (CrowdStrike)

  • Jeus   fe948451df90df80c8028b969bf89ecbf501401e7879805667c134080976ce2e (CrowdStrike)
  • HTTPHelper   ff32bc1c756d560d8a9815db458f438d63b1dcb7e9930ef5b8639a55fa7762c9 (CrowdStrike)
  • SnakeBaker   b6995c31a7ee88392fc25fd6d1a3a7975b3cb4ec3a9a318c3fcfaaf89eb65ce1 (CrowdStrike)
  • NodalBaker   0518a163b90e7246a349440164d02d10f31d514a7e5cce842b6cf5b3a0cc1bfa (CrowdStrike)
  • PipeDown   2ef212f433b722b734d80b41a2364a41ca0453dbfe3e6ec8b951eca795075a02 (CrowdStrike)
  • DevobRAT   fde50c3a373ebc2661e08c99c1cb50dc34efc022a3880c317ab5b84108ef83aa (CrowdStrike)
  • Anycon   2110a6e89d98a626f846ec8deccbac057300d194933ae0cbf1ef4831a4cc829e (CrowdStrike)
  • CitriLoader   d0cf9c1f87eac9b8879684a041dd6a2e1a0c15e185d4814a51adda19f9399a9b (CrowdStrike)
  • FudModule (shared access noted)   cbd1634cf7c638f2faf5e3ec79137db6704ec9de8df798fc46aeeed38de3da9b (CrowdStrike)

APPENDIX F: ANY RUN and VIRUS TOTAL LINKS

LABYRINTH CHOLLIMA (Espionage Head)

Dozer

VT: https://www.virustotal.com/gui/file/7dee2bd4e317d12c9a2923d0531526822cfd37eabfd7aecc74258bb4f2d3a643
ANY.RUN: https://any.run/search/?query=7dee2bd4e317d12c9a2923d0531526822cfd37eabfd7aecc74258bb4f2d3a643

Brambul

VT: https://www.virustotal.com/gui/file/d2359630e84f59984ac7ddebdece9313f0c05f4a1e7db90abadfd86047c12dd6
ANY.RUN: https://any.run/search/?query=d2359630e84f59984ac7ddebdece9313f0c05f4a1e7db90abadfd86047c12dd6

Joanap

VT: https://www.virustotal.com/gui/file/4fe3c853ab237005f7d62324535dd641e1e095d1615a416a9b39e042f136cf6b
ANY.RUN: https://any.run/search/?query=4fe3c853ab237005f7d62324535dd641e1e095d1615a416a9b39e042f136cf6b

KorDLL Bot

VT: https://www.virustotal.com/gui/file/73edc54abb3d6b8df6bd1e4a77c373314cbe99a660c8c6eea770673063f55503
ANY.RUN: https://any.run/search/?query=73edc54abb3d6b8df6bd1e4a77c373314cbe99a660c8c6eea770673063f55503

Koredos

VT: https://www.virustotal.com/gui/file/a795964bc2be442f142f5aea9886ddfd297ec898815541be37f18ffeae02d32f
ANY.RUN: https://any.run/search/?query=a795964bc2be442f142f5aea9886ddfd297ec898815541be37f18ffeae02d32f

Hawup RAT

VT: https://www.virustotal.com/gui/file/453d8bd3e2069bc50703eb4c5d278aad02304d4dc5d804ad2ec00b2343feb7a4
ANY.RUN: https://any.run/search/?query=453d8bd3e2069bc50703eb4c5d278aad02304d4dc5d804ad2ec00b2343feb7a4

Hoplight

VT: https://www.virustotal.com/gui/file/05feed9762bc46b47a7dc5c469add9f163c16df4ddaafe81983a628da5714461
ANY.RUN: https://any.run/search/?query=05feed9762bc46b47a7dc5c469add9f163c16df4ddaafe81983a628da5714461

Manuscrypt

VT: https://www.virustotal.com/gui/file/dced1acbbe11db2b9e7ae44a617f3c12d6613a8188f6a1ece0451e4cd4205156
ANY.RUN: https://any.run/search/?query=dced1acbbe11db2b9e7ae44a617f3c12d6613a8188f6a1ece0451e4cd4205156

HTTPHoplight

VT: https://www.virustotal.com/gui/file/ceccb2339088fa2d6337082704bbf67f84eeb0d0b60ce5ab0ab7e1824002fa4c
ANY.RUN: https://any.run/search/?query=ceccb2339088fa2d6337082704bbf67f84eeb0d0b60ce5ab0ab7e1824002fa4c

OpenSSL Downloader

VT: https://www.virustotal.com/gui/file/f749c7e84809ffc3939eaed06ad90e15b0e11375f98d7348c0aa1bf35d3f0b8e
ANY.RUN: https://any.run/search/?query=f749c7e84809ffc3939eaed06ad90e15b0e11375f98d7348c0aa1bf35d3f0b8e

UnderGroundRAT

VT: https://www.virustotal.com/gui/file/f9586fdf4e0a65b17ee32bc3c3f493a055409abde373720d594d27fd24adffa0
ANY.RUN: https://any.run/search/?query=f9586fdf4e0a65b17ee32bc3c3f493a055409abde373720d594d27fd24adffa0

NedDnLoader

VT: https://www.virustotal.com/gui/file/512877c98fd83cd51bb287da4462b44f9d276d7ce51890f4ded1b915a6d2d5e1
ANY.RUN: https://any.run/search/?query=512877c98fd83cd51bb287da4462b44f9d276d7ce51890f4ded1b915a6d2d5e1

Stackeyflate

VT: https://www.virustotal.com/gui/file/d2e743216d17e97c8d1913d376d46095b740015f26a3c62a05e286573721d26c
ANY.RUN: https://any.run/search/?query=d2e743216d17e97c8d1913d376d46095b740015f26a3c62a05e286573721d26c

HiberRAT

VT: https://www.virustotal.com/gui/file/58f2972c6a8fc743543f7b8c4df085c5cf2c6e674e5601e85eec60cd269cfb3c
ANY.RUN: https://any.run/search/?query=58f2972c6a8fc743543f7b8c4df085c5cf2c6e674e5601e85eec60cd269cfb3c

WinWebDown

VT: https://www.virustotal.com/gui/file/fc885b323172106ab6f2f0cc77b609987384a38e3af41ad888d5389610d29daf
ANY.RUN: https://any.run/search/?query=fc885b323172106ab6f2f0cc77b609987384a38e3af41ad888d5389610d29daf

FudModule

VT: https://www.virustotal.com/gui/file/cbd1634cf7c638f2faf5e3ec79137db6704ec9de8df798fc46aeeed38de3da9b
ANY.RUN: https://any.run/search/?query=cbd1634cf7c638f2faf5e3ec79137db6704ec9de8df798fc46aeeed38de3da9b

PRESSURE CHOLLIMA (High-Payout Crypto)

Scuzzyfuss

VT: https://www.virustotal.com/gui/file/b9f6a9d4f837f5b8a5dc9987a91ba44bc7ae7f39aa692b5b21dba460f935a0ae
ANY.RUN: https://any.run/search/?query=b9f6a9d4f837f5b8a5dc9987a91ba44bc7ae7f39aa692b5b21dba460f935a0ae

MataNet

VT: https://www.virustotal.com/gui/file/357c9daf6c4343286a9a85a27bc25defdc056877ce1be2943d2e8ede3bce022c
ANY.RUN: https://any.run/search/?query=357c9daf6c4343286a9a85a27bc25defdc056877ce1be2943d2e8ede3bce022c

SwDownloader

VT: https://www.virustotal.com/gui/file/a61ecbe8a5372c85dcf5d077487f09d01e144128243793d2b97012440dcf106e
ANY.RUN: https://any.run/search/?query=a61ecbe8a5372c85dcf5d077487f09d01e144128243793d2b97012440dcf106e

SparkDownloader

VT: https://www.virustotal.com/gui/file/9ba02f8a985ec1a99ab7b78fa678f26c0273d91ae7cbe45b814e6775ec477598
ANY.RUN: https://any.run/search/?query=9ba02f8a985ec1a99ab7b78fa678f26c0273d91ae7cbe45b814e6775ec477598

TwoPence Electric

VT: https://www.virustotal.com/gui/file/081804b491c70bfa63ecdbe9fd4618d3570706ad8b71dba13e234069648e5e48
ANY.RUN: https://any.run/search/?query=081804b491c70bfa63ecdbe9fd4618d3570706ad8b71dba13e234069648e5e48

MagikCookie

VT: https://www.virustotal.com/gui/file/1579347265f948f9646931335d57e7960fe65dd429394be84b4ae15bca73dfde
ANY.RUN: https://any.run/search/?query=1579347265f948f9646931335d57e7960fe65dd429394be84b4ae15bca73dfde

StatusSymbol

VT: https://www.virustotal.com/gui/file/666c50b8b772101b0e2e35ff1de52a278c2727027b54858e457571d296fec50b
ANY.RUN: https://any.run/search/?query=666c50b8b772101b0e2e35ff1de52a278c2727027b54858e457571d296fec50b

GhostShip

VT: https://www.virustotal.com/gui/file/56e51244e258c39293463c8cf02f5dddb085be90728fab147a60741cf014aa4d
ANY.RUN: https://any.run/search/?query=56e51244e258c39293463c8cf02f5dddb085be90728fab147a60741cf014aa4d

AlertConf

VT: https://www.virustotal.com/gui/file/e0aa5ef3af26681a8c8b46d95656580779d0ff3c2fe531b95a59ee918686e443
ANY.RUN: https://any.run/search/?query=e0aa5ef3af26681a8c8b46d95656580779d0ff3c2fe531b95a59ee918686e443

GOLDEN CHOLLIMA (Baseline Revenue Track)

Jeus

VT: https://www.virustotal.com/gui/file/fe948451df90df80c8028b969bf89ecbf501401e7879805667c134080976ce2e
ANY.RUN: https://any.run/search/?query=fe948451df90df80c8028b969bf89ecbf501401e7879805667c134080976ce2e

HTTPHelper

VT: https://www.virustotal.com/gui/file/ff32bc1c756d560d8a9815db458f438d63b1dcb7e9930ef5b8639a55fa7762c9
ANY.RUN: https://any.run/search/?query=ff32bc1c756d560d8a9815db458f438d63b1dcb7e9930ef5b8639a55fa7762c9

SnakeBaker

VT: https://www.virustotal.com/gui/file/b6995c31a7ee88392fc25fd6d1a3a7975b3cb4ec3a9a318c3fcfaaf89eb65ce1
ANY.RUN: https://any.run/search/?query=b6995c31a7ee88392fc25fd6d1a3a7975b3cb4ec3a9a318c3fcfaaf89eb65ce1

NodalBaker

VT: https://www.virustotal.com/gui/file/0518a163b90e7246a349440164d02d10f31d514a7e5cce842b6cf5b3a0cc1bfa
ANY.RUN: https://any.run/search/?query=0518a163b90e7246a349440164d02d10f31d514a7e5cce842b6cf5b3a0cc1bfa

PipeDown

VT: https://www.virustotal.com/gui/file/2ef212f433b722b734d80b41a2364a41ca0453dbfe3e6ec8b951eca795075a02
ANY.RUN: https://any.run/search/?query=2ef212f433b722b734d80b41a2364a41ca0453dbfe3e6ec8b951eca795075a02

DevobRAT

VT: https://www.virustotal.com/gui/file/fde50c3a373ebc2661e08c99c1cb50dc34efc022a3880c317ab5b84108ef83aa
ANY.RUN: https://any.run/search/?query=fde50c3a373ebc2661e08c99c1cb50dc34efc022a3880c317ab5b84108ef83aa

Anycon

VT: https://www.virustotal.com/gui/file/2110a6e89d98a626f846ec8deccbac057300d194933ae0cbf1ef4831a4cc829e
ANY.RUN: https://any.run/search/?query=2110a6e89d98a626f846ec8deccbac057300d194933ae0cbf1ef4831a4cc829e

CitriLoader

VT: https://www.virustotal.com/gui/file/d0cf9c1f87eac9b8879684a041dd6a2e1a0c15e185d4814a51adda19f9399a9b
ANY.RUN: https://any.run/search/?query=d0cf9c1f87eac9b8879684a041dd6a2e1a0c15e185d4814a51adda19f9399a9b

FudModule (Shared)

VT: https://www.virustotal.com/gui/file/cbd1634cf7c638f2faf5e3ec79137db6704ec9de8df798fc46aeeed38de3da9b
ANY.RUN: https://any.run/search/?query=cbd1634cf7c638f2faf5e3ec79137db6704ec9de8df798fc46aeeed38de3da9b

Learn More
Research
No items found.
Exposure of TLS Private Key for Myclaw 360 in Qihoo 360 “Security Claw” AI Platform

DTI analysis of a leaked TLS private key from Qihoo 360's AI security platform, covering cryptographic validation, threat scenarios, and incident response.

Executive Summary

DTI analyzed the confirmed exposure of a Transport Layer Security (TLS) private key associated with the wildcard certificate *.myclaw[.]360[.]cn, which appears tied to the Security Claw (安全龙虾) artificial-intelligence assistant platform developed by Qihoo 360. Earlier public discussion of the issue relied primarily on screenshots and reposted commentary claiming that the certificate and private key were embedded in the platform’s installer package. The material provided for this investigation includes the full X.509 certificate and corresponding private key. Cryptographic validation confirms that the supplied private key matches the public key contained in the certificate, establishing that the exposed credential is authentic and operational rather than a placeholder or decoy.

The certificate is issued by WoTrus CA Limited under the issuing chain WoTrus RSA DV SSL CA 2. It is a wildcard certificate covering both *.myclaw[.]360[.]cn and myclaw[.]360[.]cn and was originally issued with a validity period spanning 12 March 2026 through 12 April 2027. Because wildcard certificates authenticate any host within the domain namespace, possession of the corresponding private key would allow an attacker to impersonate services across the entire Security Claw infrastructure if the certificate remained trusted and unrevoked.

Subsequent certificate-transparency analysis conducted during this investigation indicates that the certificate has since been rotated and replaced as part of an apparent incident-response action. CT log entries show that on 16 March 2026, a new wildcard certificate for *.myclaw[.]360[.]cn was issued with a new RSA key pair and shortened validity period, replacing the originally exposed certificate. The rapid issuance of the replacement certificate and the change in key material strongly suggest that Qihoo 360 detected the credential exposure and executed emergency key rotation to invalidate the compromised trust material.

Infrastructure analysis further confirms that the parent domain ecosystem (360[.]cn) is registered to Beijing Qihoo Technology Co., Ltd. (北京奇虎科技有限公司) and uses internally controlled DNS and mail infrastructure. This strongly supports attribution of the myclaw[.]360[.]cn namespace to Qihoo 360’s operational domain environment. The exposure therefore represents a confirmed cryptographic trust-material leak, with potential consequences including server impersonation, TLS interception, credential theft, and malicious update delivery within the Security Claw ecosystem. Although the certificate appears to have been rotated following discovery of the issue, the operational impact ultimately depends on whether the compromised key was actively deployed in production services and whether any adversary obtained the key prior to remediation.

Background: Qihoo 360 and the Security Claw Platform

Qihoo 360 is widely recognized as one of China’s largest cybersecurity and internet-technology companies, operating across both consumer and enterprise security markets. Since its founding in the early 2000s, the company has developed a broad portfolio of security and software products that include antivirus platforms, endpoint protection suites, web browsers, vulnerability-scanning tools, and large-scale threat-intelligence services. Through these products, Qihoo 360 has established an extensive user base spanning hundreds of millions of individual users as well as corporate and government customers. Much of the company’s security ecosystem is built around large telemetry pipelines that collect threat data from deployed endpoints and feed it into centralized analytics systems used to detect malware, exploit campaigns, and network intrusions.

In recent years the company has increasingly invested in artificial-intelligence technologies as part of its broader cybersecurity strategy. Like many large security vendors, Qihoo 360 has begun integrating machine-learning models and generative AI capabilities into its defensive tools, both to automate analysis tasks and to provide interactive interfaces for users and analysts. This effort has produced a range of AI-enabled assistants and intelligent agents designed to augment traditional security workflows. These systems typically allow users to query threat data, analyze malware samples, or receive automated recommendations through natural-language interfaces powered by backend AI models.

However, they have started pulling back on this, as they have begun learning about the pitfalls.

The Security Claw (安全龙虾) platform appears to be one of the products emerging from this initiative. Based on publicly available information and artifacts analyzed during this investigation, Security Claw functions as a locally installed client application that interacts with remote services operated by Qihoo 360. Rather than performing all processing locally, the client appears to act as a front-end interface that communicates with cloud-hosted AI infrastructure. These backend services operate within the myclaw.360.cn domain namespace, which appears to serve as the central network environment for the platform’s API endpoints and inference services.

Reports associated with the platform indicate that the client software connects to at least one backend endpoint located at https://myclaw[.]360[.]cn:19798, a service running on a non-standard port rather than the default HTTPS port 443. The use of such ports is common in internal service architectures where applications communicate directly with API gateways or service nodes without passing through standard web-server front ends. The presence of this endpoint suggests that the client communicates with a specialized service interface rather than a conventional public website.

Architecturally, this design reflects a hybrid deployment model commonly used by modern AI assistant platforms. In this model, a lightweight local application acts as a wrapper that manages user interactions, authentication, and system integration while delegating computationally intensive tasks such as natural-language processing, model inference, and large-scale data retrieval to cloud infrastructure. The client collects user prompts and contextual information from the local system and forwards these requests to backend services where AI models perform the actual analysis or generate responses.

Systems built on this architecture typically consist of multiple interconnected backend components. These may include authentication services responsible for validating client identities, API gateways that route requests to the appropriate services, telemetry collectors that gather usage and performance data from deployed clients, and inference endpoints hosting the machine-learning models used to generate responses. Additional components often include update services responsible for delivering model updates or configuration files to the client software. All of these elements operate together to create the user-facing experience of an AI assistant while relying on centralized cloud infrastructure to perform the majority of processing tasks.

Technical Findings

Certificate Structure

Analysis of the certificate associated with the Security Claw infrastructure indicates that it is a standard X.509 server authentication certificate issued for the wildcard domain namespace *.myclaw[.]360[.]cn. The certificate’s Common Name (CN) is configured as *.myclaw[.]360[.]cn, enabling it to authenticate any host operating under that subdomain space. In addition to the wildcard identifier, the certificate’s Subject Alternative Name (SAN) extension explicitly includes both *.myclaw.360[.]cn and the root host myclaw[.]360[.]cn. This configuration allows the certificate to be used by both the base domain and any subordinate services, a design pattern typically employed in microservice architectures where multiple backend services operate under a single domain namespace.

The certificate was issued by WoTrus RSA DV SSL CA 2, a certificate authority chain operated by WoTrus CA Limited, a Chinese certificate authority widely used within domestic cloud infrastructure and enterprise platforms. The certificate’s validity window begins on 12 March 2026 and extends through 12 April 2027, reflecting a relatively long operational lifespan typical of domain-validated certificates used in application backends. Cryptographically, the certificate employs an RSA 2048-bit public key, a widely adopted key size for TLS server authentication that provides an established balance between security strength and compatibility across client platforms.

The certificate is uniquely identified by the serial number 98dfeafdc4c32371f0ab490c8a3c7819, which serves as the certificate authority’s internal identifier for the issued credential. Its cryptographic fingerprint, calculated using the SHA-256 hashing algorithm, is 5a0a0df9695395223a1d342d2ccf82f449b342a281ed056dfa7880965bcbe3ca. This fingerprint provides a reliable mechanism for identifying the certificate across transparency logs, passive TLS telemetry, and network monitoring systems.

Functionally, the certificate is a domain-validated TLS certificate intended solely for server authentication. It does not contain certificate authority privileges and cannot be used to sign subordinate certificates or create additional trust anchors. Instead, its purpose is to enable servers operating under the myclaw[.]360[.]cn namespace to prove domain ownership during TLS handshakes, allowing clients to establish encrypted connections that they believe originate from legitimate Security Claw infrastructure.

The provided certificate is an X.509 server certificate with the following key attributes:

Common Name: *.myclaw.360.cn
Subject Alternative Names: *.myclaw.360.cn myclaw.360.cn
Issuer: WoTrus RSA DV SSL CA 2
Organization: WoTrus CA Limited
Validity Period: Not Before: 2026-03-12 Not After : 2027-04-12
Public Key Algorithm: RSA 2048-bit
Certificate Serial Number: 98dfeafdc4c32371f0ab490c8a3c7819
The certificate’s SHA-256 fingerprint is: 5a0a0df9695395223a1d342d2ccf82f449b342a281ed056dfa7880965bcbe3ca

Private Key Validation

Cryptographic analysis confirms that the private key provided in the dataset corresponds directly to the public key embedded within the associated TLS certificate. This relationship was verified by extracting and comparing the RSA modulus from both the certificate and the private key. The modulus values match exactly, demonstrating that the two artifacts form a valid cryptographic key pair.

This verification establishes that the exposed private key is the genuine operational key associated with the certificate rather than unrelated or fabricated data. In other words, the key is capable of performing the cryptographic operations required to authenticate servers presenting the certificate during Transport Layer Security (TLS) negotiations.

Within TLS architecture, the private key represents the confidential element of the certificate pair and functions as the mechanism by which a server proves its identity to connecting clients. During the TLS handshake process, the server must demonstrate possession of this secret key in order to validate that it legitimately controls the certificate presented to the client. If the server successfully performs this proof, the client accepts the certificate as authentic and proceeds to establish an encrypted communication channel.

Consequently, possession of the private key enables any system holding it to complete TLS handshakes that appear fully legitimate to clients relying on standard certificate validation. This capability effectively allows the holder of the key to impersonate servers operating under the certificate’s domain namespace and establish encrypted connections that clients would normally interpret as trusted communications with the genuine service.

Infrastructure Attribution

To assess ownership and operational control of the namespace, passive DNS intelligence and domain-registration data indicate that the namespace is part of the broader Qihoo 360 domain ecosystem. This determination provides strong evidence that the infrastructure supporting the Security Claw platform is operated directly within the company’s network environment.

The parent domain 360[.]cn is registered to 北京奇虎科技有限公司 (Beijing Qihoo Technology Co., Ltd.), a major Chinese cybersecurity and internet-technology firm. Domain registration records show that the domain was originally created on 17 March 2003 and is maintained through the registrar Xiamen eName Technology Co., Ltd. These details align with long-standing records identifying Qihoo 360 as the primary operator of the 360[.]cn domain space and its associated services.

The use of dedicated DNS and mail infrastructure under corporate-controlled domains strongly suggests that Qihoo 360 manages its core network services internally rather than outsourcing these functions to third-party providers. This pattern is typical of large security vendors that maintain tight operational control over their infrastructure for security, reliability, and compliance reasons.

Additional enrichment data indicates that the 360.cn domain environment routinely deploys wildcard TLS certificates issued by WoTrus, the same certificate authority responsible for the *.myclaw.360.cn certificate examined in this investigation. The reuse of this certificate authority and wildcard certificate deployment pattern across the broader Qihoo domain ecosystem reinforces the conclusion that the MyClaw certificate originates from the company’s established PKI practices rather than from an unrelated or externally managed infrastructure.

Taken together, the domain registration data, passive DNS records, and PKI deployment patterns provide strong attribution linking the myclaw.360.cn namespace to Qihoo 360’s operational infrastructure, supporting the assessment that the exposed TLS credentials were associated with a service environment under the company’s direct control.

Threat Analysis

The exposure of a Transport Layer Security (TLS) private key associated with a wildcard certificate introduces several potential attack scenarios that could compromise both the integrity and confidentiality of communications within the affected service environment. Because TLS certificates serve as the cryptographic mechanism through which clients authenticate remote servers and establish encrypted channels, possession of the corresponding private key effectively allows an attacker to masquerade as legitimate infrastructure. In this case, the affected certificate covers the wildcard namespace *.myclaw[.]360[.]cn, meaning that any service operating under that domain could theoretically be impersonated if the certificate remained trusted and unrevoked.

One of the most direct risks presented by such an exposure is server impersonation. An attacker in possession of the private key could deploy a malicious server configured to present the same certificate during TLS negotiation. Because the certificate chains to a publicly trusted certificate authority and matches the expected domain namespace, client applications connecting to the attacker’s infrastructure would likely complete the TLS handshake successfully and treat the connection as legitimate. The wildcard nature of the certificate significantly amplifies this risk, as it would allow the attacker to impersonate any host within the myclaw[.]360[.]cn namespace rather than a single specific service endpoint.

A related and potentially more damaging scenario involves man-in-the-middle (MITM) interception. If an attacker were able to manipulate DNS responses, compromise a local network, or otherwise redirect client traffic, they could route requests intended for legitimate MyClaw infrastructure to servers under their control. Because the attacker possesses the correct private key, the TLS handshake would succeed and encrypted sessions would be established without triggering certificate warnings. Under such circumstances, the attacker could decrypt and inspect traffic passing through the connection. Data potentially exposed through such interception could include authentication credentials, session cookies, API tokens used by the application, and the contents of AI prompts or conversation logs exchanged between the client and backend inference services.

Another risk concerns malicious update distribution. Many modern software platforms retrieve updates, configuration files, or model components from backend servers under their operational domain namespace. If the Security Claw client retrieves such resources from endpoints within myclaw[.]360[.]cn, an attacker capable of impersonating those endpoints could deliver modified update packages or configuration files. In the worst case, this could allow the distribution of malicious binaries to client systems, effectively transforming the incident into a supply-chain compromise affecting all users receiving the spoofed updates.

Finally, the exposure creates the possibility of AI response manipulation within the Security Claw platform itself. Because the platform functions as an AI assistant that communicates with backend inference services, impersonating those services could allow attackers to alter responses returned by the AI system. This could enable injection of malicious prompts, manipulation of analysis results, or the insertion of misleading security guidance into automated workflows. In environments where the AI system assists with security analysis or operational decision-making, such manipulation could have cascading effects on downstream processes.

Taken together, these scenarios illustrate how the compromise of TLS trust material, particularly a wildcard certificate, can extend beyond simple traffic interception and potentially affect software distribution mechanisms, AI service integrity, and user trust in the platform’s infrastructure.

Potential AI-Enabled Attack Scenarios Leveraging Compromised Security Claw Infrastructure

The exposure of a private key associated with the wildcard TLS certificate for *.myclaw[.]360[.]cn introduces not only traditional network security risks such as impersonation and interception, but also a set of potential AI-enabled attack vectors that could exploit the architecture of the Security Claw platform itself. Because the platform appears to function as a locally installed AI assistant communicating with cloud-hosted inference services, control over the cryptographic trust boundary between client and backend services could enable adversaries to manipulate the AI system’s behavior in ways that extend beyond conventional software compromise. The integration of AI inference services into the operational workflow effectively creates a new attack surface in which model outputs, prompts, and analytic results become potential targets for adversarial manipulation.

One plausible attack scenario would involve AI response manipulation at the inference layer. If an attacker were able to impersonate backend inference services using the compromised certificate, they could intercept requests from the Security Claw client and return modified outputs generated by a malicious or modified AI model. In practice, this could allow the adversary to alter the results of automated security analyses performed by the platform. For example, malware samples submitted for analysis could be falsely classified as benign, while legitimate system components could be flagged as malicious. Such manipulations could degrade the reliability of the platform’s analytic output and undermine trust in automated security recommendations generated by the system.

Logic Diagram for Potential Attacks From Mistake

Another potential attack vector involves prompt-injection attacks targeting the AI interaction pipeline. Modern AI assistant architectures often rely on structured prompts sent from the client to backend models, where contextual instructions and system policies guide the model’s behavior. An adversary positioned within the communication channel could modify these prompts before they reach the inference service or inject additional instructions into the prompt stream. By manipulating these inputs, attackers could influence the behavior of the AI model, potentially causing it to disclose sensitive data, generate misleading analyses, or execute unintended actions within automated workflows. This type of attack is conceptually similar to adversarial prompt injection techniques observed in other large-language-model deployments.

A related scenario involves model poisoning or model-substitution attacks. If the Security Claw platform retrieves model components, configuration files, or inference instructions from backend servers under the myclaw[.]360[.]cn namespace, an adversary capable of impersonating those endpoints could distribute modified model weights or configuration artifacts to client systems. Such modifications could subtly alter the behavior of the AI system over time. For instance, the modified model might consistently downgrade the severity of certain classes of threats, ignore specific indicators of compromise, or generate outputs designed to mislead analysts reviewing the results. Because AI models often behave probabilistically rather than deterministically, detecting such manipulation could be significantly more difficult than identifying conventional malware.

The compromise could also enable data exfiltration through the AI interaction channel. Security Claw appears to operate as an assistant capable of processing user prompts, system telemetry, and potentially sensitive security data. If adversaries intercepted or controlled the backend inference endpoint, they could capture large volumes of input data sent from client systems. This data could include malware samples, internal network information, security logs, configuration data, or investigative notes submitted by analysts interacting with the AI assistant. Over time, such data collection could yield valuable intelligence about organizational networks, defensive tools, and investigative workflows.

Another possible attack vector would involve AI-driven social engineering and influence operations directed at analysts using the platform. If attackers controlled the AI responses returned to users, they could craft outputs designed to subtly influence human decision-making. For example, the AI might recommend specific remediation steps that inadvertently weaken security controls, suggest the dismissal of legitimate alerts, or provide misleading threat-intelligence summaries. Because users may perceive AI-generated recommendations as authoritative, particularly when the platform is marketed as a cybersecurity assistant, such manipulation could have cascading operational consequences within security operations centers or incident-response teams.

The exposure could further facilitate autonomous reconnaissance and exploitation capabilities embedded within the AI service architecture. If the adversary were able to modify backend AI services rather than merely impersonate them, they could theoretically integrate automated reconnaissance capabilities into the system itself. In this scenario, the AI service might analyze telemetry collected from client systems and automatically identify exploitable vulnerabilities or network configurations. Rather than simply returning analytic results to the user, the compromised system could covertly transmit reconnaissance data to attacker infrastructure or generate tailored exploit payloads targeting discovered weaknesses.

Finally, there is the possibility of supply-chain amplification through AI-driven automation. Security Claw’s architecture suggests that it may be integrated with broader Qihoo security services, potentially including threat-intelligence feeds, malware analysis pipelines, or automated defensive tooling. If attackers were able to manipulate the AI system at the backend level, they could leverage this integration to propagate malicious outputs across multiple connected services. For example, manipulated threat classifications could influence automated detection signatures distributed to endpoint security products, potentially degrading detection capability across a large installed base of users.

Taken together, these scenarios illustrate how the compromise of cryptographic trust material in an AI-enabled platform could enable attack techniques that extend beyond traditional network security threats. In conventional systems, the theft of a TLS private key primarily enables impersonation or interception attacks. In AI-integrated architectures, however, control over the communication channel between client and inference service also enables adversaries to manipulate the informational outputs of the system itself. Because users increasingly rely on AI-generated analysis to support operational decisions, such manipulation could have downstream effects that propagate through automated workflows, investigative processes, and defensive strategies.

Root Cause Assessment

The most plausible explanation for the exposure of the TLS private key is a failure within the software build and packaging pipeline used to produce the Security Claw client installer. Evidence associated with the incident indicates that the certificate and its corresponding private key were present within files bundled in the application’s installation package, suggesting that sensitive credential material was inadvertently included during the software build process.

In contemporary software development environments, application installers are frequently generated automatically through continuous integration and continuous delivery (CI/CD) pipelines. These pipelines often assemble installation packages directly from development repositories or build directories that may contain configuration files, test certificates, and other credentials used during internal development and debugging. If the build pipeline does not explicitly exclude such files through filtering rules or packaging controls, sensitive artifacts can unintentionally become part of the final distribution bundle.

This type of exposure is consistent with a broader class of supply-chain vulnerabilities in which development credentials are mistakenly distributed alongside production software. Similar incidents have been documented across the software industry, including cases where application installers or container images contained embedded API keys, code-signing certificates, or cloud service credentials. In each case, the root cause typically involved insufficient separation between development assets and production build artifacts, allowing confidential materials to propagate into publicly accessible software packages.

Analytical Assessment

The exposure of a private key associated with a wildcard TLS certificate constitutes a serious failure in the protection of cryptographic trust material. Within modern internet security architecture, TLS certificates serve as the foundation of authenticated encrypted communication between clients and servers. The corresponding private key is the critical secret that enables a server to prove its identity during the TLS handshake process. When this key is exposed outside of controlled infrastructure, the integrity of the entire trust relationship established by the certificate is compromised. In this case, the risk is amplified by the fact that the certificate is a wildcard credential for the domain namespace *.myclaw[.]360[.]cn, meaning that the key could theoretically authenticate any service operating under that domain hierarchy. As a result, possession of the private key could allow an attacker to impersonate multiple services across the platform rather than a single isolated endpoint.

Although the ultimate operational consequences depend on several factors including whether the certificate was actively deployed in production infrastructure and how quickly the credential was revoked or replaced, the discovery of the key in a publicly accessible software artifact strongly suggests that sensitive trust material was mishandled during the platform’s build or distribution process. Software installers and packaged binaries should never contain cryptographic secrets intended for server-side authentication. Their presence in distributed software indicates that development or deployment environments likely included credential files that were not properly excluded during packaging. Such mistakes are typically symptomatic of weaknesses in build pipeline controls, including insufficient separation between development assets and production artifacts, inadequate secret-scanning procedures, or a lack of automated checks designed to prevent sensitive files from being included in release builds.

The incident is particularly notable because it involves Qihoo 360, a company whose core business is cybersecurity. As a major provider of antivirus software, enterprise security tools, and threat-intelligence services, Qihoo 360 operates infrastructure that supports hundreds of millions of users. Organizations of this scale are expected to maintain mature security engineering practices, including strict credential management policies, secure build pipelines, and rigorous release validation procedures. The appearance of operational cryptographic material in distributed software raises questions about the robustness of those internal controls.

Even if the certificate was never deployed in production systems or if the exposure window was short due to rapid incident response and key rotation, the leak nonetheless highlights systemic risks associated with credential management within modern software development environments. Large-scale platforms frequently rely on numerous certificates, API keys, and other authentication secrets to operate complex distributed architectures. Without strong safeguards, these secrets can inadvertently propagate through development repositories, build directories, or installer packaging processes.

In this context, the exposure should be understood not only as a discrete technical vulnerability but also as an indicator of broader process weaknesses. Effective security engineering requires strict segregation of sensitive credentials, automated detection mechanisms for secrets in build artifacts, and clear procedures for revocation and rotation when exposure occurs. The presence of a valid TLS private key in publicly distributed software suggests that at least some of these controls were insufficiently implemented or failed during the platform’s release cycle. As AI-enabled platforms like Security Claw become more deeply integrated into security workflows and enterprise environments, ensuring the integrity of the cryptographic infrastructure underpinning these systems becomes increasingly critical.

Conclusion

Cryptographic validation confirms that the exposed material is a legitimate and operational TLS key pair for *.myclaw[.]360[.]cn. The RSA modulus in the certificate and private key match exactly, proving authenticity. The certificate chains to WoTrus RSA DV SSL CA 2, a publicly trusted authority, meaning any server using this key would be accepted as legitimate by clients.

The certificate’s structure aligns with Qihoo 360’s broader PKI practices, which rely on WoTrus-issued, domain-validated wildcard certificates to secure microservice-based architectures. As such, compromise of the private key represents a direct breach of the platform’s cryptographic trust boundary.

If deployed in production, the impact is substantial. An attacker with the key could impersonate any host within the myclaw.360.cn namespace, enabling seamless TLS-authenticated connections that appear legitimate. This extends beyond single-host compromise to full namespace-level impersonation. Under traffic redirection conditions (e.g., DNS or network manipulation), the same capability enables decryption and inspection of encrypted sessions, exposing credentials, tokens, and AI interaction data.

The risk also extends into platform integrity. Impersonated endpoints could deliver malicious updates or configuration data, while spoofed inference services could manipulate AI outputs or inject instructions into the interaction pipeline.

Impact ultimately hinges on deployment status and response timing. Evidence indicates the certificate was rapidly rotated, suggesting effective incident response and a potentially limited exposure window. However, if the key was obtained prior to rotation, exploitation during that interval remains plausible.

Further analysis should focus on certificate transparency logs, passive DNS telemetry, and any vendor disclosures to establish timeline, exposure scope, and whether the compromised certificate was ever actively used.

Appendix A: Data

360Claw的SSL证书泄漏

-----BEGIN CERTIFICATE-----
MIIGJjCCBI6gAwIBAgIRAJjf6v3EwyNx8KtJDIo8eBkwDQYJKoZIhvcNAQELBQAw
SjELMAkGA1UEBhMCQ04xGjAYBgNVBAoTEVdvVHJ1cyBDQSBMaW1pdGVkMR8wHQYD
VQQDExZXb1RydXMgUlNBIERWIFNTTCBDQSAyMB4XDTI2MDMxMjAwMDAwMFoXDTI3
MDQxMjIzNTk1OVowGjEYMBYGA1UEAwwPKi5teWNsYXcuMzYwLmNuMIIBIjANBgkq
hkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA6GoA3XNAfUmduNst6xvYGphkmWpMKTC9
RCJpvKoKSuFylOwY+tGm8vmKl4Uc4SYUp3a8TdFjFtEzCuRqEo3IVYRdenk94K8G
jqg09hGg87MmUbI+PDzXJYZiCjrIHib+VK9V1RyBypA78ju6Gnpw4F4EFa3PjM1+
BUYpU3yxqx7gALSJLu6fuia+jV+LnawxB2ZVyoEgcYZYoJFQ9l9MuO6K9Wy7FK9e
MLgVwkB/ZFYl3olAJonkgmtopdWSJtort9bUxD4Oqbs6sc2YBLfcrXyX4cp3KGAr
3UfhptKlKyA0MZJJaT4NOvXrAlSd6JF3Gu6rfj/+G4x7hPCpKuIOXQIDAQABo4IC
tTCCArEwHwYDVR0jBBgwFoAU2LPS8kwDXVRDQ6AmyEVq9BSUg8gwHQYDVR0OBBYE
FD+ZRI/zTmye9mMCFP7QOfIIGC6gMA4GA1UdDwEB/wQEAwIFoDAMBgNVHRMBAf8E
AjAAMBMGA1UdJQQMMAoGCCsGAQUFBwMBMBMGA1UdIAQMMAowCAYGZ4EMAQIBMGsG
CCsGAQUFBwEBBF8wXTA3BggrBgEFBQcwAoYraHR0cDovL2NydC5jcmxvY3NwLmNu
L1dvVHJ1c1JTQURWU1NMQ0EyLmNydDAiBggrBgEFBQcwAYYWaHR0cDovL29jc3Au
Y3Jsb2NzcC5jbjApBgNVHREEIjAggg8qLm15Y2xhdy4zNjAuY26CDW15Y2xhdy4z
NjAuY24wggGNBgorBgEEAdZ5AgQCBIIBfQSCAXkBdwB1AByfaCzp+vBFaVD4G5aK
h93bMhDYTObIsuOCUkrEz1mfAAABnOD5fqQAAAQDAEYwRAIgav6fXo4u77J/Ta2T
OdvnF7C/t4My7dmaGFQ7aQ4JzvQCIB66E+kdquRt2EZ/2QqbdOTqMscgGVpV7Gnn
WXLNmY2hAH8AjspHC6zeavOiBrCkeoS3Rv4fxr+VPiXmm07kAkjzxugAAAGc4PmA
zgAIAAAFAAMZgWAEAwBIMEYCIQDSKSYgaC6oG7l2yjSbwtqdCtQ0Hi7Mz8yWWrUr
W5edNQIhAKQMvy6g0naB/IOEDihyq4nNejnufZ7kwDh76GnNxFnJAH0AWW5sM4aU
sllyolbIoOjdkEp26Ag92oc7AQg4KBQ87lkAAAGc4Pl96QAIAAAFAAA48LcEAwBG
MEQCIEFrHnt8cRycbZpEngvZmTa2LnOUHKmrdfX4ElNDnMyuAiAw8aUn7hWtqT58
+HI2rZBu/BCtX+6q6YjQ/eeX7LfhDjANBgkqhkiG9w0BAQsFAAOCAYEAg09UAgUz
HzknfmUBH4FGGXYKurPZh3BPyDzaA1LPJVIYfseEhR6N7+iDSDt9tSez/H0aW0sm
rzP942kUe/afF827P3h5I8qsbqrfJVKCAdFfMHqYeb4JA6um/MFWBT0Av1SpcO1y
ewBPDn+xDbsDJe5UQ8gT2N6q/ktspiA/h4AgLfIsdY/4OxcpuKubht6enzUJSzsr
0I/TF0j/G7fFtSn3a2AwQSafuuClfPeX2Dt1oiDiLhqYpGQ1ZYGIAEutVJEHbW0G
Z4CBNy+OQ8U0FeRp5UdKZxCz9ZkFg7RNUc63UP0oKpKEZhBQ8ftb2C/H0Zd1uUiJ
ayL1E4Qy80vt2XMTjNacCAF5l8T6MmfJI4w2zWsggoV6wsLxcd/AfUzXlZFQezKg
jGUSfm4dYVXoSda/lB+BZSsgfx5DCC2N6bOMazNDqCT2C23S6nLOt9TdpjaUVe4T
zuwrbXc0Rtr2iLTmZAeGET5GJWib8Zb63zqo52OzBgydQaCt3bcQVfZZ
-----END CERTIFICATE-----

key

Learn More
Research
Doppelgänger / RRN Disinformation Infrastructure Ecosystem 2026

Analysis of the Doppelgänger / RRN disinformation ecosystem. Learn how this DevOps-style infrastructure uses automated media impersonation, TLD rotation, and cloud-native hosting to target global audiences and evade enforcement.

Executive Summary

The Doppelgänger / RRN ecosystem (RRN = Reliable Recent News) constitutes a new iteration of the Social Design Agency (SDA),  a structurally mature, infrastructure-centric disinformation architecture that has been operating continuously from 2022 through 2026. Rather than functioning as a loose collection of spoofed websites or transient propaganda outlets, the network exhibits the hallmarks of a coordinated, professionally managed influence apparatus. Its design prioritizes infrastructure resilience, scalability, and operational continuity over short-term visibility.

At its core, the ecosystem relies on systematic media brand impersonation executed at scale. Recognizable Western news outlets are replicated through domain substitution, typo variants, and semantic extensions, producing a high-volume impersonation layer that mimics legitimate journalism. These impersonation domains are not isolated artifacts; they are anchored to a centralized narrative constellation built around the RRN brand family, which functions as a clearinghouse and coordination node for messaging.

rrn[.]com[.]tr current iteration of Researchers & Reporters Network (aka Doppelganger Disinfo Network)

Domain acquisition patterns indicate batch provisioning during defined campaign waves, most notably in mid-2022 and again in late-2024. These bursts reflect deliberate staging cycles rather than organic domain accumulation. Complementing this provisioning model is a deliberate top-level domain diversification strategy. The operation leverages low-cost and low-scrutiny TLDs, rotates extensions in response to enforcement actions, and preserves second-level domains across TLD swaps to maintain continuity. This enforcement-aware migration pattern demonstrates pre-positioned redundancy and lifecycle planning.

Hosting and delivery architecture further reinforce the operation’s sophistication. The ecosystem is cloud-native and heavily fronted by content delivery networks that obscure origin infrastructure. Backend services are distributed across hyperscaler platforms, including Google Cloud and to a lesser extent AWS, along with static asset reuse from legitimate domains, with micro-clustering patterns that distribute risk and reduce single points of failure. The absence of concentrated Russian hosting infrastructure suggests attribution resistance through geographic neutrality rather than lack of coordination.

Backend artifacts reveal structured CMS management. WordPress deployments exhibit role-based segmentation, coordinated account provisioning, and SEO-oriented publishing controls. These features indicate centralized backend governance and editorial workflow discipline. The infrastructure also reflects automated domain variant generation, employing scripted logic for brand tokens, typographical alterations, and semantic suffix combinations. This level of automation is consistent with a provisioning pipeline rather than manual spoofing. Assistance from Amazon Web Services Threat Intelligence enriched the presence of AWS IP addresses, identifying primarily legitimate assets being reused in off-AWS infrastructure.

The campaign demonstrates deliberate geographic micro-targeting across European Union member states and the United States. Infrastructure segmentation mirrors narrative segmentation, with country-specific impersonation clusters aligned to regional political contexts. This coupling of technical segmentation and messaging strategy underscores a hybridization of cyber infrastructure tradecraft and psychological operations.

Taken together, these characteristics indicate DevOps-style provisioning discipline and resilience engineering. Domains are stockpiled, rotated, and redeployed with minimal disruption. Infrastructure is compartmentalized, diversified, and rapidly replaceable. Such operational maturity is consistent with institutional backing and sustained management, rather than opportunistic or freelance activity.

Campaign Architecture Model

Across both structural reporting and dataset analysis, the campaign exhibits a deliberately layered and modular operational model. The architecture is not flat, nor is it improvisational. Instead, it reflects clear functional segmentation, with each tier responsible for a distinct operational objective.

At the apex sits an operator coordination layer. This tier likely manages provisioning workflows, narrative timing, infrastructure deployment, and enforcement response. It is the command-and-control plane of the information operation, though not in the malware sense; rather, it orchestrates domain registration cycles, publishing cadence, and geographic targeting priorities. This also shows the banality of disinformation being just a process driven means to a larger end in the global war on reality.

Beneath this layer resides the core narrative hub, anchored by the RRN domain family. This constellation functions as a central content repository and thematic synchronization point. It consolidates narratives, standardizes messaging frames, and acts as a reference anchor for downstream properties. When seizures occur, this hub migrates in controlled fashion, preserving continuity through second-level domain retention and TLD substitution.

Below the hub tier are country-specific narrative front domains. These properties localize messaging for particular audiences, adapting tone, framing, and political emphasis according to national context. They provide plausible deniability by presenting themselves as independent outlets, while remaining structurally tethered to the broader ecosystem.

The next layer consists of media impersonation domains. These are the most visible components of the campaign, designed to replicate established Western media brands with high visual fidelity. Their purpose is brand deception: to exploit audience trust in recognizable outlets and to embed narratives within seemingly legitimate editorial environments.

Supporting these front-facing elements is a redirect and tracking layer. This tier manages traffic flow, referral routing, and possibly engagement analytics. It enables flexible amplification pathways and allows operators to shift traffic patterns without modifying core content nodes.

Above distribution sits the SEO optimization layer. Search visibility is engineered through keyword structuring, backlink strategies, and metadata tuning. This layer ensures that impersonation domains surface within search ecosystems, increasing organic discovery and enhancing perceived legitimacy.

Finally, social media amplification functions as the outermost dissemination ring. Coordinated accounts, paid promotion, or content seeding strategies drive traffic toward the impersonation domains. Social platforms act as accelerants, extending reach into geographically segmented audiences.

At the terminus of this layered model are the audiences themselves, segmented by geography and political context. Messaging is not broadcast uniformly; it is calibrated. German audiences receive different narrative emphasis than U.S. or French audiences, even when core themes remain aligned.

This architecture separates content generation, brand deception, distribution mechanics, and resilience engineering into discrete but interconnected layers. The result is a modular influence system capable of rapid reconfiguration. When one layer is disrupted, such as through domain seizure, the remaining tiers persist to enable continuity. This structural separation is a defining feature of the campaign’s operational maturity.

Domain Corpus & Structural Clustering

The domain ecosystem resolves into three principal structural tiers: core hubs, narrative fronts, and media impersonation clusters. Each tier performs a distinct operational role within the broader influence architecture.

The core RRN hubs function as the gravitational center of the campaign. Observed anchors include rrn[.]world (2022-2025 * as of 2026 domain re-purposed by unknown entities exposing the  doppelganger/SDA group), the previously seized rrn.media, its post-enforcement successor rrn[.]so, rrn[.]com[.]tr, and the earlier rrussianews[.]com. These domains operate as centralized narrative clearinghouses. They provide thematic consistency, content staging, and coordination continuity. When enforcement actions occur, the transition between domains preserves second-level naming conventions, indicating planned migration rather than reactive improvisation. The hub tier is not simply a publishing site; it is the synchronization layer for the ecosystem’s messaging and lifecycle management.

Beneath this central constellation sit the narrative front domains. Properties such as 50statesoflie., acrosstheline., avisindependent.eu, artichoc[.]cc, levinaigre[.]so, ukrlm[.]so, and shadowwatch[.]us are structured to appear as independent editorial outlets. Their purpose is reframing. Rather than overtly presenting RRN branding, they repackage aligned narratives under the veneer of autonomous journalism. This layer introduces plausible deniability and audience-specific tonality while remaining structurally tethered to the broader system. The naming conventions are less overtly imitative than the impersonation tier, but they are thematically suggestive, often invoking investigative or oppositional framing.

The largest and most visible component of the ecosystem is the media impersonation cluster, comprising approximately sixty percent of the observed domain corpus. This tier includes clones of prominent Western outlets such as Spiegel, Bild, Süddeutsche Zeitung, FAZ, Welt, T-Online, The Guardian, Daily Mail, ANSA, and variants referencing Fox News. These domains are engineered to replicate the visual and structural appearance of legitimate news brands, exploiting pre-existing public trust.

Impersonation within this cluster follows consistent technical patterns. Top-level domain substitution replaces primary brand extensions with lower-cost or less scrutinized alternatives. Typosquatting mechanisms include letter duplication, omission, and phonetic substitution, creating visually plausible but technically distinct domains. Additional variants employ brand-semantic suffixes or geographic modifiers to enhance credibility while maintaining differentiation from the authentic domain. The repetition and systematic variation across these brand families strongly suggest automated or scripted domain generation logic rather than manual, ad hoc spoofing.

Taken together, these three tiers illustrate a graduated deception model. The core hubs centralize narrative control. The narrative fronts contextualize and reframe messaging under independent branding. The impersonation clusters maximize credibility exploitation through high-fidelity replication. The structural coherence across all three layers reinforces the conclusion that this is a coordinated provisioning ecosystem rather than isolated instances of media spoofing.

Temporal analysis of the 48-domain dataset reveals that domain acquisition did not occur as a continuous or organic process. Instead, registrations cluster into two distinct provisioning bursts, each aligned with identifiable geopolitical inflection points.

The first wave occurred in mid-2022, coinciding with the escalation phase of the war in Ukraine. During this period, domain registrations expanded rapidly across multiple brand families and narrative fronts. The timing suggests synchronization with heightened geopolitical tension and intensified information competition. Rather than opportunistic spoofing, the burst reflects pre-coordinated deployment intended to support sustained narrative operations during a critical phase of the conflict.

The second wave emerged in September 2024. This provisioning cycle aligns with Western electoral timelines and follows public enforcement actions targeting earlier Doppelgänger infrastructure. The pattern indicates both narrative refresh and infrastructure regeneration. Domains registered during this period show evidence of replacement logic, TLD diversification, and continued brand-family clustering, consistent with an adaptive response to seizure activity.

Across both waves, several structural characteristics remain consistent. Registration timestamps fall within narrow windows, suggesting batch provisioning rather than independent acquisition. Multiple domains tied to the same media brand families appear within close temporal proximity, reinforcing the likelihood of centralized control. The recurrence of identical naming logic across separate waves further indicates a reusable deployment pipeline.

This temporal clustering model is incompatible with organic domain growth. Instead, it reflects planned campaign staging cycles in which infrastructure is provisioned in anticipation of narrative events or in response to enforcement disruption. The pattern is consistent with structured influence operations that operate in defined phases rather than continuous improvisation.

TLD Strategy & Enforcement Evasion

Analysis of top-level domain selection reveals a deliberate concentration in a specific family of extensions. Dominant TLDs across the ecosystem include .media, .agency, .ltd, .today, .life, .ws, .cc, .so, .beauty, .expert, .vip, .pics, and .top. The distribution is neither random nor purely aesthetic; it reflects operational utility.

These extensions share several characteristics. They are generally low in acquisition cost, widely available at scale, and subject to comparatively limited scrutiny relative to legacy TLDs. Many also carry news-semantic or quasi-professional connotations such as .media, .agency, .today, or .expert which enhance surface credibility when paired with recognizable media brand tokens. This semantic plausibility increases the likelihood that users will perceive the domains as legitimate news outlets rather than synthetic replicas.

The selection pattern also supports rapid provisioning and replacement. Because these TLDs are typically less saturated than primary brand equivalents, operators can register multiple variants quickly and in batch. This flexibility is critical to enforcement resilience.

Observed seizure-to-migration behavior reinforces this assessment. When rrn[.]media was disrupted, operations pivoted to rrn[.]so while preserving the second-level domain. Similarly, 50statesoflie[.]com reappeared under .cc and .so variants, and acrosstheline[.]press transitioned to a .cc counterpart. In each case, the second-level domain remained intact while only the top-level extension changed.

Preservation of the second-level domain across new TLDs constitutes a very high-confidence linkage signal. It demonstrates continuity of operator control and planning rather than independent replication. The pattern indicates that alternate TLDs were likely pre-positioned or rapidly provisioned using the same deployment pipeline. This TLD substitution model is therefore not merely a branding choice; it is a resilience mechanism embedded within the infrastructure strategy.

Registrar & Registration Patterns

Registrar-level analysis indicates deliberate diversification rather than consolidation. Domains within the ecosystem are distributed across multiple commercial registrars, including Cloudflare, GoDaddy (Jomax), Namecheap, Dynadot, and Porkbun. No single registrar dominates the corpus. This dispersion reduces the likelihood of centralized administrative exposure and complicates straightforward clustering based on registrar account identifiers alone.

Privacy shielding is applied almost universally. Registrant information is redacted or routed through privacy services, limiting direct attribution vectors. Registration durations are typically short, most commonly one- to two-year terms, reinforcing the disposable nature of the infrastructure. There is no evidence of long-term brand cultivation or multi-year strategic retention of primary domains. Instead, domains appear engineered for limited operational lifespan, with replacement assumed as part of the lifecycle model.

Taken together, these characteristics support a strategy of attribution resistance through registrar diversification. By spreading registrations across multiple providers, the operators reduce the impact of any single registrar-level disruption or investigative pivot. This also suggests compartmentalization: different domain clusters may be provisioned under separate registrar accounts to prevent a single compromise from exposing the full network.

The lifecycle management model is explicitly disposable. Domains are provisioned for campaign phases, used for narrative dissemination, and abandoned or replaced following enforcement pressure or strategic refresh cycles. This is consistent with burst registration waves and TLD substitution behavior observed elsewhere in the ecosystem.

Hosting & IP Space Analysis

Infrastructure analysis reveals a consistent architectural pattern built around layered hosting abstraction. At the outermost layer, domains are fronted by Cloudflare, which provides edge delivery, caching, and origin masking. This CDN fronting obscures backend IP exposure and complicates direct attribution through simple DNS resolution. Behind this edge layer, backend services are deployed across hyperscale cloud providers, principally Google Cloud, where individual sites resolve to distributed virtual instances. At the application layer, disposable WordPress nodes function as the publishing engine, allowing rapid content deployment and replacement without persistent infrastructure commitments.

The dataset supports this model. Across 48 domains, 34 unique IP addresses were observed, indicating distributed backend allocation rather than centralized hosting. A substantial portion of domains resolved through Cloudflare address space in the 104.x range, reinforcing the prevalence of CDN masking. Backend nodes and functions appeared in Google Cloud 34.x ranges as well as some lesser activity in AWS 15.x ranges, often in small micro-clusters of related domains sharing hyperscaler infrastructure or repurposing static assets or content from legitimate websites. A minor presence of European hosting providers exists, but without concentration sufficient to suggest geographic anchoring.

This configuration reflects a cloud-native deployment strategy optimized for flexibility and resilience. Hyperscaler infrastructure provides rapid provisioning, geographic neutrality, and scalable bandwidth, while CDN masking reduces visibility into origin servers. The distributed IP footprint and lack of single-ASN concentration further enhance survivability and reduce detection risk.

Notably, there is no observable concentration of infrastructure within Russian autonomous systems. This absence should not be interpreted as contradictory to Russian-aligned tradecraft. On the contrary, reliance on Western hyperscalers and CDN masking aligns with evolved attribution-resistant design principles. By operating within globally reputable cloud ecosystems, the campaign blends into high-volume commercial traffic, leveraging legitimate infrastructure to reduce investigative friction.

The resulting hosting posture is deliberately attribution-resistant. It prioritizes redundancy, geographic neutrality, and rapid redeployment capacity over static hosting stability. This design is consistent with a professionally managed influence operation engineered for persistence under enforcement pressure rather than a transient spoofing campaign.

DNS & Nameserver Linkage

DNS-layer analysis provides several high-probability linkage indicators that may offer stronger structural correlation than hosting data alone. While IP addresses can shift due to CDN fronting or cloud migration, nameserver configurations often persist across operational changes and therefore provide a more durable pivot.

One primary indicator would be the reuse of identical nameserver pairs across multiple brand families. If domains impersonating unrelated outlets such as Spiegel, Bild, and Süddeutsche share the same NS records, the likelihood of independent registration diminishes substantially. Shared nameserver infrastructure across distinct media brands would suggest centralized DNS provisioning rather than coincidental overlap.

A related signal would be reliance on the same DNS provider across otherwise unrelated impersonation domains. When domains targeting different national audiences or brands resolve through a common DNS control environment, it implies coordination at the administrative level. Similar time-to-live (TTL) values across domains can further reinforce this signal, as TTL configurations often reflect default settings applied at the account or template level rather than individually tuned parameters.

Consistency in Start of Authority (SOA) structure such as identical formatting conventions, refresh intervals, or authoritative contact placeholders would provide additional evidence of centralized DNS management. SOA artifacts are rarely manipulated for cosmetic purposes and often reveal provisioning templates used by operators.

If nameserver reuse were observed across the Spiegel, Bild, Süddeutsche, and RRN domain families, it would strongly indicate a unified DNS control plane underpinning both narrative hubs and impersonation properties. Such convergence would demonstrate that, despite registrar dispersion and TLD diversification, domain resolution remains orchestrated from a common administrative layer.

In comparative evidentiary strength, nameserver clustering is likely a more robust attribution signal than IP overlap. IP infrastructure can be transient, especially in cloud-native deployments. Nameserver configurations, by contrast, frequently reflect centralized provisioning logic and are less susceptible to routine backend rotation. As a result, DNS-layer commonality may provide the clearest structural linkage within a distributed, attribution-resistant hosting environment.

Backend CMS Artifact Analysis

Forensic review of recovered WordPress artifacts provides insight into backend governance and operational discipline. The earliest observable provisioning activity indicates bootstrap configuration using a Yandex-linked email account, suggesting centralized initial setup rather than distributed contributor onboarding. Following this bootstrap phase, multiple accounts associated with the @rrn[.]com[.]tr namespace were rapidly provisioned, reflecting coordinated account creation within a defined administrative domain.

User roles within the CMS exhibit structured segmentation. Accounts labeled with function-specific identifiers such as “seoadmin” and “RRN_Staff” indicate differentiated permissions and workflow responsibilities. This separation of duties is characteristic of managed editorial environments rather than informal publishing collectives. The presence of search-engine-optimization–focused accounts further demonstrates that visibility engineering was embedded into backend operations, not treated as an afterthought.

Artifacts dated to 2025 reveal application-password configurations, which are typically associated with API integrations, automated publishing pipelines, or credential compartmentalization for security control. The continued presence of such artifacts indicates ongoing maintenance and lifecycle management rather than abandonment of infrastructure following enforcement pressure.

Collectively, these backend signals imply centralized coordination of publishing workflows, structured SEO integration, and sustained operational oversight. The pattern reflects a professionalized content management hierarchy with defined roles, controlled credential distribution, and repeatable provisioning logic. Such characteristics are inconsistent with decentralized volunteer activism or loosely organized advocacy networks. Instead, they align with a managed, institutionally structured information operation.

Automated Domain Generation Model

Domain naming patterns across the ecosystem reveal consistent construction logic indicative of automation rather than manual registration. The observed formats follow repeatable templates. The most straightforward pattern replicates the core brand token directly as a second-level domain paired with an alternate top-level extension. A second pattern appends semantic qualifiers to the brand, often news-oriented or temporal terms before applying a conventional TLD. A third variation incorporates geographic modifiers, creating localized variants that maintain brand recognition while implying regional relevance. Additional structures involve typographical manipulation of the brand token itself or preservation of the second-level domain during TLD migration events.

Typographical techniques follow predictable methods. Letter duplication produces visually plausible variants such as “bildd.” Letter omission removes characters to create near-identical strings, for example “blld.” Phonetic substitution alters spelling while retaining recognizability, as in “build.” Semantic suffixes such as “-today,” “-live,” or “-life” introduce news-related framing, while geographic modifiers like “-eu” or “-asia” imply localized legitimacy. These manipulations are systematic and repeat across multiple brand families, reinforcing the likelihood of template-based domain generation.

The preservation of the second-level domain across new TLDs during enforcement events further supports the presence of structured provisioning logic. Rather than improvising new names, operators maintain core tokens and rotate extensions, suggesting preplanned substitution pathways embedded within the registration pipeline.

The consistency and recurrence of these patterns strongly suggest a scripted bulk provisioning mechanism. Domain creation appears to follow predefined logic trees, enabling rapid generation of multiple variants per target brand. This automation facilitates scalability, redundancy, and rapid replacement following seizure or suspension.

Based on the observed logic, predictive domain templates can be modeled. Likely future variants would include constructions such as brand paired with “.media,” “.agency,” or “.today.” Hyphenated semantic extensions appended to established brands such as brand-live or brand-life are also probable. Additionally, migration to lower-scrutiny country-code or generic TLDs such as “.cc” or “.so” remains consistent with prior behavior.

Monitoring Certificate Transparency logs against these structured templates is recommended as an early-warning mechanism. Because automated pipelines often generate certificates shortly after registration, template-based CT monitoring may identify new impersonation domains before large-scale amplification occurs.

Geographic Target Segmentation

Geographic segmentation within the ecosystem reflects deliberate alignment between infrastructure deployment and narrative emphasis. Targeting is not uniform across regions; instead, infrastructure tactics and messaging themes are calibrated to local political contexts and audience sensitivities.

Germany emerges as the most extensively targeted environment. The infrastructure footprint there is dominated by high-volume media impersonation, particularly of prominent national outlets. The corresponding narrative focus centers on anti-NATO themes, criticism of sanctions policy, and efforts to widen domestic political divisions. The scale and density of impersonation domains associated with German brands indicate prioritization beyond incidental inclusion.

In France, the operational model blends media clones with narrative front domains. Messaging frequently emphasizes the economic costs of sanctions and promotes themes of Ukraine-related fatigue. The infrastructure suggests a strategy aimed at reframing policy debates through domestically contextualized narratives rather than direct geopolitical confrontation.

The United States is approached through narrative front properties combined with election-cycle framing. Rather than relying exclusively on high-fidelity impersonation of national outlets, the ecosystem leverages independently branded sites to question institutional legitimacy and amplify distrust in democratic processes. Timing of domain provisioning aligns with electoral periods, reinforcing the assessment of politically sensitive targeting.

In the United Kingdom, media impersonation remains the dominant tactic. Messaging themes concentrate on skepticism toward NATO policy and criticism of foreign engagement. The structure parallels the German model but appears narrower in scope.

Italy is targeted primarily through impersonation of ANSA and related institutional brands. The emphasis shifts toward undermining institutional trust and reinforcing domestic dissatisfaction narratives. This indicates adaptation to national media ecosystems and audience trust structures.

Across the broader European Union, the campaign employs an amplification mesh model. Rather than focusing exclusively on single-country impersonation clusters, domains and social distribution mechanisms propagate narratives across borders, fostering cross-national polarization and reinforcing pan-European fissures.

The relative density of impersonation domains, narrative alignment, and provisioning volume suggests that Germany represents the highest-priority target within the ecosystem. Infrastructure investment and thematic emphasis converge most heavily in that information environment, indicating strategic weighting rather than incidental inclusion.

Germany appears highest-priority target.

What the Infrastructure Is Not

Infrastructure analysis reveals a consistent absence of indicators typically associated with financially motivated cybercrime or intrusion-focused operations. There is no evidence of malware command-and-control coordination embedded within the observed domains. The hosting architecture, DNS behavior, and certificate issuance patterns do not reflect infrastructure designed to manage implants, beacon traffic, or staged payload delivery.

Similarly, there are no artifacts suggesting phishing kit reuse or credential-harvesting frameworks. The domains do not exhibit structural similarities to common phishing templates, nor do they display the rapid redirect logic or form-handling mechanics associated with account compromise campaigns. The absence of credential collection endpoints or kit fingerprint overlap further distinguishes this ecosystem from conventional fraud operations.

There is also no observable affiliate monetization structure. The infrastructure does not show integration with traffic arbitrage networks, affiliate referral programs, or performance-based revenue systems. Domain lifecycles are short and aligned with narrative waves rather than revenue optimization windows. Likewise, there is no evidence of ad network integration, programmatic advertising infrastructure, or content-farming strategies designed to generate advertising impressions at scale.

Hosting patterns further differentiate the operation from typical criminal infrastructure. The ecosystem does not rely on bulletproof hosting providers or obscure offshore ASNs commonly associated with malware distribution or fraud. Instead, it leverages mainstream hyperscaler platforms and CDN fronting, prioritizing camouflage within legitimate cloud ecosystems rather than protection from law enforcement through hardened criminal service providers.

Collectively, these absences are analytically significant. The infrastructure is optimized for narrative dissemination, brand impersonation, and audience influence rather than financial extraction or technical exploitation. Its design reflects an information operation architecture engineered for credibility manipulation and distribution resilience. This is narrative delivery infrastructure, not cybercrime infrastructure.

Operational Maturity Assessment

The Doppelgänger ecosystem exhibits operational characteristics consistent with disciplined infrastructure engineering rather than ad hoc domain deployment. Provisioning behavior reflects DevOps-style methodology: domains are registered in coordinated bursts, deployed in structured waves, and integrated into a repeatable pipeline that supports rapid staging and replacement. Infrastructure is treated as code: scalable, replicable, and disposable.

Campaign activation appears synchronized with geopolitical or electoral inflection points, indicating burst staging rather than continuous organic growth. Domains are stockpiled in advance of use, enabling operators to activate replacement nodes with minimal latency following enforcement actions. This pre-positioned redundancy reduces operational downtime and demonstrates forward-planned lifecycle management.

Rapid pivoting in response to seizures further illustrates enforcement-aware design. When domains are disrupted, second-level identifiers are preserved and redeployed under alternate top-level domains. Hosting and DNS configurations are rotated without altering the broader narrative framework. The system absorbs disruption without collapsing, reflecting modular segmentation that isolates functional layers from single points of failure.

The architecture’s reliance on CDN masking, hyperscaler backend infrastructure, and distributed IP allocation demonstrates cloud-native proficiency. Deployment choices prioritize camouflage within legitimate commercial cloud environments, reducing attribution risk and complicating network-level blocking strategies. Infrastructure components are loosely coupled yet centrally coordinated, reinforcing resilience.

Attribution minimization is embedded throughout the lifecycle. Registrar dispersion, privacy shielding, and geographic hosting neutrality collectively reduce direct linkage signals. Operational design favors structural ambiguity while maintaining internal coherence.

The campaign’s evolution reflects increasing sophistication under pressure. During Phase I (2022–2023), the model centered on a relatively centralized RRN hub supported by impersonation spokes. Phase II (2024) introduced enforcement disruption through domain seizures, testing the resilience of the architecture. In Phase III (2024–2025), the ecosystem adapted into a more distributed modular mesh, reducing reliance on singular hubs and expanding TLD diversification.

Rather than diminishing under enforcement pressure, the infrastructure matured. Redundancy increased, segmentation deepened, and migration pathways became more seamless. The trajectory indicates learning and adaptation, reinforcing the assessment that the operation is professionally managed and strategically sustained rather than episodic or opportunistic.

Strategic Assessment

The Doppelgänger ecosystem exhibits characteristics consistent with industrialized influence infrastructure rather than episodic or improvised activity. Its provisioning discipline, redundancy planning, and lifecycle management imply sustained funding and coordinated oversight. The infrastructure is treated as a strategic asset, engineered for persistence under scrutiny and adaptable under enforcement pressure. This reflects a model in which infrastructure is not merely a vehicle for messaging but the foundation of the influence operation itself.

The operational posture aligns with an infrastructure-first influence warfare framework. Domains are provisioned in waves, diversified across TLDs, shielded behind CDN layers, and redeployed with minimal latency following disruption. Backend publishing environments are structured and role-segmented. DNS and hosting choices prioritize camouflage within legitimate hyperscaler ecosystems. These attributes collectively indicate that technical architecture is central to the campaign’s design, not secondary to narrative content.

Psychological operations are embedded within this technical foundation. Messaging is geographically segmented, timed to political cycles, and distributed through impersonation layers engineered to exploit audience trust. The technical and narrative components are integrated rather than siloed. DevOps-style provisioning supports narrative agility, enabling rapid amplification, replacement, or recalibration in response to geopolitical developments.

The campaign represents a hybridization of multiple strategic disciplines. Cyber infrastructure strategy provides resilience, obfuscation, and scalability. Narrative warfare supplies thematic direction and audience targeting. Search ecosystem manipulation ensures discoverability and legitimacy through SEO optimization. Election-cycle timing introduces temporal precision, aligning infrastructure activation with moments of heightened political sensitivity.

Taken together, these characteristics distinguish the operation from opportunistic spoofing or isolated propaganda efforts. The ecosystem reflects structured, enforcement-aware influence engineering. Its design anticipates disruption, incorporates redundancy by default, and integrates technical and psychological components into a cohesive operational model.

Editor’s Note: DomainTools Investigations engaged in pre-publication collaboration with both Google Threat Intelligence Group and Amazon Web Services Threat Intelligence on this material. Both teams were immediately responsive, engaging in analysis in their respective areas and providing helpful feedback. We appreciate their partnership.

Appendix A Domain Data Assessed Map (48 domains)

Domain constellation map

Domains Researched

20minuts[.]com
50statesoflie[.]cc
50statesoflie[.]com
50statesoflie[.]so
acrosstheline[.]cc
acrosstheline[.]press
ansa[.]ltd
artichoc[.]cc
avisindependent[.]eu
bild-d[.]beauty
bild[.]beauty
bild[.]expert
bild[.]llc
bild[.]pics
bild[.]work
bild[.]ws
bildd[.]beauty
bildd[.]lol
blld[.]live
build[.]vip
build[.]ws
dailymail[.]cfd
faz[.]agency
faz[.]life
fox-news[.]in
fox-news[.]top
levinaigre[.]so
rrn[.]com[.]tr
rrn[.]media
rrn[.]so
rrn[.]world
rrussianews[.]com
shadowwatch[.]us
spiegel[.]agency
spiegel[.]fun
spiegel[.]ltd
spiegel[.]media
spiegel[.]today
spiegeli[.]life
spiegeli[.]today
sueddeutsche[.]cc
sueddeutsche[.]co
sueddeutsche[.]me
theguardian-com[.]com
ukrlm[.]so
welt[.]ltd
welt[.]media
welt[.]ws

Appendix B Bibliography

Correctiv. 2024. “Inside Doppelganger: How Russia Uses EU Companies for Its Propaganda.” July 22, 2024. https://correctiv.org/en/fact-checking-en/2024/07/22/inside-doppelganger-how-russia-uses-eu-companies-for-its-propaganda/.

Der Spiegel. 2026. “Im Inneren der russischen Propagandamaschine.
https://www.spiegel.de/politik/hacktivist-infiltriert-desinformationskampagne-im-inneren-der-russischen-propagandamaschine-a-265fd485-1d0d-45b6-b0b3-4fd46091ddfa.   

Digital Forensic Research Lab (DFRLab). 2024a. “How Doppelganger and Other Russia-Linked Operations Target U.S. Elections.” September 6, 2024. https://dfrlab.org/2024/09/06/how-doppelganger-and-other-russia-linked-operations-target-us-elections/.

Digital Forensic Research Lab (DFRLab). 2024b. “Doppelganger Websites Persist One Month Following U.S. Government Seizures.” October 9, 2024. https://dfrlab.org/2024/10/09/doppelganger-websites-persist/.

European Digital Media Observatory (EDMO). 2024. “Doppelganger Investigations Bring Russian Propaganda Campaign to a Halt.” November 18, 2024. https://edmo.eu/publications/doppelganger-correctiv-investigations-bring-russian-propaganda-campaign-to-a-halt/.

European External Action Service (EEAS). 2024. “Doppelganger Strikes Back: Unveiling FIMI Activities Targeting European Parliament Elections.” June 2024. https://euvsdisinfo.eu/doppelganger-strikes-back-unveiling-fimi-activities-targeting-european-parliament-elections/.

EU DisinfoLab and Qurium. 2022. Doppelganger: Media Clones Serving Russian Propaganda. September 27, 2022. https://nsarchive.gwu.edu/sites/default/files/documents/semon9-giki0/2022-09-27-EUDisinfoLab-Qurium-Doppelganger.pdf.

European Centre for Press and Media Freedom (ECPMF). 2024. “Actions Must Be Taken to Address Mass Pro-Russian Spoofing of Legitimate Media Outlets.” September 30, 2024. https://www.ecpmf.eu/actions-must-be-taken-to-address-mass-pro-russian-spoofing-of-legitimate-media-outlets/.

Lawfare. 2024. “Making Sense of the Doppelganger Disinformation Operation.” October 16, 2024. https://www.lawfaremedia.org/article/lawfare-daily--making-sense-of-the-doppelganger-disinformation-operation--with-thomas-rid.

Rid, Thomas. 2024. “The Lies Russia Tells Itself.” Foreign Affairs, September 30, 2024. https://www.foreignaffairs.com/united-states/lies-russia-tells-itself.

STRATCOM COE. 2024. The Doppelganger Case: Assessment of Platform Regulation on the EU Disinformation Environment. https://stratcomcoe.org/publications/the-doppelganger-case-assessment-of-platform-regulation-on-the-eu-disinformation-environment/304.

U.S. Cyber Command. 2024. “Russian Disinformation Campaign ‘DoppelGänger’ Unmasked.” September 3, 2024. https://www.cybercom.mil/Media/News/Article/3895345/russian-disinformation-campaign-doppelgnger-unmasked-a-web-of-deception/.

U.S. Department of Justice. 2024. “Justice Department Disrupts Covert Russian Government-Sponsored Foreign Malign Influence Operation.” September 4, 2024. https://www.justice.gov/archives/opa/pr/justice-department-disrupts-covert-russian-government-sponsored-foreign-malign-influence.

Learn More
Research
No items found.
Lotus Blossom (G0030) and the Notepad++ Supply-Chain Espionage Campaign

How Lotus Blossom (G0030) compromised the Notepad++ update pipeline in a precision supply-chain espionage campaign targeting high-value organizations.

Executive Summary

In late 2025 and early 2026, a series of independent disclosures by software maintainers, security researchers, and national cyber authorities converged on an unsettling conclusion: for months, the update mechanism of one of the world’s most widely used open-source text editors had been quietly subverted. What initially appeared to be an isolated infrastructure anomaly was ultimately revealed to be a sustained compromise of the Notepad++ update pipeline, stretching back roughly six months. As investigators reconstructed the timeline, tracking unauthorized access to hosting infrastructure, lingering credentials that outlived initial remediation, and selectively altered update responses, a far more deliberate operation came into focus. This report is the product of analysis and parallel reconstruction of all public reporting on Lotus Blossom with additional research by DTI, drawing together technical forensics, victimology, and strategic context to assess both the campaign and the actor behind it.

The evidence points to a quiet, methodical intrusion rather than a blunt supply-chain smash-and-grab. From their foothold inside the update infrastructure, the attackers did not indiscriminately push malicious code to the global Notepad++ user base. Instead, they exercised restraint, selectively diverting update traffic for a narrow set of targets, organizations and individuals whose positions, access, or technical roles made them strategically valuable. Taken together, the operational choices, tooling, and victim profile support attribution, with moderate to high confidence, to the China-aligned espionage actor commonly tracked as Lotus Blossom (G0030) in concurrence with other organizations assessment.

What most clearly distinguishes this campaign is its precision. The malicious updates were tailored, the delivery carefully gated, and the operational noise deliberately kept low. There is no evidence of ransomware, financial theft, destructive activity, or influence operations. That absence is itself a signal. Everything about the intrusion, from the limited number of victims to the patient dwell time, points to an intelligence-gathering mission oriented toward quietly acquiring insight rather than extracting immediate material gain. The inferred objectives align closely with state intelligence priorities, encompassing political decision-making, economic and financial visibility, and access to telecommunications and technical environments.

Viewed in a broader historical context, the Notepad++ compromise represents a clear evolution in Lotus Blossom’s tradecraft. Earlier campaigns relied heavily on spear-phishing and bespoke backdoors delivered directly to victims. Rather than compromising end-user systems through conventional infrastructure attacks, such as opportunistic abuse of widely trusted software updates, the actors shifted the locus of trust toward the developer ecosystem itself. By abusing a legitimate update mechanism relied upon specifically by developers and administrators, they transformed routine maintenance into a covert entry point for high-value access. Yet despite this technical evolution, the strategic logic remains consistent. The campaign reflects continuity in purpose, a sustained focus on regional strategic intelligence, executed with more sophisticated, more subtle, and harder-to-detect methods than in prior iterations.

Actor Overview: Lotus Blossom (G0030)

Lotus Blossom is best understood as one of the more durable and methodical Chinese cyber-espionage clusters, with activity traced by multiple vendors and government-linked research groups back to at least 2009–2010. Over more than a decade of operations, the group has appeared under a shifting set of aliases, reflecting differences in vendor telemetry and analytic frameworks, but those naming inconsistencies mask a striking continuity beneath the surface. Across campaigns separated by years, Lotus Blossom exhibits the same core patterns: recurring malware families, stable operational rhythms, and a highly consistent choice of targets. This continuity is one of the strongest indicators that analysts are observing a single, long-lived espionage program rather than a loose collection of short-term intrusion efforts.

At its core, Lotus Blossom is a mission-driven intelligence actor, not a financially motivated threat group. There is no credible reporting tying the cluster to ransomware, extortion, cryptomining, or large-scale fraud. Instead, its operations consistently prioritize access, visibility, and persistence. In multiple documented campaigns, compromised environments remained under observation for months or even years, with operators carefully enumerating systems, staging data locally, and maintaining footholds through understated persistence mechanisms. The absence of monetization artifacts, such as payment infrastructure, monetization tooling, or public-facing impact, strongly reinforces the assessment that Lotus Blossom’s mandate is intelligence collection rather than profit.

Geographically, the group’s center of gravity has long been Southeast Asia, a region that aligns closely with Chinese strategic, diplomatic, and security interests. Vietnam, the Philippines, Hong Kong, Taiwan, and neighboring states recur repeatedly in public reporting. Over time, however, there is clear evidence of measured expansion beyond this core theater. More recent campaigns, including the Notepad++ supply-chain operation, show activity extending into Central America and Oceania, suggesting either broadened tasking or an adaptive response to evolving intelligence priorities. Importantly, this expansion has not come with a change in tempo or style; the group applies the same low-noise tradecraft regardless of geography.

One of Lotus Blossom’s defining traits is its tolerance for long dwell times and multi-year campaigns. Unlike vulnerability-driven actors that move rapidly from exploitation to exit, Lotus Blossom appears comfortable maintaining access with minimal interaction, sometimes returning to environments long after initial compromise. This patience is reflected in how the group manages infrastructure and malware lifecycles. Tooling is not rapidly discarded after exposure; instead, families are iterated and refined over years, with new variants introduced only when necessary. This approach reduces operational risk and supports sustained intelligence collection.

Operationally, the group shows a strong preference for quiet persistence over disruption. Techniques documented across campaigns emphasize blending in rather than standing out: registry-based persistence, Windows services, DLL sideloading, and the use of legitimate administrative utilities. Command-and-control traffic is frequently disguised as normal web or API activity, and in some cases tunneled through legitimate platforms. This tradecraft minimizes alerts and allows the actor to remain embedded in sensitive networks without triggering incident response thresholds.

A key throughline across Lotus Blossom’s history is its reliance on custom backdoors that evolve but remain recognizably related. Early campaigns made use of backdoors such as Elise, followed by the long-running Sagerunex family, which has been observed in multiple variants since at least 2016 and is widely regarded as uniquely associated with the group. The emergence of Chrysalis in the Notepad++ supply-chain campaign represents the latest iteration of this lineage: a bespoke implant designed for stealth, flexibility, and long-term access. The persistence of these families across years underscores both development continuity and institutional knowledge within the operator set.

Within the broader Chinese APT ecosystem, Lotus Blossom occupies a regional strategic espionage tier. It is less globally expansive than groups such as APT10 or APT41, which have conducted large-scale, worldwide operations against managed service providers, supply chains, and intellectual property targets. At the same time, Lotus Blossom is markedly more disciplined and persistent than opportunistic or vulnerability-driven clusters that surge around new exploits and then fade. Its niche is sustained regional intelligence collection: quieter, narrower in scope, but exceptionally durable. That combination – longevity, patience, and restraint – has made Lotus Blossom one of the more consistently effective, and correspondingly harder to uproot, espionage actors operating in the Chinese cyber landscape.

Historical Operations and Tradecraft Evolution

Lotus Blossom’s operational history can be understood as a gradual but deliberate evolution, marked by clear phases in tooling, targeting, and delivery mechanisms, each building on lessons learned from the last.

In its early era, roughly spanning 2012 to 2015, Lotus Blossom was first brought into clear view through campaigns documented by multiple security vendors. During this period, the group focused heavily on government and military organizations across Southeast Asia, reflecting a tightly scoped intelligence mandate aligned with regional political and defense priorities. Access was typically achieved through spear-phishing, often using carefully crafted, weaponized documents designed to appear relevant to the recipient’s official duties. Once opened, these lures delivered a custom backdoor known as Elise, which gave the operators persistent access to compromised systems. The objectives in this phase were relatively unambiguous: the collection of political and defense intelligence, including insight into policy deliberations, military posture, and regional security relationships. The tradecraft was effective but conventional, relying on social engineering and direct victim interaction to establish initial footholds.

The middle era, from approximately 2016 through 2024, marks a period of consolidation and professionalization. During this time, Lotus Blossom transitioned away from Elise and adopted the Sagerunex backdoor family, which would become a defining element of its operations for nearly a decade. Sagerunex was not simply a replacement implant but a more flexible and durable platform, iterated across multiple variants and tailored for long-term persistence. Alongside this tooling shift, the group expanded its target set. While government entities remained important, campaigns increasingly encompassed telecommunications providers, media organizations, and manufacturing or industrial firms. This broader victimology suggests an intelligence remit that had widened to include information flows, public narratives, supply chains, and industrial capacity.

Technically, this era is notable for Lotus Blossom’s growing reliance on legitimate third-party services as covert command-and-control channels. By tunneling communications through cloud platforms, webmail, and other widely used services, the group was able to blend malicious traffic into normal enterprise activity, significantly reducing detection risk. At the same time, operators invested in improved operational security and persistence mechanisms, favoring low-visibility techniques such as Windows services, registry modifications, and careful privilege management. These choices enabled long dwell times and multi-year access to sensitive environments, reinforcing the group’s reputation for patience and discipline.

The modern era, beginning in 2025 and extending into 2026, represents the most pronounced shift in Lotus Blossom’s tradecraft. In this phase, the group adopted supply-chain compromise as a primary delivery vector, moving upstream to exploit trusted software distribution mechanisms rather than targeting victims directly. This approach dramatically reduced reliance on social engineering and increased the likelihood of execution in privileged, trusted contexts. Central to this period was the development and deployment of Chrysalis, a previously undocumented backdoor that fits within the group’s established lineage but reflects contemporary defensive realities, emphasizing stealth, flexibility, and survivability.

Operational focus in this era also shifted toward developer and administrator tooling, applications and environments used by individuals with elevated privileges and deep visibility into organizational systems. Even when positioned to affect a broad population, Lotus Blossom demonstrated highly selective victim delivery, carefully gating malicious updates to a narrow set of high-value targets. This restraint underscores the intelligence-driven nature of the activity and the group’s continued aversion to unnecessary exposure.

Viewed end to end, the Notepad++ supply-chain campaign stands as the clearest and most mature expression of this evolution. It combines the group’s longstanding strategic focus on regional intelligence with a modern delivery mechanism that exploits trust itself, integrating bespoke tooling, blended command-and-control, and disciplined selectivity into a single, tightly executed operation.

Targeting Patterns Across Lotus Blossom’s Operational History

Across more than a decade of observed activity, Lotus Blossom’s targeting patterns reveal a high degree of consistency in strategic intent, even as the specific sectors and access methods have evolved. Rather than pursuing breadth or opportunistic exploitation, the group has repeatedly demonstrated a preference for narrow, high-value target sets aligned with enduring state intelligence requirements.

Geographically, Lotus Blossom’s center of gravity has remained firmly anchored in Southeast Asia since its earliest documented campaigns. Countries such as Vietnam and the Philippines recur across multiple reporting periods, reflecting their geopolitical relevance, proximity to contested maritime regions, and the importance of regional security dynamics. Over time, the group’s targeting expanded outward in a measured fashion rather than a sudden global surge. East Asian entities, particularly in Hong Kong and Taiwan, appear during periods of heightened political sensitivity, while more recent operations show selective activity in Oceania and Central America. This pattern suggests deliberate tasking tied to evolving diplomatic, security, and economic priorities rather than indiscriminate global reach.

Sectorally, Lotus Blossom’s targeting history shows a clear progression from core state institutions toward broader strategic enablers. In its early years, the group focused heavily on government ministries and military or defense-adjacent organizations, consistent with a mandate centered on political and defense intelligence. As the group matured, it expanded into telecommunications providers, a shift that provided insight into information flows, network dependencies, and potential downstream access. Subsequent targeting of media organizations indicates an interest in narrative awareness and public messaging, while incursions into manufacturing and industrial sectors point to intelligence collection related to supply chains, industrial capacity, and economic resilience.

A notable and recurring theme is Lotus Blossom’s focus on access multipliers, entities or roles that provide visibility beyond their immediate organizational boundaries. Telecommunications operators, IT service providers, and managed service environments appear repeatedly because they offer the potential to observe or pivot into multiple downstream networks. This logic is further reinforced in the group’s most recent campaigns, which emphasize developer and administrator environments. By targeting the tools and systems used by highly privileged technical staff, Lotus Blossom maximizes intelligence yield while minimizing the number of compromises required.

Equally important is what the group does not target. There is little evidence of sustained activity against consumer sectors, retail organizations, or entities primarily associated with direct financial gain. Even when financial institutions appear in victimology, the surrounding indicators point toward financial intelligence and relationship mapping, not theft or fraud. This restraint reinforces the assessment that Lotus Blossom’s targeting is governed by intelligence value rather than monetization potential.

Finally, Lotus Blossom’s targeting is characterized by selectivity and patience. Campaigns routinely involve small numbers of victims, long dwell times, and repeated engagement with the same regions or sectors over many years. The Notepad++ supply-chain campaign exemplifies this approach: despite access to a potentially massive user base, the group limited malicious delivery to a tightly controlled subset of targets. This pattern is consistent with an actor that values sustained insight and low exposure over rapid or dramatic effects.

Taken together, Lotus Blossom’s targeting history reflects a disciplined, intelligence-driven model. Geography, sector, and individual victim selection all serve a coherent strategic purpose, supporting the conclusion that the group functions as a long-term regional intelligence collector rather than a broad-spectrum or opportunistic threat actor.

Campaign Overview: Notepad++ Supply-Chain Compromise

Campaign Overview: Notepad++ Supply-Chain Compromise

The Notepad++ campaign represents a deliberate and technically mature supply-chain operation built around the exploitation of trust, rather than the compromise of software code itself. Instead of tampering with the Notepad++ application or its publicly available source, the attackers targeted third-party hosting infrastructure responsible for distributing software updates. By positioning themselves within this upstream delivery path, they were able to influence what end users received without altering the integrity of the project’s codebase or repositories.

Central to the operation was the abuse of WinGUp (GUP.exe), the legitimate updater mechanism used by Notepad++. Under normal conditions, GUP.exe is responsible for periodically checking for updates and retrieving them from trusted servers. The attackers subverted this process by selectively redirecting update requests from chosen systems to attacker-controlled servers. To the end user, and to most security controls, the process appeared indistinguishable from a routine update transaction.

A critical distinguishing feature of this campaign is that the Notepad++ source code was never modified. This choice conferred several operational advantages. By avoiding source-level tampering, the attackers bypassed source-code reviews, integrity checks, and the scrutiny of the open-source community. The malicious payloads were delivered in the form of trojanized installers, injected only at the point of distribution, allowing the operation to remain invisible to developers and maintainers focused on the code itself.

This approach also enabled a high degree of plausible deniability. Because the compromise occurred within hosting and delivery infrastructure rather than the project’s repositories, attribution was obscured and initial investigations could plausibly attribute anomalies to misconfiguration or transient infrastructure issues. Most importantly, the attackers exercised tight control over victim selection. Update redirection was applied only to specific targets, ensuring that malicious installers were delivered to a narrow, high-value subset of users while the vast majority of the Notepad++ user base continued to receive legitimate updates without incident.

Taken together, these elements reflect a campaign characterized by advanced planning, privileged access, and operational restraint. The selective nature of delivery, the avoidance of unnecessary exposure, and the exploitation of infrastructure trust rather than code vulnerabilities are all hallmarks of Lotus Blossom’s established tradecraft. The Notepad++ supply-chain compromise stands as a clear example of how the group has adapted its methods to modern software ecosystems while remaining true to its long-standing emphasis on stealthy, intelligence-driven operations.

Infection Chains and Malware Deployment

Analysis of the Notepad++ supply-chain incident reveals that the operation was not built around a single, static infection pathway, but rather multiple distinct infection chains deployed over the course of several months. Each chain showed minor variations in tooling, payload composition, and supporting infrastructure, suggesting active management and iteration by the operators. This modularity allowed Lotus Blossom to adapt to changing conditions, rotate infrastructure, and selectively tailor implants to different victims, all while preserving a consistent operational framework.

Despite these variations, the infection chains shared a set of core behavioral elements that define the campaign’s execution. In every observed case, the process began with a legitimate Notepad++ update request, handled by the WinGUp (GUP.exe) updater. For selected targets, this trusted process was subverted to launch a malicious installer delivered from attacker-controlled infrastructure. From the perspective of the operating system and the user, the execution chain appeared routine, inheriting the trust and execution context of a normal software update.

Once execution was achieved, the malware performed initial reconnaissance to situate itself within the environment. Commands such as whoami and tasklist, along with broader system enumeration routines, were used to identify the current user context, running processes, and basic system characteristics. This early situational awareness informed subsequent decisions, including which payloads to deploy and how aggressively to establish persistence.

The next phase involved the staging of artifacts within user application data directories, a deliberate choice that balanced accessibility and stealth. By operating within per-user paths rather than system-wide locations, the malware reduced the likelihood of triggering security controls tied to protected directories, while still maintaining reliable execution and storage. These directories served as temporary holding areas for loaders, configuration files, and auxiliary components.

From this staging environment, the operation progressed to the deployment of a custom loader, responsible for orchestrating the remainder of the infection chain. The loader acted as a pivot point, handling decryption, unpacking, and execution of the final payloads. Depending on the target and the specific chain in use, this culminated in the installation of either Cobalt Strike–based implants or the Chrysalis backdoor. The presence of both options indicates a flexible approach: Cobalt Strike offered a mature, feature-rich post-exploitation framework, while Chrysalis provided a bespoke, lower-profile alternative aligned with Lotus Blossom’s preference for custom tooling.

In all observed chains, post-compromise communications were conducted using encrypted, low-frequency outbound connections over HTTPS. Beaconing intervals were deliberately sparse, and traffic was structured to resemble legitimate web or API interactions, minimizing anomalies in network telemetry. This communications model prioritized stealth and survivability over responsiveness, reinforcing the broader pattern of restraint and long-term access that characterizes Lotus Blossom’s operations.

Collectively, these infection chains demonstrate a disciplined, repeatable deployment model that balances adaptability with consistency. The variations across chains reflect active operational oversight, while the shared elements underscore a well-established playbook optimized for covert, intelligence-driven access rather than rapid exploitation or overt impact.

Tradecraft Observed in the Notepad++ Supply-Chain Operation

In analyzing the Notepad++ supply-chain compromise and correlating it with broader reporting on Lotus Blossom operations, a consistent theme emerges. The group relies on stealthy, evasive techniques that blend malicious activity into normal system behavior rather than overt exploitation that would draw defensive attention. This section explains the key tradecraft elements that enabled the campaign’s success and situates them within broader patterns observed in similar Chinese state-aligned espionage activity.

A foundational aspect of Lotus Blossom’s technique set is the frequent use of living-off-the-land (LOTL) utilities, trusted, legitimate system tools that are co-opted to execute malicious logic under the guise of normal administrative or maintenance tasks. In LOTL attacks, adversaries leverage binaries that are already present on the target system (such as command interpreters or native utilities) to perform reconnaissance, lateral movement, or privilege escalation. Because these tools are part of the standard operating environment, their invocation often escapes traditional signature-based defenses and is not flagged by endpoint security as anomalous behavior. This approach is deliberately evasive, allowing an attacker to achieve foothold and persistence while minimizing the generation of new, suspicious artifacts. (Kiteworks | Your Private Data Network)

Another sophisticated tactic documented in the Notepad++ intrusion was the abuse of DLL sideloading, an established evasion technique that enables malicious code to be loaded by a legitimate host process. In the Notepad++ case, researchers found that a renamed legitimate utility (the Bitdefender Submission Wizard) was used as the initial execution context. A malicious companion DLL, placed in the same directory with the same name expected by the host process, was then loaded in place of the legitimate library. This technique allows the adversary to inject custom payloads without directly executing an executable they control, further blending with normal system activity and reducing the footprint seen by defensive tools. (Security Affairs)

Once executed, many of the campaign’s implants communicated with remote infrastructure using API-style command-and-control (C2) endpoints designed to resemble benign web traffic. These endpoints often expose paths that mimic legitimate update, telemetry, or cloud service APIs rather than raw sockets or obvious HTTP beaconing. By shaping communications in this way and by hosting them behind domains or services that appear innocuous, operators improve the chances that their traffic will traverse restrictive egress filters and escape detection by network intrusion detection systems. This API-like pattern of C2 infrastructure has been observed not only in the Notepad++ campaign but also in prior Lotus Blossom activity where third-party services (e.g., Dropbox, Twitter, or webmail) were repurposed as covert tunnels for beaconing and data exfiltration. (Cisco Talos Blog)

Finally, Lotus Blossom’s infrastructure usage patterns demonstrate rotation and redundancy without abandoning operational grammar. Rather than hard-coding a static set of servers or domains, the group periodically shifts hosting providers, domain names, and IP space while maintaining consistent behavioral identifiers in their implants and C2 protocols. This approach complicates blunt IP-blocklist defenses while preserving the recognizable telemetry that seasoned defenders use to attribute activity over time. The result is an operational posture that is resilient to takedown and resistant to simple detection heuristics, yet still exhibits an identifiable signature across campaigns and years of activity.

Collectively, these tradecraft elements illustrate a highly disciplined adversary that prioritizes stealth, persistence, and low noise. By blending malicious activity into the fabric of normal system and network behavior, Lotus Blossom not only evaded detection during the Notepad++ campaign but also reaffirmed the group’s long-standing preference for covert intelligence collection over disruptive or noisy exploitation.

Victimology and Target Base

The victimology observed in the Notepad++ supply-chain campaign reinforces the assessment that this operation was tightly scoped and intelligence-driven, rather than opportunistic or indiscriminate. Public reporting and forensic analysis identify a small, carefully selected set of confirmed or strongly suspected victims, each of which aligns with Lotus Blossom’s historical targeting logic.

Among the confirmed or observed targets were a government organization in the Philippines, a financial institution in El Salvador, and an IT service provider in Vietnam. In addition, individual technical users were identified in Vietnam, Australia, and El Salvador. While the total number of victims was limited, the diversity of roles and sectors represented is significant. Each victim category provides a disproportionate intelligence return relative to the number of compromises required.

Geographic Pattern

The geographic distribution of victims is coherent and strategically consistent, rather than random. Southeast Asia remains the clear center of gravity, with Vietnam and the Philippines reflecting long-standing intelligence priorities for China. These countries sit at the intersection of contested maritime regions, regional security cooperation, and shifting diplomatic alignments, making them enduring targets for political, military, and economic intelligence collection.

Australia’s appearance in the victim set is also notable. As a Five Eyes intelligence partner, Australia represents a high-value target for insight into allied policy coordination, defense posture, and intelligence sharing frameworks. Even limited access to technical users in this environment can yield significant contextual intelligence.

The inclusion of El Salvador and, more broadly, Central America reflects a more recent but increasingly visible pattern. While not traditionally viewed as a primary cyber-espionage theater, the region functions as a financial and diplomatic intelligence gateway, offering visibility into international financial relationships, development financing, and external influence dynamics. The presence of both a financial institution and individual technical users in this geography suggests deliberate tasking rather than incidental spillover.

Sectoral Focus

Across all geographies, the sectoral composition of victims follows a consistent pattern. Targets cluster around government and policy-relevant institutions, financial systems and intermediaries, and IT and technical service providers. In the most recent phase of operations, particular emphasis is placed on developers and system administrators, individuals whose roles grant them privileged access and broad situational awareness within their organizations.

These targets function as access multipliers. A single compromised developer workstation or IT service provider can expose configuration data, credentials, network topologies, and downstream customer environments. Similarly, access to financial institutions or government agencies provides insight into policy deliberations, economic conditions, and institutional relationships that extend well beyond the compromised endpoint itself.

Taken together, the victimology of the Notepad++ campaign underscores Lotus Blossom’s disciplined targeting philosophy. The group consistently favors high-leverage roles and institutions that maximize intelligence value while minimizing operational exposure. The limited number of victims, combined with their strategic placement across regions and sectors, reinforces the conclusion that this campaign was designed to support sustained intelligence collection rather than broad access or immediate impact.

Why Notepad++?

Notepad++ occupies a uniquely advantageous position within technical environments, which helps explain its selection as a delivery vector in this campaign. The application is ubiquitous among technical users, including developers, system administrators, network engineers, and security analysts. In many organizations, it is installed by default on workstations used for infrastructure management, application development, and operational support. As a result, systems running Notepad++ often belong to users who possess elevated privileges, deep contextual knowledge of internal systems, and routine access to sensitive resources.

From an intelligence perspective, compromising such a tool offers an unusually high return on investment. Technical users commonly rely on Notepad++ to view, edit, and temporarily store credentials, configuration files, and infrastructure details in plaintext. Scripts and automation logic, used to manage servers, networks, cloud resources, and security controls, are frequently authored or reviewed within the editor. Access to these materials can reveal VPN endpoints, cloud service credentials, API keys, internal naming conventions, and operational workflows, providing insight far beyond the confines of a single endpoint. In many cases, these artifacts also reference institutional documentation, internal procedures, and architectural diagrams that are not otherwise externally visible.

Equally important is the trust relationship inherent in the software’s update mechanism. The Notepad++ updater is a routine, background process that users expect to run without intervention or scrutiny. By abusing this mechanism, the attackers were able to inherit the implicit trust that users and operating systems place in legitimate updates. This eliminated the need for phishing emails, malicious attachments, or other forms of overt social engineering that might raise suspicion or trigger defensive controls. The act of updating the software, normally a security-positive behavior, became the point of compromise.

In effect, the update process itself functioned as the lure. Rather than persuading users to take risky actions, the attackers embedded themselves in a workflow that users already regarded as safe and necessary. This inversion of trust reflects a sophisticated understanding of how technical users operate and underscores why Notepad++ was such an effective and strategically chosen access vector for an intelligence-focused campaign.

Political, Economic, Financial, and Espionage Motives

Intelligence Objectives and Strategic Rationale

The intelligence objectives underlying the Notepad++ supply-chain campaign align closely with long-standing state priorities, particularly in the political, economic, and strategic domains. The operation’s design and execution suggest a deliberate effort to build situational awareness rather than to achieve immediate operational effects.

Political Intelligence.

A central aim of the campaign appears to be sustained monitoring of government policy direction in Southeast Asia, a region where diplomatic alignment, security cooperation, and maritime disputes remain fluid. Access to technical users and institutions in this environment provides insight into policy deliberations, inter-agency coordination, and shifts in national posture that may not be visible through public channels. Closely related is the collection of intelligence on defense cooperation and maritime strategy, including how regional governments coordinate with one another and with external partners. The presence of targets linked to allied ecosystems further suggests an interest in alignment with U.S. and partner positions, offering indirect visibility into broader coalition dynamics and strategic intent.

Economic and Industrial Intelligence.

Beyond politics and defense, the campaign reflects a clear focus on economic and industrial intelligence. Access to financial institutions and technical service providers enables visibility into indicators of economic stability, capital flows, and institutional dependencies. Similarly, targeting entities involved in infrastructure and technology operations supports an understanding of modernization efforts, procurement cycles, and industrial capacity. Monitoring regulatory discussions and trade posture, often embedded in internal documentation, draft policies, and technical planning materials provides advanced awareness of economic decisions that can shape regional competitiveness and resilience.

Financial Intelligence (Non-Theft).

Notably, while financial institutions appear in the victim set, there is no evidence of fraud, theft, or fund diversion associated with this campaign. Instead, the activity is consistent with financial intelligence collection: mapping relationships between institutions, understanding transaction flows at a structural level, and identifying dependencies within national and regional financial systems. This distinction is important. The absence of monetization artifacts reinforces the assessment that the objective was insight, not profit, and places the activity firmly in the realm of state intelligence rather than cybercrime.

Strategic Espionage Doctrine.

Taken together, these objectives reflect a strategic espionage doctrine that prioritizes access over action, patience over disruption, and information dominance without escalation. The campaign was designed to quietly position the operator for long-term understanding, not to coerce, signal, or destabilize. By avoiding destructive activity and limiting exposure, the operation preserved freedom of action while minimizing diplomatic or political risk. In this sense, the Notepad++ supply-chain compromise represents intelligence preparation, laying the groundwork for informed decision-making rather than attempting to shape outcomes directly through cyber means.

Attribution Assessment

The totality of available evidence supports attribution of the Notepad++ supply-chain campaign, with moderate to high confidence, to Chinese actors, and specifically Lotus Blossom (G0030). This assessment is not based on any single indicator, but on the convergence of multiple independent factors that, taken together, form a coherent and internally consistent attribution picture.

First, the campaign aligns closely with Lotus Blossom’s longstanding geographic focus on Southeast Asia. Vietnam and the Philippines, both represented among confirmed or observed victims, have appeared repeatedly in the group’s historical operations over more than a decade. This persistent regional focus distinguishes Lotus Blossom from more globally oriented Chinese APT clusters and reinforces continuity with prior tasking rather than a one-off expansion by a different actor.

Second, the tooling lineage observed in this campaign is consistent with Lotus Blossom’s established development patterns. The deployment of Chrysalis, a previously undocumented backdoor, fits cleanly within the group’s historical reliance on bespoke implants such as Elise and Sagerunex. The design philosophy, custom code, low noise, and flexibility for long-term access, mirrors earlier Lotus Blossom tooling rather than the commodity frameworks or mixed criminal–espionage toolsets associated with other Chinese clusters.

Third, the selective nature of targeting and low infection counts strongly support this attribution. Despite access to an update mechanism capable of affecting a massive global user base, the attackers constrained delivery to a narrow set of high-value victims. This restraint is characteristic of Lotus Blossom’s operational model, which consistently favors precision and intelligence yield over scale. It contrasts sharply with campaigns conducted by other Chinese APTs that have demonstrated a willingness to pursue broad, high-volume access when aligned with their objectives.

Fourth, the campaign demonstrates mature operational security and infrastructure discipline. The use of infrastructure-level compromise, API-style command-and-control endpoints, low-frequency encrypted communications, and careful infrastructure rotation without abandoning recognizable campaign grammar reflects a level of planning and tradecraft that Lotus Blossom has exhibited repeatedly in past operations. These elements point to an actor experienced in sustaining access over long periods while minimizing detection and attribution risk.

Finally, the victimology aligns closely with historical Lotus Blossom target sets. Government entities, financial institutions used for intelligence rather than theft, IT service providers, and privileged technical users all fall squarely within the group’s established targeting preferences. This continuity in “who” is targeted is as significant as the technical “how,” reinforcing the conclusion that the campaign represents an evolution of an existing program rather than the work of a different group adopting similar techniques.

When weighed collectively, these factors form a strong attribution case. While other Chinese APT clusters share individual characteristics, such as supply-chain access, custom tooling, or regional interest, no other known group fits the full combination of geography, tradecraft, restraint, tooling lineage, and victimology as closely as Lotus Blossom.

Defensive and Strategic Implications

The Notepad++ supply-chain compromise carries implications that extend well beyond this single incident, both for network defenders and for policymakers concerned with national and economic security. The campaign highlights structural weaknesses in how trust is established, maintained, and defended in modern software ecosystems.

Implications for Defenders.

First, the operation underscores that open-source software is not inherently low risk. Transparency of code does not automatically translate into security when the distribution and update mechanisms sit outside the codebase itself. In this case, the source remained intact while the delivery path was subverted, demonstrating that trust can be undermined upstream of any code review or integrity check. Defenders should treat open-source tools with the same rigor applied to proprietary software, particularly where update mechanisms rely on third-party infrastructure.

Second, the campaign highlights updating infrastructure as a critical attack surface. Software updaters are privileged by design, frequently allowed through endpoint and network controls, and trusted to execute code without user scrutiny. When compromised, they provide an attacker with a reliable and stealthy execution path. Securing update pipelines through stronger integrity validation, monitoring of anomalous update behavior, and defense-in-depth around hosting and distribution, is therefore as important as securing the software itself.

Third, the targeting logic reinforces that developer and administrator workstations are among the highest-value espionage targets in modern environments. These systems often aggregate credentials, scripts, configuration files, and architectural knowledge that can expose entire networks or multiple downstream organizations. Traditional security models that focus primarily on servers or perimeter assets risk overlooking these high-leverage endpoints.

Finally, the campaign demonstrates the limits of indicator-driven defense. Behavioral detection and contextual analysis are more reliable than static IOCs against a patient, low-noise adversary. Infrastructure rotation, bespoke tooling, and selective targeting render simple blocklists and hash-based detection insufficient. Defenders are better served by focusing on anomalous process chains, unexpected updater behavior, unusual DLL loading patterns, and deviations in network communication profiles such as perimeter DNS or packet inspection, even when individual indicators appear benign in isolation.

Implications for Policy and National Security.

At a strategic level, the campaign illustrates that supply-chain compromise has become a primary vector for state-level espionage. As articulated in the work of Jian Tan on software supply-chain trust, capable actors increasingly avoid the cost of penetrating targets individually and instead position themselves inside trusted ecosystems that provide scalable, repeatable access to high-value users. This shift complicates deterrence and response, as such compromises can persist undetected for extended periods and propagate across multiple sectors simultaneously through a single poisoned trust relationship.

The victimology also highlights that smaller and mid-sized states are frequently targeted as intelligence gateways. Access to institutions in these countries can yield disproportionate insight into regional dynamics, allied relationships, and international financial or diplomatic flows. This reality challenges assumptions that only major powers or headline geopolitical rivals face sustained cyber-espionage pressure.

Finally, the incident reinforces that trust relationships within software ecosystems are now contested terrain. Developers, maintainers, hosting providers, and users all participate in chains of trust that adversaries actively seek to exploit. Protecting these ecosystems is both a technical and strategic challenge, requiring coordination between private industry, open-source communities, and governments to harden shared infrastructure without undermining the openness and collaboration that make these ecosystems valuable in the first place.

Taken together, these implications point to a future in which defending against espionage is less about patching individual vulnerabilities and more about protecting trust itself, in software, in infrastructure, and in the relationships that bind modern digital systems together.

Outlook and Forward Assessment

Looking ahead, the patterns observed in the Notepad++ campaign and in Lotus Blossom’s historical operations provide a useful basis for anticipating how this actor is likely to operate in the near to medium term. The group’s evolution has been incremental rather than abrupt, suggesting continuity of mission and tradecraft rather than experimentation for its own sake.

Likely Future Activity

Lotus Blossom is likely to continue targeting developer and administrator tooling, particularly applications and platforms that are widely deployed in technical environments and implicitly trusted by their users. These tools offer consistent access to privileged contexts and aggregate high-value information such as credentials, automation scripts, configuration data, and architectural documentation. As long as developers and administrators remain central to modern infrastructure operations, they will remain attractive espionage targets.

Geographically, future activity is expected to involve measured expansion into adjacent regions with strategic relevance, rather than a dramatic shift toward global saturation. Southeast Asia will almost certainly remain the core theater, but selective operations in regions that function as diplomatic, economic, or intelligence gateways, similar to the activity observed in Oceania and Central America, are likely to continue. Such expansion reflects evolving intelligence requirements rather than a change in operational philosophy.

From a delivery perspective, the group’s demonstrated success with the Notepad++ compromise strongly suggests an ongoing preference for supply-chain and trust-based access. Compromising distribution infrastructure, update mechanisms, or widely trusted platforms reduces reliance on social engineering and increases the likelihood of execution in high-trust environments. This model is efficient, stealthy, and well aligned with Lotus Blossom’s emphasis on low-noise, long-term access.

Warning Indicators

Defenders should be alert to a set of warning indicators that are subtle in isolation but meaningful in combination. Selective update anomalies, where only a small subset of systems receive unexpected update behavior, may indicate upstream manipulation rather than benign error. Similarly, low-volume, API-style beaconing that blends into normal HTTPS traffic can signal command-and-control activity designed to evade traditional network detection.

Another important indicator is the compromise of “boring but trusted” tools: utilities that are widely used, rarely scrutinized, and considered operationally mundane. These applications often sit outside the focus of security monitoring precisely because they are perceived as low risk, making them ideal vehicles for trust exploitation. Finally, defenders should treat long dwell times without overt impact as a potential red flag rather than a sign of benign activity. In Lotus Blossom’s operating model, the absence of disruption is often an intentional feature, not an accident.

Taken together, these indicators point to an adversary that values patience, precision, and invisibility. Future campaigns are likely to look unremarkable at first glance, blending into routine operational noise. Recognizing and responding to them will depend less on spotting dramatic events and more on detecting subtle deviations in how trusted systems behave over time.

Bottom-Line Judgment

The Notepad++ supply-chain campaign is a textbook example of modern Chinese state-aligned cyber-espionage, optimized for discretion, persistence, and strategic intelligence collection.

Lotus Blossom remains one of China’s most quietly effective APTs, less visible than headline actors, but deeply embedded in regional intelligence operations.

Confidence Ratings

  • APT involvement: High
  • Espionage motive: High
  • Lotus Blossom attribution: Moderate–High

Appendix A: Indicators of Compromise (IOCs)

Actor: Lotus Blossom (G0030)
Campaign: Notepad++ Supply-Chain Compromise (2025–2026)

Analyst note: This appendix consolidates publicly reported and analytically derived IOCs associated with Lotus Blossom and the Notepad++ supply-chain campaign. The list is intended for threat hunting and contextual correlation, not as a stand-alone blocklist. The actor demonstrates frequent infrastructure rotation, selective delivery, and low-noise operations; therefore, behavioral correlation remains essential.

A.1 Campaign-Specific Network IOCs (Notepad++ Supply-Chain)

Command-and-Control Domains (observed in reporting):

  • cdncheck[.]it[.]com
  • wiresguard[.]com
  • Skycloudcenter[.]com

cdncheck.it.com

  • This domain has been observed as a command-and-control endpoint used by malicious payloads delivered in the Notepad++ supply-chain campaign; Cobalt Strike Beacons were configured to communicate with it, and attackers used paths like /api/update/v1 and /api/FileUpload/submit for C2 traffic. 
  • It is deployed in multiple infection chains as a C2 domain, not a legitimate service; security analysts note its inclusion in IoCs tied to the Notepad++ compromise. 
  • There is no public indication that cdncheck.it.com is associated with any legitimate “cdncheck” project such as the ProjectDiscovery tool named cdncheck (which is an open-source asset scanning tool). The similarity in names appears coincidental. 

Summary: Used as attacker-controlled infrastructure; no publicly known legitimate service.

2. wiresguard.com (referred to in C2 contexts)

  • The domain api.wiresguard.com appears in Notepad++ campaign IoCs collected by security researchers—Beacons and API paths like /update/v1, /api/FileUpload/submit, and /api/getInfo/v1 were observed being used by Cobalt Strike implants and other payloads. 
  • There is no evidence from public OSINT that the domain is linked to the WireGuard VPN project (the legitimate technology is spelled WireGuard). It is widely assessed to be an attacker-controlled domain imitating a plausible service name to blend into developer traffic. 
  • Analysts treat this domain as part of malicious infrastructure rather than a trusted service provider.

Summary: Likely malicious C2 domain mimicking a benign service name; no legitimate affiliation found in open OSINT.

3. skycloudcenter.com

  • The subdomain api.skycloudcenter.com is identified in multiple IoC lists from incident analysis—it appears in URLs such as /a/chat/s/{GUID} used by the Chrysalis backdoor for encrypted communications. 
  • Reporting notes that this domain is part of the API-style command-and-control infrastructure rather than a known cloud provider or mainstream SaaS platform. 
  • There is no clear legitimate service tied to this domain in publicly indexed OSINT; its naming seems intended to resemble a cloud service but lacks authoritative footprint (no major product, published service, or corporate identity in searchable records).

Summary: Appears exclusively as attacker infrastructure used for backdoor C2; no confirmed legitimate service.

Associated IP Addresses (observed during campaign window):

  • 45.77.31[.]210
  • 59.110.7[.]32:8880
  • 124.222.137[.]114:9999

45.77.31[.]210 (HTTPS)

Role in campaign: Hosted second-stage Cobalt Strike Beacon shellcode and exposed API-style C2 endpoints used by the Beacon (GET/POST patterns). (Securelist)

Observed paths / IOCs:

  • https://45.77.31[.]210/users/admin (Beacon shellcode retrieval) (Securelist)
  • https://45.77.31[.]210/api/update/v1 (Beacon HTTP-GET) (Securelist)
  • https://45.77.31[.]210/api/FileUpload/submit (Beacon HTTP-POST) (Securelist)

Hosting / network ownership (OSINT):

  • rDNS indicates Vultr-hosted infrastructure (e.g., 45.77.31.210.vultrusercontent.com). (IPinfo)
  • Vultr’s public ASN is AS20473 (The Constant Company). (IPinfo)

Analytic note: Securelist describes a later shift where the same “grammar” (paths, updater chain) persists while delivery/C2 pivots toward domains (e.g., cdncheck[.]it[.]com)—classic “rotate infra, keep protocol shape” tradecraft. (Securelist)

59.110.7[.]32:8880 (HTTP)

Role in campaign: Hosted a Cobalt Strike Beacon and implemented API-like endpoints for GET/POST comms (directly referenced as part of the Notepad++ supply-chain operation telemetry set). (Securelist)

Observed paths / IOCs:

  • http://59.110.7[.]32:8880/uffhxpSy (Beacon staging/hosting URL) (Rapid7)
  • http://59.110.7[.]32:8880/api/getBasicInfo/v1 (Beacon HTTP-GET) (Rapid7)
  • http://59.110.7[.]32:8880/api/Metadata/submit (Beacon HTTP-POST) (Rapid7)

Hosting / network ownership (OSINT):

  • Mapped to AS37963 (Hangzhou Alibaba Advertising Co., Ltd.), i.e., Alibaba Cloud–linked hosting. (IPinfo)
  • Also appears in Abuse.ch ecosystem tracking as malicious-host infrastructure (additional corroboration signal, not attribution by itself). (urlhaus.abuse.ch)

124.222.137[.]114:9999 (HTTP)

Role in campaign: Hosted a Cobalt Strike Beacon with the same “API façade” pattern (update/status/info submission), and is listed by both Securelist (campaign IOC list) and Rapid7 (Chrysalis/related tooling context). (Securelist)

Observed paths / IOCs:

  • http://124.222.137[.]114:9999/3yZR31VK (Beacon staging/hosting URL) (Rapid7)
  • http://124.222.137[.]114:9999/api/updateStatus/v1 (Beacon HTTP-GET) (Rapid7)
  • http://124.222.137[.]114:9999/api/Info/submit (Beacon HTTP-POST) (Rapid7)

Hosting / network ownership (OSINT):

  • The 124.222.137.0/24 netblock is shown as AS45090 (Shenzhen Tencent Computer Systems Company Limited), i.e., Tencent Cloud–linked hosting. (IPinfo)

What these three IPs imply (campaign-level assessment)

  • All three are consistent with the campaign’s low-volume, high-control delivery model: they’re not mass-distribution nodes; they’re purpose-built staging/C2 with “benign enterprise API” URL shapes (/api/*/v1, /submit, etc.). (Securelist)
  • The hosting mix (Vultr + Alibaba Cloud + Tencent Cloud) is consistent with infrastructure agility and cost-effective rotation without changing the operational “grammar” (paths, beacon profile style). (Securelist)

C2 Characteristics:

  • HTTPS-based communication
  • API-style URI paths (update/telemetry-like)
  • Low-frequency beaconing
  • Small request/response payloads
  • Consistent URI grammar across rotating domains

Appendix A.2 (Expanded): Malware & Tooling IOCs

Actor: Lotus Blossom (G0030)
Campaign: Notepad++ Supply-Chain Compromise (2025–2026)

A.2.1 Custom Backdoors / Implants

1. Chrysalis – Custom Backdoor (Campaign-specific)

  • Description: A previously undocumented custom backdoor deployed via malicious Notepad++ updates. It’s feature-rich, implements structured C2, and uses advanced loader obfuscation and API hashing techniques. It was delivered after DLL sideloading via renamed Bitdefender binaries and NSIS installer abuse.
  • Observed in: Multiple security reports on the Notepad++ supply-chain compromise confirm Chrysalis as the primary bespoke implant in the most recent execution chain. Chrysalis replaces or augments Cobalt Strike payloads in some infection conduits. (Rapid7)
Sample Name or Artifact Type Observed Role Notes / Citation
update.exe NSIS installer Initial dropper for backdoor Rapid7 analysis discussed multiple NSIS bundles delivering Chrysalis components. (Rapid7)
BluetoothService.exe Legitimate loader renamed DLL sideload host Rapid7 cites the renamed Bitdefender utility abused for sideloading log.dll. (Help Net Security)
log.dll Loader DLL Decrypts/executes the backdoor Rapid7 notes that log.dll loads and decrypts Chrysalis. (Help Net Security)

Sample Hash Indicators:
(These are candidate hashes observed in threat-hunting discussions associated with Chrysalis–type activity; use with contextual correlation)

Notes: Chrysalis is associated with multi-stage loading and encrypted communications and is explicitly tied to the Notepad++ compromise in Rapid7 technical analysis. (Rapid7)

2. Sagerunex – Historical Lotus Blossom Backdoor Family

  • Description: A long-standing backdoor family consistently linked with Lotus Blossom operations in Southeast Asia prior to the Notepad++ incident. Sagerunex appears in multiple variants over years and is part of the group’s standard espionage toolkit. (Picus Security)
  • Behavior: Often installed as a Windows service or registry persistence component; connects to C2 via encrypted or tunneled channels; used for long-term access and data exfiltration. (Picus Security)

Sample Hash Indicators:
(Historical Sagerunex variants are well documented in vendor telemetry but specific public hashes for this campaign have not been widely published. The below hashes are examples drawn from public threat intelligence discussions tied to earlier variants.)

Notes: Sagerunex’s variants may not be directly linked to the Notepad++ campaign but represent the broader Lotus Blossom backdoor lineage. (Picus Security)

3. Elise – Early Custom Backdoor (Historic, Pre-Campaign)

  • Description: An older custom backdoor associated with early Lotus Blossom campaigns (circa 2012–2015), widely referenced in historic vendor analysis. (Picus Security)
  • Behavior: Provided persistence and remote access, often delivered via spear-phishing lures targeting government and defense institutions.

Public Hashes:
There are no widely published hashes specifically tied to Elise in the context of the Notepad++ campaign. Historical Elise variants appear in older vendor IOC sets but are not directly cited in current Notepad++ analyses.

Notes: Elise remains part of the Lotus Blossom malware ecosystem but is not directly observed in the Notepad++ supply-chain campaign in available public reporting. (Picus Security)

A.2.2 Ancillary / Supporting Artifacts

Loaders / Execution Components Observed:

  • NSIS installer artifacts (e.g., update.exe) — utilized to bootstrap malicious payload delivery. (Rapid7)
  • Renamed legitimate utilities (e.g., Bitdefender Submission Wizard / BluetoothService.exe) — used for DLL sideloading of malicious components. (Help Net Security)

Note on Hash Interpretation:
Several hashes circulating in public hunting forums are included above for Chrysalis and Sagerunex, but these should be used only in conjunction with behavioral and contextual evidence (e.g., execution lineage, process ancestry, file paths, registry persistence) due to the non-global nature of the Notepad++ campaign.

A.2.3 Confidence Levels

Malware/Tool Campaign-Relevant Hash Availability
Chrysalis Backdoor High Partial public hunting hashes available
Sagerunex Backdoor Moderate (historical) Yes (historical lists)
Elise Backdoor Low (historical) Limited public hashes

Post-Exploitation Frameworks:

  • Cobalt Strike–based implants (selective deployment)

Execution & Loading Techniques:

  • DLL sideloading via legitimate executables
  • Custom loaders responsible for decrypting/unpacking final payloads

A.3 File System Artifacts

Observed / Common Staging Locations:

  • %APPDATA%\ProShow\load
  • %APPDATA%\Adobe\Scripts\alien.ini
  • %APPDATA%\Bluetooth\BluetoothService\

General Patterns:

  • Use of user-writable directories
  • Non-descriptive filenames
  • Configuration files masquerading as benign application data
  • Loader and payload separation

A.4 Process & Execution Indicators

Suspicious Parent/Child Relationships:

  • GUP.exe (WinGUp updater) spawning non-standard installer binaries
  • Legitimate signed executables loading unsigned or anomalous DLLs

Reconnaissance Commands Observed:

  • whoami
  • tasklist
  • System and environment enumeration commands
  • Network configuration discovery

A.5 Persistence Indicators

Persistence Techniques (Observed Historically):

  • Windows services created for backdoor execution
  • Registry modification for auto-start
  • DLL search-order hijacking
  • Loader-based persistence chained from user context

A.6 Infrastructure & Operational Patterns (Campaign Grammar)

These are higher-order IOCs useful for hunting beyond static indicators:

  • API-like C2 endpoints mimicking update or cloud services
  • Infrastructure rotation without change in URI structure
  • Selective delivery (only a subset of update requests redirected)
  • Long dwell times with no visible disruption
  • Absence of ransomware, cryptomining, or fraud tooling

A.7 Historical Lotus Blossom Targeting Context (Non-Exhaustive)

Geographies Recurrently Associated with Activity:

  • Southeast Asia (Vietnam, Philippines, Indonesia)
  • East Asia (Hong Kong, Taiwan)
  • Oceania (Australia)
  • Central America (El Salvador)

Target Entity Types:

  • Government ministries and agencies
  • Defense-adjacent organizations
  • Telecommunications providers
  • Financial institutions (intelligence, not theft)
  • IT service providers / MSPs
  • Developers and system administrators

A.8 Defensive Guidance for IOC Use

  • Do not rely solely on blocklists. Many IOCs are short-lived.
  • Correlate with behavioral indicators:
    • Unexpected updater behavior
    • DLL sideloading chains
    • API-like HTTPS beaconing
    • Long-term low-noise persistence
  • Treat developer and admin endpoints as high-priority hunt targets.
  • Monitor update infrastructure and third-party hosting dependencies.

A.9 Confidence Statement

The IOCs listed above align with public vendor reporting and multi-source analysis of Lotus Blossom activity. While individual indicators may overlap with other actors or benign infrastructure, the combined presence of these IOCs with Lotus Blossom tradecraft patterns provides a strong basis for attribution and threat-hunting.

Appendix B: Sources and Citations

This appendix consolidates all primary reporting, technical analyses, and authoritative reference material used to support the assessments, attribution, and narrative in this report. Sources are grouped by function (technical analysis, media reporting, and reference frameworks) to allow readers to distinguish between direct forensic evidence, journalistic corroboration, and contextual intelligence baselines.

B.1 Primary Technical Analysis and Vendor Research

These sources form the core evidentiary basis for the campaign analysis, infection chains, victimology, and tradecraft assessment.

  1. Kaspersky Securelist
    “Notepad++ supply-chain attack”
    Comprehensive technical analysis detailing infection chains, infrastructure abuse, victim categories, and malware behavior.
    https://securelist.com/notepad-supply-chain-attack/118708/
  2. Kaspersky Press and Research Materials
    Supplemental summaries and clarifications derived from Securelist reporting and telemetry.
    https://www.kaspersky.com/about/press-releases
  3. MITRE ATT&CK – Lotus Blossom (G0030)
    Authoritative reference for historical tooling, targeting patterns, and known techniques associated with Lotus Blossom.
    https://attack.mitre.org/groups/G0030/

B.2 Media and Independent Reporting

These sources provide external corroboration, contextual framing, and confirmation of selectivity, attribution hypotheses, and geopolitical relevance.

  1. Ars Technica
    “Notepad++ updater was compromised for 6 months in supply-chain attack”
    Reporting on duration, infrastructure compromise, and selective delivery.
    https://arstechnica.com/security/2026/02/notepad-updater-was-compromised-for-6-months-in-supply-chain-attack/
  2. Reuters
    “Popular open-source coding application targeted in Chinese-linked supply-chain attack”
    Independent confirmation of selective targeting, suspected Chinese state linkage, and expert commentary.
    https://www.reuters.com/technology/popular-open-source-coding-application-targeted-chinese-linked-supply-chain-2026-02-02/
  3. Tom’s Hardware
    “Notepad++ update server hijacked in targeted attacks”
    Coverage of update infrastructure compromise and threat actor speculation.
    https://www.tomshardware.com/tech-industry/cyber-security/notepad-update-server-hijacked-in-targeted-attacks
  4. TechRadar Pro
    “Notepad++ hit by suspected Chinese state-sponsored hackers – what we know so far”
    Summary reporting and confirmation of supply-chain vector and victim selectivity.
    https://www.techradar.com/pro/security/notepad-hit-by-suspected-chinese-state-sponsored-hackers-heres-what-we-know-so-far

B.3 Tradecraft, Techniques, and Supporting Intelligence

These sources provide background validation for techniques observed in the campaign and historical Lotus Blossom operations.

  1. Cisco Talos Intelligence
    “Lotus Blossom espionage group”
    Historical overview of Lotus Blossom tooling, C2 behavior, and targeting.
    https://blog.talosintelligence.com/lotus-blossom-espionage-group/
  2. Security Affairs
    Coverage of DLL sideloading, infrastructure compromise, and China-linked APT analysis relevant to the Notepad++ campaign.
    https://securityaffairs.com/
  3. Living-off-the-Land (LOTL) Reference
    Background on LOTL techniques leveraged by advanced threat actors.
    https://www.kiteworks.com/risk-compliance-glossary/living-off-the-land-attacks/

B.4 Attribution and Analytical Confidence Notes

  • Attribution to Lotus Blossom (G0030) is based on multi-factor correlation, including:
    • Geographic and sectoral victimology
    • Custom malware lineage (Elise → Sagerunex → Chrysalis)
    • Operational selectivity and restraint
    • Infrastructure and C2 grammar continuity
  • No single source alone asserts attribution with certainty; confidence derives from convergent analysis across multiple independent sources.

B.5 Citation Handling Notes

  • No specific victim organizations are named in publicly available technical reporting; all victim references are sector- and country-level, consistent with source disclosures.
  • Indicators of Compromise (Appendix A) are drawn from public reporting and are time-bound and perishable.
  • This appendix reflects sources available as of February 2026; subsequent disclosures may refine or expand attribution and victimology.
Learn More
Research
THE KNOWNSEC LEAK: Yet Another Leak of China’s Contractor-Driven Cyber-Espionage Ecosystem

Leaked Knownsec documents reveal China’s cyberespionage ecosystem. Analyze TargetDB, GhostX, and 404 Lab’s role in global reconnaissance and critical infrastructure targeting.

EXECUTIVE SUMMARY

In November of 2025, an allegedly massive leak of data from Chinese company “KnownSec” was posted to a github account. The initial leak was covered by Wired Magazine, and a few other outlets. The leak has since been pulled off of Github and downloaded by very few, and of those few who gained access, only one uploaded 65 documents as a primer to the leak elsewhere for others to see. DTI was able to get the 65 document images and this report is derived from this slice of a much larger leak that is out there but not available.

On December 31 2025, platform and threat intelligence company Resecurity published an excellent analysis of the full leak. As we’ve been working through the 60+ available screenshots from the leak since early November, Resecurity’s post provides additional context in a few areas, especially targeting, that compliment the depth to which we analyzed Knownsec’s technical capabilities.

Ostensibly, KnownSec appeared to be just another security company, but this is only a half truth. In reality, like other reports we have written on Chinese firms, it has a shadow organization that works for the PLA, MSS, and the organs of the Chinese security state. This leak exposes a state-aligned cyber contractor that operates far beyond the role of a typical cybersecurity vendor. Its internal documents, product manuals, and data repositories show a company engineered to support Chinese national security, intelligence, and military objectives. Tools like ZoomEye and the Critical Infrastructure Target Library give China a global reconnaissance system that catalogs millions of foreign IPs, domains, and organizations mapped by sector, geography, and strategic value. Massive datasets containing real names, ID numbers, mobile phones, emails, and credentials allow Knownsec and its government clients to correlate infrastructure with people, enabling rapid deanonymization, targeting, and social engineering.

On top of this data foundation, Knownsec’s offensive products; GhostX, Un-Mail, and Passive Radar purport to provide a full intrusion and surveillance pipeline. GhostX delivers browser exploitation, routing manipulation, credential theft, and endpoint monitoring. Un-Mail enables covert takeover and continuous exfiltration of email accounts across major global providers. Passive Radar ingests PCAP data via local uploads, FTP, or SSH to reconstruct internal network topologies, user communication patterns, and service inventories. These tools work together to support long-term access, DNS hijack, admin takeover, and infrastructure control across foreign government, telecom, financial, and energy networks.

Organizational charts, customer lists, and internal briefings reveal Knownsec’s primary clients as Public Security Bureaus, defense research institutes, and likely the MSS, positioning it within China’s industrialized cyber-operations ecosystem. Its products are marketed directly to law enforcement and military customers, with teams explicitly labeled for “military industry,” “intelligence,” and “public-security support.” The leaked data shows a vertically integrated espionage stack for reconnaissance, exploitation, collection, and persistence, designed for both domestic surveillance and foreign intelligence operations, making Knownsec a central enabler of China’s modern cyber strategy.

Background

Knownsec (知道创宇), headquartered in Beijing, presents itself to the outside world as a familiar figure in the Chinese cybersecurity landscape, a company selling vulnerability assessments, penetration testing, and defensive solutions. It has long been framed as one of the country’s “white-hat” pillars, a firm dedicated to patching security gaps and strengthening networks. But the leaked internal documents, product manuals, work breakdown structure (WBS) project sheets, personnel directories, and vast infrastructure datasets tell a much more complex and far more consequential story. Beneath its public branding, Knownsec operates as an offensive intelligence contractor whose day-to-day work aligns directly with the operational needs of China’s security and military apparatus.

In practice, Knownsec functions within a tight constellation of state-aligned cyber contractors, a network that includes outfits like 404 Lab (internal to Knownsec) , Qi-An-Xin, Venustech, and i-SOON (安洵). These entities form a parallel ecosystem to China’s formal intelligence services, separate on paper, but woven into the broader machinery of state surveillance and cyberespionage. Together, they develop and maintain the tools, datasets, and capabilities required for large-scale identity tracking, offensive reconnaissance, infrastructure enumeration, and targeted intrusion. What sets Knownsec apart within this constellation is the degree of integration seen across its product lines: it does not merely produce one tool or one dataset, but rather an entire operational pipeline spanning discovery, exploitation, reconnaissance, persistence, and human-layer correlation.

The leaked materials reveal that Knownsec maintains some of the most extensive foreign targeting datasets yet seen in a contractor leak, covering Taiwan, Japan, South Korea, India, and multiple Western nations. Its clients include Public Security Bureaus at the provincial and national levels, defense research institutes, and intelligence-adjacent technical units. The company’s organizational charts and internal communications make clear that these relationships are not incidental; they are foundational to Knownsec’s business model and technical direction. In this light, Knownsec emerges not as a private security firm in the Western sense, but as a core node in China’s contractor-driven cyber state, a strategic architecture in which commercial entities serve as the research, development, and operational arms of state cyber power.

ACTOR TAXONOMY

Organizational Structure

Knownsec’s internal architecture per this dump, resembles less a commercial technology company and far more a defense integrator calibrated to state needs. The organizational hierarchy is sharply defined, layered, and optimized for the production of offensive cyber capabilities. Each division has a narrowly tailored mandate that fits into a larger operational machine, an arrangement that mirrors the compartmentalization and task specialization typical of state-sponsored research institutes and weapons contractors.

At the technical core is the 404 Security Lab (404 实验室), a unit responsible for offensive research, exploitation development, and deanonymization, including stewardship of the GhostX tooling family. This is the engine room where browser exploits, network manipulation modules, and deanonymization workflows are built. Surrounding it is the Product Technology R&D Center, which transforms raw offensive ideas into stable, deployable products (most notably Passive Radar), protocol-analysis frameworks, and related reconnaissance systems. Feeding these tools is the Data Business Division, which curates massive datasets, foreign breach archives, and credential repositories, effectively forming the human intelligence layer of Knownsec’s cyber operations. Where state-aligned priorities shift toward military readiness or battlefield cyber support, the Military Products Division (军工) adapts and reconfigures Knownsec’s core technologies – ZoomEye, Radar, GhostX – into militarized variants suitable for defense research institutes and specialized units. Meanwhile, the ZoomEye Team maintains the company’s most publicly recognizable asset: a continuous internet-wide scanning and exposure fingerprinting platform. Once all these tools are built, the Beijing Testing Group ensures they meet stability and operational-readiness requirements before deployment to customers.

This hierarchy fractures into distinct functional strata. At the strategic layer, executive leadership and cost-center directors coordinate funding, long-term planning, and alignment with state-customer requirements. The operational layer, project managers, planners, and supervisors – turns those directives into executable work, assigning tasks across teams and ensuring compliance with delivery timelines. The technical layer comprises exploit developers, reverse engineers, protocol analysts, “radar specialists” (aka those working with the platform dealing with internet scale sensing/detection), and data scientists, the hands-on specialists who build Knownsec’s offensive capabilities. Beneath them, the support layer handles content review, security inspection, documentation, and QA critical roles that ensure continuity and polish across the toolchain.

Viewed holistically, the internal structure mirrors the logic of a Chinese cyber-weapons manufacturer: program management offices overseeing multi-year development tracks; governance systems controlling scope, deliverables, and interdepartmental dependencies; and specialized teams that collaborate, integrate, and refine capabilities in parallel. The result is not a loose assemblage of researchers, but a multi-team, multi-layered production line, where offensive tools move from concept to deployment with the discipline and scale of an industrial operation aligned to national strategic priorities.

Org Structure per leak 2025

Role Characterization

Knownsec’s internal personnel structure forms a tiered hierarchy that resembles the command-and-control model of a state-linked defense contractor rather than a commercial cybersecurity vendor. At the top sits the strategic layer, composed of executive leadership, business-unit heads, and cost-center directors who set long-term priorities, allocate resources, and ensure alignment with the missions of Public Security Bureaus, military research institutes, and other government stakeholders. Their role is not merely administrative; they define the operational direction of Knownsec’s offensive tooling, selecting which capabilities to develop, which foreign networks to map, and which datasets to prioritize for correlation.

Beneath them churns the operational layer, populated by project managers, planners, and supervisors responsible for translating strategic objectives into actionable engineering programs. These individuals oversee WBS tasking, cross-team coordination, and delivery timelines. They determine how GhostX (“GhostX Framework” offensive cyber platform) modules integrate with Un-Mail (email interception tool), how Passive Radar ingests or parses PCAP data, and how TargetDB updates synchronize with ZoomEye (search engine) output. In effect, they are the connective tissue that binds Knownsec’s sprawling toolchain into a coherent, predictable development pipeline.

The technical layer of exploit developers, radar engineers, data analysts, infrastructure specialists is the skilled workforce that turns those plans into operational capabilities. These teams build the browser exploitation chains, protocol-analysis engines, deanonymization classifiers, and dataset-correlation tools that make Knownsec’s products function as integrated intrusion systems. Supporting them is a broad support layer of content reviewers, security inspectors, and test engineers who ensure data quality, operational safety, and readiness for customer deployment. This division of labor reinforces Knownsec’s resemblance to a Chinese cyber defense integrator, featuring programmatic control structures, specialized technical teams, and multi-layer orchestration designed to reliably produce offensive cyber capabilities at scale.

FULL CAPABILITY ANALYSIS

Global Reconnaissance Layer

Knownsec’s offensive operations begin with a global reconnaissance layer, a foundation built on visibility rather than exploitation. At the heart of this layer is ZoomEye, the company’s internet-wide scanning and fingerprinting platform. Externally marketed as a security research tool, ZoomEye in practice functions as a persistent intelligence sensor grid, one capable of mapping the exposed surfaces of entire nations. Unlike Shodan or FOFA, which rely on hybrid community indexing and slower crawl cycles, ZoomEye conducts full IPv4-space scanning, generating a continuously refreshed portrait of devices, services, and vulnerabilities across the global internet.

ZoomEye’s detection capabilities are unusually granular. Its internal documentation highlights a library of 40,000+ component fingerprints, allowing it to identify not just common servers but also specialized firewalls, industrial controllers, VPN concentrators, and software versions critical for exploitation targeting. The platform recrawls its indexed universe every 7–10 days, making its data nearly real-time, a crucial requirement for Chinese security organs that depend on freshness for both censorship enforcement and foreign operations. Every newly exposed port, misconfigured appliance, or unpatched system becomes visible to Knownsec’s analysts before many national CERTs are even aware of the shift.

The true power of ZoomEye emerges in its integration with Knownsec’s TargetDB (关基目标库: Key Target Library), a classified-style infrastructure database that cross-references ZoomEye results with sector, geographic, and organizational metadata. Raw IPs and banners from ZoomEye become tagged entries in a structured intelligence map identifying which systems belong to ministries, power companies, telecom operators, banks, or military units. In this way, ZoomEye doesn’t merely scan the internet; it prioritizes it, funneling raw exposure intelligence directly into China’s national-level targeting workflows.

ZoomEye

A global cyberspace search engine equivalent to Shodan/FOFA but with:

  • Full IPv4-space scanning
  • 40,000+ component fingerprints
  • Rapid recrawl cycles (7–10 days)
  • Cross-integration with TargetDB
Zoom Eye aka “Eye of Zhong Kui” (Zhong Kui is a mythological demon-hunter; the name implies threat detection and purification.)

TargetDB (关基目标库)

Knownsec’s TargetDB (关基目标库) is the analytical backbone of its reconnaissance capability, an immense, curated intelligence repository that transforms raw internet data into a structured map of global critical infrastructure. Far more than a simple asset index, TargetDB resembles a state-run targeting platform: a system designed to catalog, classify, and prioritize foreign networks according to strategic value. The scale alone is staggering. Internal documentation lists 24,241 organizations, 378,942,040 IP addresses, and 3,482,468 domains, all tagged with metadata that places them within specific industries, national sectors, and operational categories. These entries span 26 geographic regions, covering not only China’s immediate neighbors but also major economies and political rivals across Asia, Europe, and the West.

What gives TargetDB its strategic potency is the precision of its annotations. Each organization and network block is mapped to sector designations such as military, military-industrial, government ministries, telecom operators, energy providers, financial institutions, transportation networks, media outlets, and educational institutions. This transforms an anonymous IP range into a clearly identified target: a ministry of foreign affairs server in Tokyo, a regional power-grid node in Kaohsiung, a financial-trading gateway in Mumbai, or a satellite uplink belonging to a Korean telecom. The database does not simply list assets; it assigns them meaning, aligning infrastructure with strategic objectives and intelligence requirements.

In practice, TargetDB functions as a foreign-target prioritization engine, allowing Chinese state clients to focus their operations on the most consequential systems. When paired with ZoomEye’s continuous scanning, TargetDB becomes a living intelligence reference that highlights newly exposed systems belonging to sensitive entities. This fusion of raw exposure data with organizational and geopolitical context gives Knownsec and its customers a ready-made blueprint for cyber campaigns identifying who matters, where they are located, and precisely which services are vulnerable at any given moment.

This database is a foreign-target prioritization engine.

The Critical Infrastructure Target Library contains:

  • 24,241 organizations
  • 378,942,040 classified IPs
  • 3,482,468 domains
  • Sector mappings across 26 geographic regions

It annotates:

  • Military units
  • Government ministries
  • Telecom operators
  • Energy companies
  • Financial institutions
  • Media and education networks

Data Lake (o_data_*)

Knownsec’s o_data_ data lake* represents one of the most revealing and troubling components of the entire leak. Beneath the polished surface of its security products lies a sprawling, carefully indexed archive of global breach data, sourced from criminal markets, prior compromises, open leaks, and internal acquisitions. These datasets include LinkedIn collections from Brazil and South Africa, Taiwan Yahoo account dumps, Indian Facebook user sets, and extensive Chinese national datasets ranging from railway passenger manifests to banking records and ID-card tables. Layered atop this are telecom subscriber databases, often containing phone numbers, IMSI/IMEI identifiers, addresses, and account metadata. Each dataset is catalogued with schema details including username, password, id_card, mobile, email, real_name, address, investment_style, and more, making the data lake a high-resolution, global directory of human digital traces.

Within Knownsec’s operational ecosystem, this data lake is not a passive archive; it functions as an identity-correlation engine. When a TargetDB entry identifies an exposed service or a ZoomEye scan reveals a misconfigured endpoint, analysts can pivot into the o_data_* records to uncover the real-world individuals associated with that IP, email, or domain. A VPN endpoint in Osaka becomes a person with a name, mobile number, and password reuse history. A Taiwanese banking server becomes an enumerated list of employees with matching emails, credential pairs, and personal details. These correlations enable credential replay attacks, account takeover attempts, and highly tailored social-engineering operations long before any exploit payload is deployed.

But the most powerful function of the data lake is its role in deanonymization. Modern cyber operations often hinge on identifying the human behind the machine, and the o_data_* archives allow Knownsec and by extension its state customers to strip away anonymity across borders. By linking breached credentials, phone numbers, and identity documents to technical infrastructure, the data lake fuels a range of offensive workflows: spearphishing campaigns, targeted malware delivery, behavioral profiling, and covert influence operations. In effect, the o_data_* collection serves as the human-intelligence layer of Knownsec’s cyber apparatus, turning scattered breach records into a structured intelligence resource that drives foreign espionage, domestic tracking, and precision targeting at scale.

A massive archive of global breach data:

  • LinkedIn Brazil, South Africa
  • Taiwan Yahoo email/password datasets
  • Indian Facebook sets
  • Chinese national ID/railway/banking data
  • Telecom subscriber DBs

Purpose:

  • Correlate human identities
  • Enable credential replay
  • Enable deanonymization
  • Power targeted phishing and social engineering

Access Layer

Knownsec’s Access Layer is embodied most clearly in its flagship offensive toolkit, GhostX, a system designed not merely to breach endpoints but to reduce, reconstruct, and ultimately control digital identity. GhostX operates at the intersection of browser exploitation, network manipulation, and host persistence. It begins with browser fingerprinting, gathering granular details, plugins, fonts, extensions, power telemetry, and rendering quirks to create a durable identity signature that follows a user across VPNs, proxies, and devices. Once a target is profiled, GhostX can be set to escalate into active compromise: extracting browser-stored passwords, siphoning cookies and session tokens, and deploying keylogging modules that capture input in real time. These capabilities allow operators to pivot immediately into email accounts, internal dashboards, or social platforms without requiring traditional exploit chains.

But GhostX’s reach extends well beyond the endpoint. The suite includes tools for internal service identification, mapping what the compromised machine can see inside a network database, ports, admin interfaces, intranet portals, and shared resources. From there, GhostX can manipulate the network environment itself through routing attacks and DNS hijacking, redirecting traffic or impersonating internal systems. The ability to create new admin accounts on routers or internal services turns a momentary foothold into a durable position within the victim’s infrastructure, enabling stealthy lateral movement or long-term monitoring. Operators can also invoke remote command execution, screenshot capture, and webpage cloning, giving GhostX a Swiss-army-knife versatility normally found in high-end, nation-state-grade intrusion platforms.

Central to GhostX’s design is its suite of anti-forensic mechanisms and techniques such as code mixing, behavior shaping, and signatureless execution explicitly described in internal product briefs. These features aim to frustrate defenders, slow incident response, and complicate attribution. When combined, GhostX becomes a multi-vector exploitation and persistence framework, engineered to collapse anonymity, extract access, and maintain covert presence across both user endpoints and network infrastructure. It is a foundational component of Knownsec’s offensive cycle, bridging the gap between reconnaissance and deeper operational penetration.

GhostX   Virtual Identity Reduction & Exploitation Suite

GhostX a multi-vector exploitation and persistence framework.

Capabilities include:

  • Browser fingerprinting
  • Password extraction
  • Cookie and credential theft
  • Keylogging
  • Website cloning
  • Screenshot monitoring
  • Internal service identification
  • Routing manipulation
  • DNS hijacking
  • Admin user creation
  • Command execution
  • Anti-forensics (code mixing, signature evasion)

Un-Mail Webmail Takeover & Persistent Collection

Knownsec’s Un-Mail platform is the company’s dedicated engine for webmail takeover and long-term communications exploitation, effectively turning inboxes into intelligence feeds. Unlike traditional phishing tools or standalone password stealers, Un-Mail is built to compromise webmail ecosystems at the application layer, beginning with XSS-based exploitation of major mail portals. These injection points allow attackers to intercept login sessions, capture live session tokens, or inject malicious scripts directly into a victim’s browser workflow. Once access is established, Un-Mail seamlessly transitions into session hijacking and cookie replay, bypassing MFA or password-change events and ensuring operators maintain continuous entry even as the victim continues to use their account.

The platform’s most powerful capability is its ability to perform IMAP/POP mailbox replication, silently downloading the entire mailbox including archived, deleted, or years-old communications into a local datastore under operator control. This “first sync” is typically followed by ongoing incremental collection, with Un-Mail monitoring for new messages and exfiltrating them in real time. Operators can configure keyword triggers for sensitive terms, automate alerts when certain contacts communicate, and selectively forward or clone messages without user visibility. Internal product slides emphasize full inbox exfiltration and customizable monitoring dashboards, indicating a mature COMINT-oriented architecture rather than a simple webmail attack script.

Un-Mail’s reach is expanded by its cross-provider compatibility, with explicit support for Gmail, Outlook/Hotmail, Yahoo, AOL, and major Chinese providers such as 163, 126, TOM, and Yeah.net. This broad compatibility allows Knownsec and its state clients to conduct communications intelligence collection across national borders, harvesting diplomatic correspondence, corporate strategy emails, and internal government mails for targeting purposes. The result is a tool purpose-built for persistent surveillance, supporting intelligence requirements ranging from domestic monitoring to foreign espionage, further evidence that Knownsec’s operational mission extends deep into offensive state-cyber tradecraft.

Capabilities:

  • XSS exploitation of webmail portals
  • Session hijacking
  • Cookie replay
  • IMAP/POP mailbox replication
  • Full inbox exfiltration
  • Real-time keyword monitoring
  • Cross-provider compatibility (Gmail, Outlook, Yahoo, 163, 126, etc.)

This enables communications intelligence collection (COMINT) across national borders.

Internal Network Discovery

Knownsec’s Passive Radar (无源雷达) is designed for the phase immediately following initial access, when the operational priority shifts from intrusion to comprehension. While tools such as GhostX focus on endpoints and Un-Mail captures communications, Passive Radar illuminates the internal network environment those systems inhabit. Its purpose is not exploitation in isolation, but the reconstruction of the operational terrain inside a compromised organization.

Unlike active scanners that generate detectable traffic, Passive Radar relies exclusively on the ingestion and analysis of packet capture (PCAP) data. This passive approach allows operators to observe a network as it actually behaves, without altering traffic patterns or triggering defensive controls. The system accepts PCAPs through three primary ingestion paths: direct offline uploads, remote retrieval via FTP, and secure acquisition over SSH. These mechanisms allow traffic to be sourced from compromised servers, misconfigured storage systems, network taps, or siphoned repositories without requiring live interaction with the target environment.

Once ingested, Passive Radar automatically extracts and classifies the network’s technical structure. It identifies IP addressing schemes, port usage, protocol signatures, service banners, device types, and traffic flows, assembling these elements into a coherent model of internal communications. By correlating flows over time, the platform reveals which systems communicate persistently, how authentication and directory services are organized, where data is aggregated or forwarded, and which services function as internal chokepoints.

This process exposes high-value internal assets that are often invisible from the perimeter: domain controllers, mail gateways, internal content-management systems, financial platforms, and management interfaces. Behavioral flow analysis highlights trust relationships, reused credentials, and open administrative paths that can be leveraged for lateral movement. Device classification further identifies unmanaged servers, weakly configured firewalls, and embedded or IoT systems that present escalation opportunities.

Through this transformation of raw packet data into structured internal intelligence, Passive Radar provides the situational awareness required to move beyond an initial foothold and toward sustained control of a target network.

Passive Radar (无源雷达)

The strategic significance of Passive Radar lies not merely in what it observes, but in how it collapses uncertainty for offensive operators. By deriving intelligence from real traffic rather than inferred exposure, the platform reveals how a network truly functions under normal conditions. This traffic-derived perspective exposes dependencies, trust boundaries, and operational habits that conventional vulnerability scanning cannot reliably detect.

Viewed through an offensive lens, Passive Radar functions as an internal reconnaissance and targeting system. Its outputs identify viable lateral-movement routes, uncover unencrypted administrative channels, and surface shared authentication paths that enable quiet expansion through a network. Instead of probing for weaknesses, it allows operators to exploit the structure that already exists, reducing noise while increasing precision.

This capability is particularly valuable in state-aligned operations, where persistence, attribution control, and long-term access outweigh speed. Passive Radar turns captured network traffic into operational intelligence that supports methodical expansion, selective exploitation, and planned data extraction. In effect, it converts the interior of a victim network from an opaque risk space into a charted environment suitable for controlled maneuver.

For Knownsec’s government and military customers, Passive Radar serves the same role in cyberspace that reconnaissance and terrain analysis serve in conventional operations. It enables planners to study internal infrastructure, anticipate defensive responses, and design lateral movement and persistence strategies with confidence. In this sense, Passive Radar is not simply a security product, but a foundational intelligence capability that bridges access and dominance within the digital battlespace.

A PCAP-based internal situational awareness tool:

3 ingestion modes:

  • Offline PCAP
  • FTP
  • SSH

Extracts:

  • IPs
  • Ports
  • Protocols
  • Behavioral flows
  • Services
  • Device types

Purpose:

  • Map internal networks
  • Identify critical hosts
  • Reveal lateral-movement opportunities
  • Build operational intelligence for deeper compromise

Persistence & Exfiltration Layer

Knownsec’s Persistence & Exfiltration Layer represents the phase of an operation where intrusion shifts from momentary access to steady, renewable intelligence collection. Once an endpoint or infrastructure node has been compromised through GhostX, Un-Mail, or Passive Radar–assisted lateral movement, Knownsec’s tooling activates a suite of mechanisms designed to keep the operator embedded indefinitely. At the user level, this includes keylogging and clipboard capture, which harvest credentials, sensitive text, and operational behavior with granular precision. These seemingly simple functions become powerful when combined with GhostX’s browser and routing manipulation: every password typed, every copied token, every pasted URL becomes part of the attacker’s internal map of the victim’s digital life.

Beyond user surveillance, Knownsec’s tools enforce persistence by manipulating the environment itself. Forced browsing modules can redirect users to attacker-controlled sites to refresh payloads or harvest updated cookies, while webshell interaction provides a remote backdoor for issuing commands and staging follow-up operations. The ability to perform DNS hijacking ensures long-term redirection and covert traffic interception, allowing Knownsec’s operators or their state clients to control access to internal or external resources without needing continuous endpoint presence. When this is combined with admin account creation on routers or internal network appliances, attackers gain durable infrastructure-level footholds that survive password changes, system updates, and even some forms of incident response.

Communication exfiltration remains a central pillar of Knownsec’s persistence strategy. Through Un-Mail, compromised inboxes can be synchronized via ongoing IMAP replication, creating a live copy of the user’s communications outside the victim network. New messages are silently collected, sensitive terms trigger alerts, and historical archives can be mined for strategic value. When all these elements operate together keystroke capture, environmental manipulation, infrastructure control, and communications replication they form a persistent intelligence foothold. This foothold is not just durable; it is regenerative, enabling long-term espionage, strategic monitoring, and operational leverage across months or even years, well after the initial compromise has been forgotten by the victim.

Includes:

  • Keylogging
  • Clipboard capture
  • Forced browsing
  • Webshell interaction
  • DNS hijack for long-term redirection
  • Admin account creation on routers
  • IMAP-based ongoing mailbox replication

This creates persistent intelligence footholds.

OPSEC & Anti-Forensics

Knownsec’s toolchain incorporates a mature OPSEC and anti-forensics layer, reflecting the needs of an organization that expects its operations to face scrutiny from both corporate defenders and national incident-response teams. Rather than treating stealth as an afterthought, Knownsec designs its offensive tools to actively manipulate the investigative environment, reshaping the forensic trail and degrading the defender’s ability to reconstruct what happened. This begins with proxy chain deployment, allowing operators to route traffic through multilayered, frequently shifting intermediaries that obscure the true origin of commands, payloads, or callback traffic. By automating these routing changes, Knownsec ensures that attribution efforts are diluted across ranges of unrelated IP space.

Beyond network obfuscation, Knownsec incorporates behavior-shaping and code-mixing techniques, which alter how malicious scripts behave on compromised systems. Instead of producing predictable logs or recognizable execution patterns, operations are blended into normal system activity or fragmented across modules that only reveal their true function when combined under specific conditions. These methods frustrate heuristic detection and force analysts to piece together sequences of behavior that appear benign in isolation.

Perhaps most challenging for defenders is the emphasis on signatureless execution and anti-tracing modules, which remove or modify indicators that typically reveal compromise. Malware components are often polymorphic or dynamically assembled, leaving no stable signatures for endpoint security tools to match. Meanwhile, anti-tracing features interfere with monitoring hooks, logging frameworks, and analyst tools, making post-incident reconstruction incomplete or misleading. Together, these OPSEC and anti-forensic capabilities signal that Knownsec’s offensive products are built not only to infiltrate networks but to survive inside them, resisting detection long enough to achieve intelligence objectives and complicating attribution even after an intrusion is discovered.

Capabilities:

  • Proxy chain deployment
  • Behavior obfuscation
  • Code mixing
  • No-signature execution
  • Anti-tracing modules

Designed to degrade defender and investigator visibility.

TRADECRAFT & TTPs

Knownsec’s operational workflow reflects a fully realized, contractor-engineered APT intrusion lifecycle, blending state objectives with commercial development discipline. What emerges from the leak is not a set of disconnected tools, but a coherent tactic-to-technology pipeline, where each stage of intrusion is supported by a purpose-built product or dataset. The tradecraft reads like a synthesis of China’s most capable threat actors APT31, APT41, Mustang Panda yet polished through a corporate engineering lens that emphasizes stability, modularity, and reuse across diverse missions.

The intrusion sequence begins with reconnaissance, powered by ZoomEye’s internet-wide scanning and the TargetDB attribution system, which labels millions of global IPs by organization, sector, and geopolitical relevance. Once a target is identified, Knownsec pivots into its human-layer intelligence using the o_data_* collections: massive breach datasets that reveal who operates which systems, how they authenticate, and which credentials or identities overlap across services. These datasets feed directly into resource development, where credential harvesting, identity correlation, and exploit development (largely through 404 Lab) prepare the ground for an intrusion tailored to the target’s technical and human profile.

Initial access is typically obtained through GhostX’s browser exploitation modules, social-engineering campaigns crafted through breach data, or Un-Mail’s XSS-based webmail compromise. Once inside, Knownsec’s operators transition smoothly into execution, deploying JavaScript payloads, browser implants, or DNS manipulation scripts to deepen footholds. The tooling then shifts into persistence mechanisms creating admin accounts on routers, setting up IMAP mailbox replication, and establishing proxy chains that ensure continued access even as environments shift.

From there, intrusions expand through privilege escalation and discovery, guided by routing manipulation and Passive Radar’s PCAP-derived intelligence to illuminate the structure of internal networks. Defense evasion occurs continuously through code mixing, signatureless execution, and behavioral obfuscation. Credential access is achieved via browser password extraction and keylogging, enabling lateral movement into systems that would otherwise require separate exploitation. As operators explore the victim environment, they perform service fingerprinting, internal command execution, and webshell interaction to propagate their influence.

Finally, intrusion objectives manifest through collection and exfiltration, with Knownsec tools capturing screenshots, siphoning mailboxes, and sending stolen data out via IMAP or DNS-hijacked channels. Command and control remains flexible and resilient, relying on web-based callbacks and multi-hop proxy chains that obscure operational origins. Taken together, this lifecycle reveals a level of integration rarely seen outside state intelligence services: a full-spectrum intrusion pipeline where reconnaissance, exploitation, persistence, and exfiltration are engineered as interoperable modules within a single contractor-driven ecosystem.

The Knownsec pipeline mirrors a modern APT intrusion lifecycle:

This aligns with APT31, APT41, Mustang Panda, but with a commercial-engineering polish.

SUPPLY-CHAIN INTELLIGENCE

Knownsec’s operational footprint is supported by a sophisticated and multilayered supply chain, one that mirrors the procurement logic of government-backed defense contractors rather than private-sector cybersecurity firms. Internal documents show that Knownsec does not restrict its infrastructure to domestic providers; instead, it strategically procures European hosting infrastructure, including services from companies such as EDIS and Impreza. These foreign VPS and storage nodes provide staging grounds for scanning operations, payload delivery, redirection infrastructure, and exfiltration endpoints. Their geographic dispersion reduces attribution risk and increases operational reach, aligning with the needs of state customers who require global coverage and plausible deniability.

Financial organization within Knownsec also reflects a formalized, state-integrated structure. Leaked WBS project sheets reveal clearly defined cost centers, funding lines, and project sponsors, which are exactly the type of internal accounting frameworks used in China’s defense-industrial enterprises. Dedicated budgets exist for offensive R&D, data acquisition, infrastructure hosting, and specialized tools like GhostX and Passive Radar as seen in the excel images from the dump. This financial governance ensures continuity across long-term development cycles and indicates that Knownsec’s offensive tooling is not an ad-hoc initiative but an institutionalized capability sustained by predictable funding streams.

A crucial component of the supply chain is the data acquisition ecosystem. Knownsec’s massive o_data_* archives encompassing foreign breach dumps, credential collections, telecom subscriber databases, and national-ID repositories come from a mix of purchases, criminal-market harvesting, and internal scraping operations. These datasets form the human-intelligence substrate upon which exploitation and social-engineering operations depend. Similarly, Knownsec’s PCAP supply chain relies on compromised machines, operator-controlled servers, or cooperation from state entities to provide raw network captures that feed Passive Radar’s analytical engine. The success of ZoomEye likewise depends on a distributed scanning infrastructure, sustained by supporting nodes, bandwidth, and hardware that Knownsec maintains across multiple jurisdictions.

Taken together, these elements show that Knownsec’s supply chain is not incidental; it is deliberately constructed to serve national offensive cyber objectives. Its infrastructure procurement resembles the logistical patterns of government-funded cyber units; its data ingestion relies on pipelines typical of intelligence services; and its budgeting and work breakdown structures parallel those of state research contractors. Whether through hosting arrangements abroad, civilian data lakes turned into intelligence assets, or long-term PCAP sourcing, Knownsec’s dependencies align closely with Chinese government procurement cycles and strategic priorities, underscoring its role as an embedded component of the PRC’s broader cyber operations ecosystem.

Evidence from internal documents shows:

  • They maintain internal cost centers for offensive tooling.
  • WBS projects show formal funding lines with project sponsors.
  • External datasets are purchased or harvested from criminal markets.
  • Infrastructure procurement mirrors government-funded contractor operations.

Dependencies

  • PCAP supply chain (victim or operator-controlled hosts)
  • ZoomEye sensor infrastructure
  • Data lake ingestion pipelines
  • Chinese-government procurement cycles

GLOBAL TARGETING

Knownsec’s leaked infrastructure data reveals a clear pattern of structured, high-value targeting focused on the critical infrastructure of strategically significant nations. Even in the limited-resolution tables available, the indicators of compromise (IOCs) point to a deliberate and methodical mapping of Taiwan’s financial, telecommunications, and energy sectors. The sample extracted entries illustrate this well: exposed Fortinet firewalls at Nan Shan Life Insurance and Hua Nan Commercial Bank, publicly reachable Sophos XG appliances at Chunghwa Telecom, and a vulnerable Check Point service tied to Taipower, Taiwan’s national energy provider. These enumerated services tagged by IP, port, device type, and application banner function as prevalidated targets, ready for exploitation by GhostX, network-fingerprinting modules, or customized military tooling. Although these samples represent only a fraction of the full dataset, they demonstrate the precision with which Knownsec cataloged foreign infrastructure exposure.

When these IOCs are contextualized within the broader leak, a picture of systematic targeting emerges. Taiwan is disproportionately represented across the leak, with evidence of interest not only in major telecom operators and financial institutions but also in power grid, nuclear-energy, and ISP-level assets. This coverage aligns closely with PRC strategic priorities and suggests an intent to build comprehensive operational knowledge of Taiwan’s connectivity fabric, resilience posture, and critical dependencies. Similar patterns appear in Knownsec’s datasets for Japan, where telecom providers, energy-sector nodes, and major industrial corporations are cataloged; and in South Korea, where financial institutions, telecom networks, and industrial infrastructure feature prominently.

Beyond East Asia, the targeting footprint widens. Knownsec’s o_data_* records include Indian telecom subscriber databases, Facebook identity datasets, and infrastructure ranges associated with Indian ministries. This mirrors Beijing’s intelligence interest in India’s digital ecosystem and supports operations requiring identity correlation or demographic profiling. Meanwhile, portions of the dataset referencing European or Western entities appear more fragmented, but they nonetheless indicate indirect exposure: customer lists and sector-tagged entries suggest an intelligence appetite for global critical infrastructure and multinational corporations, even if not yet operationalized at the same scale as East Asia.

Taken together, these patterns show that Knownsec’s targeting is strategic, multi-regional, and overtly political, aligning with the geopolitical interests of the PRC. The infrastructure data is not random reconnaissance; it is a curated map of cyber terrain that would enable espionage, influence, and potentially pre-positioning for disruptive operations. Each IOC and sector-tagged asset represents not just a point of exposure but a node in an intelligence-gathering architecture designed to give Chinese state clients deep visibility into the operational backbone of foreign nations.

This represents strategic, multi-region, politically aligned targeting.

Internal Data Exposure: Email Addresses, Employee Identities, and Functional Roles

The Knownsec leak provides an unusually clear view into the human architecture of a Chinese cyber-contractor supporting national security, public-security bureaus, telecom regulators, and critical-infrastructure stakeholders. Unlike previous contractor leaks such as i-SOON (Anxun) which focused primarily on tools and client lists, the KnownSec corpus reveals a segment of internal personnel structures, spanning project owners, planners, cost-center sponsors, WBS task leads, and supporting engineers.

This internal data forms a blueprint of how Knownsec organizes and distributes responsibility across its offensive research, cyberspace-mapping, radar-engineering, and data-fusion programs. It offers a rare look at the people behind these capabilities, and exposes the specific functional chains by which projects move from concept to FOC (full operational capability).

Employee Identity Data

The leak contains a complete cross-section of Knownsec personnel across multiple divisions:

  • 404 Security Lab (exploit research, offensive engineering, pentesting)
  • Product Technology R&D Center (platform R&D, cyberspace mapping)
  • Product Technology Department (hardware radar, UI/UX, testing)
  • Product Technology Center 141 (high-level technical governance)
  • Public-Security Research Institute (entity fusion, PSB analytic systems)

A total of 22 named employees appear in the materials, each tied to specific organizational units and assigned responsibilities inside multi-stage research or engineering efforts. These employees represent a spectrum of roles from senior leadership with strategic authority to WBS task owners responsible for tactical implementation details.

This personnel visibility is valuable for understanding:

  • Internal tasking mechanisms
  • Operational structure beneath Knownsec’s capabilities
  • Which individuals enable offensive, defensive, or fusion-support tasks
  • How work is distributed across government-sponsored projects

Where relevant, email addresses and internal accounts allow correlation with procurement records, code repositories, or external infrastructure should those indicators surface elsewhere.

Internal Email Address Patterns

Every email address in the dump uses one of two company formats:

  • @knownsec.com → Headquarters operational accounts
  • @xm.knownsec.com → Xiamen-based R&D and engineering offices

No personal external addresses appear for employees; only official Knownsec accounts are used inside project governance systems.

The following email addresses were recovered from the leak so far:

  • zouxy2@knownsec.com
  • suig@knownsec.com
  • mas@knownsec.com
  • wangcp2@knownsec.com
  • chenc6@knownsec.com
  • hey5@knownsec.com
  • raosh@knownsec.com
  • anyh@knownsec.com
  • liuj13@knownsec.com
  • xuc2@knownsec.com
  • niexy2@knownsec.com
  • chenrl@xm.knownsec.com
  • chenjz@xm.knownsec.com
  • wangll@xm.knownsec.com
  • chenh4@xm.knownsec.com
  • liwc@xm.knownsec.com
  • wangl8@xm.knownsec.com
  • yangwh2@knownsec.com
  • zhanghj@knownsec.com

These addresses correspond directly to organizational positions inside Knownsec’s secure research and engineering divisions. There are no “throwaway” or operational aliases (e.g., Gmail/QQ/ProtonMail), which underscores that these individuals are internal employees, not contractors or external operators.

Functional Role Taxonomy

The personnel records reveal a clear hierarchy divided into strategic, operational, technical, and support layers.

Strategic Layer

These individuals control cost centers, approve research direction, and supervise multi-year programs. They connect Knownsec’s products to state-level requirements.

Key personnel:

  • 李伟辰 (Li Weichen) – Head of Product Technology Center 141

These roles align with PRC state-integration patterns, where strategic decision-makers balance customer obligations with core R&D investment.

Operational Layer

Project managers, planners, and supervisors who translate strategic objectives into executable WBS chains.

Examples:

  • PM and supervisor for 404 Security Research 2023
  • PM/Planner for AW Detection (Project 391)
  • PM/Planner for Hardware Radar 2022 V3
  • PM of 404 Lab Pentest Research
  • Project planners for Cyberspace Mapping (Carrier Platform)

These individuals operationalize multi-team engineering efforts, reflecting the governance model observed in defense integrators.

Technical Layer

Engineers responsible for exploitation, radar algorithms, system optimization, and data fusion.

Representative technical staff:

  • WBS task owner for AW exploit and discovery chain
  • Owner of AW 3.5 system testing
  • Radar v3 implementation
  • Radar optimization and stability
  • Asset-identification system optimization
  • User and functional testing tasks
  • Data-fusion task execution for PSB
  • Lead engineer for network-entity fusion research

This tier performs the core offensive and analytic development that Knownsec markets to PRC state customers.

Support Layer

Personnel performing QA, compliance, test engineering, and administrative approvals.

Notable roles:

  • Beijing Testing Group (unnamed individuals except task owners)
  • Default approver across R&D workflows

These roles ensure Knownsec’s platforms (Radar, Carrier Platform, offensive tooling) meet regulator and PSB deployment conditions.

Organizational Insight Derived from Internal Personnel Records

The internal data paints a clear picture of Knownsec as a multi-division cyber contractor seamlessly embedded within the broader security and intelligence ecosystem of the People’s Republic of China. Its organizational structure, personnel assignments, and project governance models demonstrate a company that is not merely providing commercial cybersecurity services but is directly supporting national cybersecurity mandates, public-security operations, and critical-infrastructure oversight. Every major division within Knownsec aligns with a corresponding state need, creating an operational architecture that mirrors the functions of a state-affiliated defense integrator.

This alignment is particularly visible in how technical departments map to specific government tasking. The 404 Lab serves as the offensive research and exploit-development hub, producing capabilities that directly support public-security bureaus and the national CERT apparatus. Meanwhile, the Product Technology Centers operate as the engineering backbone for large-scale cyberspace-mapping platforms used by telecom regulators such as Ministry of Industry and Information Technology (MIIT) and Critical Infrastructure Intelligence Center (CNNIC). Parallel to these, the Public-Security Research Institute builds data-fusion and analytic systems tailored for police units, reflecting a tight coupling between Knownsec’s internal R&D efforts and the investigative workflows of law-enforcement agencies.

Even the company’s internal email domains reinforce these functional distinctions. Accounts using @xm.knownsec.com cluster around engineering-heavy roles located in Xiamen, supporting platform development, radar systems, and systems integration. In contrast, @knownsec.com addresses are associated with research, data-fusion, offensive tooling oversight, and leadership responsibilities in Beijing. These boundaries reveal an internal trust and specialization model consistent with sensitive state-oriented development work.

Knownsec’s work-breakdown-structure (WBS) governance further shows a degree of engineering discipline typically found in military-industrial contractors. Projects are organized under formal sponsorship, with named approvers, supervisory layers, and sequenced deliverables. Every task has a clearly identified owner, and responsibilities cascade through planners, supervisors, and technical implementers. This hierarchy captures operational accountability at each stage, ensuring that sensitive tooling and large-scale platforms move through development in a controlled, auditable way.

Personnel mapping highlights how deeply the company depends on specialized, interoperable technical units. Offensive engineers in the 404 Lab, radar architects in the Product Technology Department, large-scale mapping engineers in the R&D Center, and data-fusion specialists in the Public-Security Research Institute all operate in defined silos. However, these silos are not isolated; they form a layered production pipeline that transforms exploit research into operational platforms capable of national-scale reconnaissance, targeting, and surveillance. In this way, Knownsec operates not just as a security vendor but as a critical node in China’s state-aligned cyber ecosystem, where human expertise, organizational structure, and strategic intent converge into a cohesive operational capability.

Key observations:

  1. Departments align to state tasking
    • 404 Lab produces exploit and offensive research for PSB and national CERT.
    • Product Tech Centers deliver cyberspace-mapping platforms for telecom regulators (MIIT, CNNIC).
    • Public-Security Research Institute builds fusion systems directly for police units.
  2. Email domains reinforce internal trust boundaries
    • @xm.knownsec.com maps to engineering-heavy functions.
    • @knownsec.com maps to research, fusion, and leadership roles.
  3. WBS governance reveals engineering maturity
    • Workflows mirror military-industrial contractors with formal sponsorship, deliverable tracking, and internal approvals.
    • Each task has a named owner, capturing chains of operational accountability.
  4. Personnel mapping exposes internal specialization
    • Offensive engineering, radar systems, cyberspace mapping, and data fusion are isolated but interoperable teams.
    • These silos reflect a layered pipeline that moves from exploit research to national-scale targeting platforms.

Strategic Significance of the Internal Data Exposure

The personnel information exposed in the Knownsec leak provides an unusually rich foundation for adversarial intelligence analysis. Instead of viewing Knownsec through the limited lens of tools, platforms, or public-facing capabilities, analysts can now reconstruct the company’s true operational architecture by tracing projects, responsibilities, and decision-making authority back to named individuals. This transforms Knownsec from an abstract corporate entity into a map of people, teams, and functions revealing how its internal machinery supports the broader PRC cyber apparatus.

With individual identities tied directly to work-breakdown structures, cost centers, and project leadership roles, analysts can identify exactly who drives offensive research and development. Names connected to GhostX, Radar 2022V3, the Cyberspace Mapping “Carrier Platform,” and data-fusion systems allow a clear understanding of which personnel shape the direction of core offensive and reconnaissance tools. Decision-making chains also emerge: who authors budget proposals, who approves them, who signs off on deliverables, and who assumes technical ownership of the most sensitive tasks. These insights expose how Knownsec manages risk, allocates resources, and governs the development of capabilities that ultimately serve national-level customers.

The data also closes the loop between Knownsec’s internal operations and China’s public-sector clients. Analysts can now link specific individuals to the ministries, state-owned enterprises, and provincial public-security bureaus they support. Whether developing mapping infrastructure for MIIT, vulnerability research for PSB, or reconnaissance tooling for State Grid or the national telecom operators, the personnel lists clarify which engineers and managers are responsible for executing state-directed work. This creates a direct, traceable line from human operators to cyber capabilities used by the PRC government.

Granular operator-level visibility of this kind is almost never present in Chinese contractor leaks. Typical disclosures provide tools, artifacts, or billing records, but rarely full mappings of engineers, planners, cost-center owners, and project supervisors. The Knownsec leak stands apart in that it reveals not only what the company builds, but who builds it, who authorizes it, and who ensures its integration into the state security ecosystem. For analysts, this level of detail offers an unprecedented window into the human and organizational architecture of one of China’s most capable cyber contractors.

State Security and Intelligence Organizations Identified in the Knownsec Leak

The Knownsec leak provides direct insight into the company’s relationship with the national security, cyber-regulation, and public-security ecosystems of the People’s Republic of China. The documents show that Knownsec does not operate as a conventional cybersecurity vendor but instead as a tightly integrated contractor supporting multiple layers of the PRC’s intelligence and public-security infrastructure. The presence of specific ministries, bureaus, CERT bodies, and state-owned enterprises across internal worksheets and customer tables reveals a contractor ecosystem that mirrors the organizational structure of the Chinese cyber state.

The Ministry of Public Security (MPS) emerges as the most prominent stakeholder in Knownsec’s operations. Multiple internal project sheets reference public-security intelligence requirements, entity-fusion deliverables, and policing-oriented research, suggesting that Knownsec’s tools such as Network Entity Data C fusion systems and analytics platforms feed directly into law-enforcement intelligence workflows. The inclusion of the Beijing Municipal Public Security Bureau as a direct customer reinforces that Knownsec supports both national and regional PSB units, providing technical capabilities that underpin investigatory, surveillance, and cyber-intelligence missions. The company’s Public-Security Research Institute acts as an intermediary, developing analytic systems specifically designed for MPS use, including the “30 Institutes” project, which historically links to police intelligence research centers.

Beyond policing, the documents show that Knownsec’s platform technologies align with the needs of China’s cyber governance infrastructure. The MIIT and CNNIC, which oversee network resources, DNS infrastructure, and telecom regulation, appear in customer lists. These associations suggest that Knownsec’s large-scale cyberspace-mapping platforms and radar systems contribute to regulatory visibility across the national network space. Similarly, the presence of CNCERT/CC and CCERT indicates that Knownsec plays a role in the country’s coordinated incident response and vulnerability-management programs. These organizations sit at the intersection of defensive coordination and intelligence-informed cyber situational awareness, and Knownsec’s products appear to support both domains.

Several state-owned enterprises also appear in the dataset, including State Grid, China Mobile, and China Telecom. While not intelligence agencies in name, these entities represent critical-infrastructure and telecommunications networks of high strategic value to Chinese state security. Their appearance in Knownsec’s internal documentation implies that Knownsec provides reconnaissance, mapping, or defensive monitoring capabilities that directly support national requirements for energy grid protection, telecom oversight, and large-scale network exposure assessment. These relationships blur the line between commercial engagement and state-aligned intelligence support, reflecting the dual-use nature of Knownsec’s core platforms.

Taken together, the organizations referenced in the leak form a coherent picture of how Knownsec embeds itself in the state’s cyber and intelligence apparatus. The company’s divisions and product lines align closely with the functional needs of public-security bureaus, national regulators, telecom carriers, and critical infrastructure operators. The network of relationships visible across the documents illustrates a contractor deeply woven into China’s national security architecture. It confirms that Knownsec’s internal operations, research programs, and platform developments are not random or commercially opportunistic but are systematically shaped by the requirements of the PRC’s intelligence and regulatory ecosystem.

Summary: Intelligence / Security Org List

OrganizationTypeRole in DumpMPS – Ministry of Public SecurityNational Police / IntelligencePrimary stakeholder for offensive, data-fusion, and entity analytics systemsBeijing Public Security BureauMunicipal PSBDirect consumer of Knownsec platforms and analysisPublic-Security Research Institute (internal Knownsec)PSB-aligned R&DBuilds fusion tech for PSB intelligence unitsMIITTelecom & Cyber RegulatorOversight for mapping platforms, radar outputsCNNICNational DNS AuthorityDomain-level surveillance & infrastructure mappingCNCERT/CCNational CERTNational-level vulnerability, incident intelCCERTEducation & Research CERTSupporting CERT node“30 Institutes” (PSB Research Institutes)Public-Security Intelligence R&DEntity fusion, data pipelines, analytic systemsState GridStrategic CII targetIncluded for reconnaissance and mappingChina Mobile / China TelecomTelecom carriersInfrastructure mapping and metadata pipelines

APPENDICES

Appendix A  Combined IOC List (Knownsec Leak Corpus)

Indicator of Compromise Summary  Knownsec TargetDB, Radar, and Foreign CI Mapping

Below is the unified IOC dataset extracted from all Knownsec screenshots, TargetDB tables, Radar 2022V3 outputs, and CI-targeting images provided in this project.

High-Confidence IP-Level IOCs (Critical Infrastructure Targets)

(All derived from Knownsec’s internal TargetDB screenshots for Taiwan CII)

country,organization,ip,port,service,device_type,notes

Taiwan,Nan Shan Life Insurance,210.242.194.198,443,httpd,Fortinet FortiGate,Listed as critical asset in CII table

Taiwan,Nan Shan Life Insurance,210.242.194.198,80,httpd,Fortinet FortiGate,Same host over HTTP

Taiwan,Hua Nan Commercial Bank,219.80.43.14,443,httpd,Fortinet FortiGate,Banking-sector firewall target

Taiwan,Hua Nan Commercial Bank,219.80.43.14,80,httpd,Fortinet FortiGate,Appears twice in Knownsec radar slices

Taiwan,Chunghwa Telecom,220.130.186.202,10443,httpd,Sophos XG,Telecom-edge gateway in CII targeting

Taiwan,Chunghwa Telecom,220.130.186.203,10443,httpd,Sophos XG,Sister device to above; separate PoP

Taiwan,Bank of Taiwan,103.21.60.3,8080,httpd,Fortinet FortiGate,Core financial gateway

Taiwan,Taipower,61.65.236.240,18264,httpd,Check Point SVN,Energy-sector firewall; high-value infrastructure

Medium-Confidence IOCs (Region-Expansion & Mapping Targets)

From Knownsec’s internal WBS expansion directives (WBS 7 & 8):

region,ip_range,notes

United States,100000_new_ips,Expansion directive: increase target coverage by 100k IPs

Taiwan,10000_new_ips,Expansion directive: +10k key Taiwan IP segments

YN_region,expansion_flag,New coverage region in platform WBS

MD_region,expansion_flag,New coverage region in platform WBS

WL_region,expansion_flag,New coverage region in platform WBS

ELS_region,expansion_flag,New coverage region in platform WBS

Data-Lake / Credential-Dump Indicators

From the o_data datasets referenced in the Knownsec HDFS export list:

dataset_name,country_or_sector,notes

o_data_taiwanahooemailpwd_tw,Taiwan,Credentials (Yahoo TW email/password dump)

linkedin_brazil,Brazil,LinkedIn identity dataset

linkedin_southafrica_202305,South Africa,LinkedIn identity dataset

o_data_facebookuserinfo_in,India,Facebook identity dump

o_data_telecom_info_india,India,Telecom subscriber dataset

o_data_royalenfield_india,India,Automotive customer dataset

o_data_shopping_order_vietnam,Vietnam,E-commerce customer dataset

o_data_shopping_vip_vietnam,Vietnam,VIP commerce dataset

o_data_insuranceindia_data,India,Insurance records dataset

o_data_sms_active_ru,Russia,SMS/telecom activity dataset

o_data_telderi_ru,Russia,Marketplace dataset

o_data_skolkovo,Russia,Skolkovo-related dataset

o_data_github,Global,GitHub developer dataset for targeting correlation

o_data_telegram_user_info,Global/Regional,Telegram identity dataset

o_data_instagram_temp,Global/Regional,Instagram scraped temp dataset

Organizational Targets & Associates (Based on Internal “典型客户” / TargetDB Sector Lists)

The following organizations appear repeatedly in Knownsec’s internal customer lists, procurement docs, or radar/TargetDB slices. These constitute strategic targeting and cooperation indicators even when no IP/IaaS attributes were provided.

country,organization,type,notes

China,Ministry of Public Security,State Client,Internal security customer consuming Knownsec platforms

China,People’s Bank of China,Financial Regulator,Monitored via PKI-linked infrastructure

China,CFCA (Financial Certification Authority),Financial PKI Infrastructure,High-value crypto/identity target

China,State Grid Corporation of China,Critical Infrastructure,Energy/SCADA mapping

China Mobile,Telecom,Carrier mapping and radar integration

China Telecom,Telecom,Carrier mapping and radar integration

China Education & Research CERT (CCERT),Academic CERT,Emergency-response alignment

China,State Council Procurement Network,Government ops,Procurement and surveillance-aligned workload

China,Beijing Public Security Bureau,Policing/LEO,Multiple contract purchases in ledger

Taiwan,Bank of Taiwan,Financial institution,Direct firewall mapping (See A.1)

Taiwan,Hua Nan Commercial Bank,Financial institution,Direct firewall mapping (See A.1)

Taiwan,Nan Shan Life Insurance,Insurance/Financial,Direct firewall mapping (See A.1)

Taiwan,Chunghwa Telecom,Telecom,Edge infrastructure fingerprinted (See A.1)

Taiwan,Taipower,Energy/Nuclear,Check Point SVN asset identified

India,Telecom Companies,Telecom,Featured in o_data_telecomcompanies_in

India,Ministry-adjacent IP ranges,Government,Identified in mapping directives

Vietnam,Shopping-order and VIP datasets,E-commerce / social profiling,Used for persona correlation

Russia,Skolkovo / Telderi / SMS datasets,Industrial / Social,Used for identity correlation

Global,GitHub developer data,Developer ecosystem,Used for deanonymization & target pivoting

Tooling-Linked IOC Classes (High-Level Indicators)

Where specific domains/servers were not provided, Knownsec tooling reveals classes of IOCs that defenders should track.

category,indicator_type,example,notes

GhostX,Persistence IOCs,admin account creation on routers,Indicates long-term foothold

GhostX,Network Manipulation,DNS hijack configurations,Used for redirect/exfil

Un-Mail,Webmail compromise,XSS injection points on webmail portals,High-risk COMINT vector

Passive Radar,Internal reconnaissance,PCAP ingestion servers,Victim-owned or attacker-controlled

Carrier Platform,Recon infrastructure,ZoomEye-linked scanners,High-volume scanning nodes

Radar 2022V3,Protocol-fingerprinting output,service banners/flows,Used to classify assets for later exploitation

Data-Fusion Projects,Identity correlation,IAM/credential merges,Used by Public-Security clients

These are behavioral indicators, not atomic IOCs, but they are directly tied to Knownsec’s operational tooling.

Appendix B   MITRE ATT&CK Map

Appendix C   Organizational Schema (Text)

State Ministries

  ↓

Knownsec Executive Leadership

  ↓

404 Lab | Product R&D | Military Division | Data Division

  ↓

Project Managers → Engineers → Operators

  ↓

Toolchain Deployment

Appendix D   Master File List from Dump

Here is a consolidated file list of all Knownsec-related uploads in this project, reconstructed from the conversation history, OCR references, system logs, and tool notes.

This includes images, PDFs, spreadsheets, and indexes you uploaded for the Knownsec leak analysis.

MASTER FILE LIST OF UPLOADED FILES (Knownsec Project)

PDFs (Leak Documents & Articles)

Leak Documentation

  • 关基目标库说明文档_V202309.pdf (multiple screenshots provided)
  • 无源雷达–产品文档 (Passive Radar Product Manual) (screenshots extracted)
  • *404安全研究2023 – internal sheets (as images, WBS pages)
  • 网空云测绘-网空航母平台-2022 (Carrier Platform 2022 WBS sheets)
  • 硬件雷达2022V3.0.0.0 主力项目 (Radar Project 2022V3 WBS)
  • 网络实体数据C与融合关键技术研究 (PSRI / “30 Institutes” project sheets)

Spreadsheets & Data Index Files

1. Personnel / Department / Project Indexes

  • master index departments and projects.xlsx
  • master index emails and people.numbers
  • Untitled.xlsx (additional personnel / dept mappings)

2. Internal Project/Deliverable Sheets

(Uploaded via screenshots but constitute distinct files)

  • 404 Lab WBS summary sheets (≈ 10 images)
  • 391 AW Detection Project sheets (≈ 10 images)
  • Carrier Platform WBS sheets (Product Technology R&D) (≈ 10+ images)
  • Radar 2022V3 WBS sheets (Product Tech Dept) (≈ 10+ images)
  • Public-Security Research Institute fusion project sheets (≈ 10 images)

C. Image Files (Screenshots)

Knownsec Internal Documents (numbered 1–64)

1.png

3.png

4.png

5.png

6.png

7.png

8.png

9.png

10.png

11.png

12.png

13.png

14.png

15.png

16.png

17.png

18.png

19.png

20.png

23.png

24.png

25.png

26.png

28.png

29.png

30.png

31.png

32.png

33.png

34.png

35.png

36.png

37.png

38.png

39.png

40.png

41.png

42.png

43.png

44.png

45.png

46.png

47.png

48.png

49.png

50.png

51.png

52.png

53.png

54.png

55.png

56.png

57.png

58.png

59.png

60.png

61.png

62.png

63.png

64.png.

Reconstructed File Descriptions (1–64)

1–11: Public-Security Research Institute (PSRI) – “Network Entity Data C & Fusion Key Tech Research”

These files corresponded to the “30 Institutes” fusion project, showing:

  • PSB-driven data-fusion research
  • Entity correlation pipelines
  • Multi-dataset integration workflows
  • WBS tasking for Zhang Huijie and Yang Guihui
  • Deliverables tied directly to Public Security Bureau (公安三所) requirements

Typical page contents:

File Description
1.png Title page or high-level summary of the Fusion Research Project
3.png WBS structure showing key tasks (data ingestion, entity resolution)
4.png PSB-facing deliverables used in law-enforcement analytics
5.png Cross-dataset linkage models
6–11 Technical diagrams, task ownership tables, and PSRI resource allocations

12–20: 404 Security Research 2023 (404实验室) / AW Detection Project 391

These images included:

  • 404 Lab internal research objectives
  • Vulnerability mining tasks
  • AW (Asset & Weakness) detection research
  • Exploit-related WBS
  • Roles for Ma Shuai, Wang Cuiping, Chen Cheng, He Yan
  • Related pentest research flows

Typical mapping:

File Description
12.png 404 Lab project summary page
13.png 0-Day research pipeline
14.png Emergency vulnerability response tasks
15.png Battle Pigeon (战鸽) support tasks
16–18 AW detection WBS (3.1–3.6), including system testing
19.png Supervisor/approver fields
20.png Overall AW research deliverables list

23–36: Product Technology R&D Center – Cyberspace Mapping Platform (“Carrier Platform 2022”)

These images belonged to the 网空航母平台-2022 project, showing:

  • Region-coverage expansion goals
  • US/Taiwan key IP-range mapping
  • Platform WBS tasks
  • System component diagrams
  • Planning roles for Chen Ruili, Chen Jinzhan, Wang Lili, Chen Hai
  • Cost-center oversight by Li Weichen

Representative:

File Description
23.png Carrier Platform project overview
24.png New coverage region expansion task (WBS 7)
25.png Capacity and optimization goals (US +100k IPs, TW +10k IPs)
26–30 Platform module integration steps
31–33 WBS assignments indicating planners and supervisors
34–36 Region-by-region mapping and industrial coverage tables

37–45: Hardware Radar 2022 V3 (产品技术部)

These files came from the Radar 2022V3 core project, including:

  • Subsystem optimization tasks
  • Feature development (vuln PoC ingestion, configuration checking)
  • UI/UX tasks
  • User testing and functional testing
  • Technical owner mappings for An Yaxuan, Liu Xun, Xu Chao, Nie Xinyu

Mapping:

File Description
37.png Radar 2022V3 master WBS summary
38–40 Exploit intelligence module features (5.5–5.6)
41.png Asset-ID system optimization (5.3)
42–43 Compliance and configuration checking tasks
44–45 User test case creation and functional test reporting

46–54: TargetDB / Critical Infrastructure Target Library

These screens captured the 关基目标库 (Critical Infrastructure Target Library):

  • Sector classifications (military, telecom, energy, finance)
  • IP counts (378,942,040)
  • Regional coverage (26 geographies)
  • Domain and asset listings
  • Example targets: Taiwan banks, power grid, telecoms

Representative:

File Description
46.png TargetDB region overview
47.png Sector-by-sector breakdown
48.png Example foreign target dataset
49–52 Asset tables (IP, port, service, role)
53–54 Classified organization lists and country coverage

55–64: Data Business Division – HDFS o_data Datasets

This batch corresponds to the o_data_* dataset listings you uploaded, including:

  • Indian telecom subscriber DBs
  • Vietnam shopping-order datasets
  • Russia SMS/telecom datasets
  • Taiwan Yahoo credential dumps
  • LinkedIn Brazil / South Africa
  • GitHub user dataset
  • Telegram data sets
File Description
55.png HDFS directory listing summary
56–60 Dataset list pages for India, Russia, Taiwan
61.png LinkedIn and GitHub dataset references
62–63 Telegram user-info dataset pages
64.png Combined o_data index with HDFS paths

Miscellaneous Internal Dataset References (via screenshots)

Not files themselves, but documented inside uploads:

  • o_data_royalenfield_india
  • o_data_rusnod_ru
  • o_data_school_test
  • o_data_shopping_order_vietnam
  • o_data_shopping_vip_vietnam
  • o_data_skolkovo
  • o_data_sms_active_ru
  • o_data_taiwan_uhq
  • o_data_taiwanahooemailpwd_tw
  • o_data_telderi_ru
  • o_data_telecom_info_india
  • o_data_telecomcompanies_in
  • o_data_telegram_data
  • o_data_telegram_user_info
  • o_data_facebookuserinfo_in
  • o_data_github
  • o_data_instagram_temp
  • o_data_insuranceindia_data
  • linkedin_brazil
  • linkedin_southafrica_202305

These were extracted from HDFS paths visible in the screenshots.

Learn More
Research
The APT35 Dump Episode 4: Leaking The Backstage Pass To An Iranian Intelligence Operation

APT35/Charming Kitten's leaked documents expose the financial machinery behind state-sponsored hacking. Learn how bureaucracy, crypto micro-payments, and administrative ledgers sustain Iranian cyber operations and link them to Moses Staff.

Executive Summary

APT35, also known as Charming Kitten, has long occupied an odd niche in the hierarchy of Iranian cyber operations. They’re the loud ones, constantly deploying new credential-harvesting pages dressed in Western university or defense-contractor branding, yet always recycling the same code and lures. For years, analysts dismissed them as a politically motivated collective within the Revolutionary Guard’s orbit, dangerous mainly to journalists and dissidents, but rarely haunting MITRE’s nightmares.

Episode 4, the latest leak, changes that perception. What matters here isn’t the spectacle of intrusion but the machinery behind it. The files dissolve the myth of the hacker into the hum of administration: spreadsheets logging hosting providers and invoice numbers, crypto receipts processed through Cryptomus, and server rentals under a mosaic of false European identities. These aren’t exploits, they’re expense reports. The dump exposes how Iranian cyber units requisition, fund, and maintain infrastructure, revealing the bureaucratic metabolism that turns state intent into executable code.

Post-leak, APT35 failed to clean up after themselves, leaving operational infrastructure, live servers, and even hosting and service passwords accessible for weeks. The supposed guardians of Iran’s cyber doctrine simply walked away from their own compromised backend. This lack of operational hygiene underscores the paradox of Charming Kitten: a bureaucracy mimicking a hacker collective, running espionage operations with clerical precision, yet unable to follow basic OPSEC discipline when their paperwork leaked into the open.

Seen through this lens, APT35 functions as a government department more than a hacker crew. Someone drafts a VPS requisition; another logs the cost in euros; a supervisor approves the line item; and only then does a technician deploy the phishing kit or C2 beacon. It’s the banality of intrusion, the paperwork of digital espionage. Episode 4 strips away the glamour of zero-days and leaves the logistics in plain view: account creation, invoice reconciliation, crypto transaction IDs as bureaucratic stamps of approval. The same apparatus that once managed oil exports now manages data theft and influence operations. Behind every exploit sits a spreadsheet; behind every “state-sponsored attack,” a purchase order; behind every patriotic slogan, an accounts-payable clerk.

The Episode Four Files: 

The leaked spreadsheets form the operational backbone of APT35’s infrastructure management system, a triptych of bureaucracy masquerading as tradecraft. Each file exposes a different layer of the machine: procurement, payment, and deployment. Together they illustrate how a state-sponsored threat actor runs its cyber operations not through shadowy improvisation but through clerical precision. The documents track every rented server, every registered domain, and every euro or satoshi spent, all with internal ticketing numbers and service IDs linking actions across datasets. What emerges is a portrait not of hackers but of administrators, a bureaucracy that treats intrusion as an accounting exercise and espionage as a workflow.

0-SERVICE-Service.csv 

This sheet functions as the operational ledger. It contains roughly 170 populated rows tying domains to registrars and service notes, plus more than 50 distinct ProtonMail identities and over 80 cleartext credential pairs in email:password format. The entries include cadence markers like “3 Months / #2016,” lifecycle notes such as “SSL / no SSL,” and line-item prices in both dollars and euros. Provider references are recurrent and standardized rather than ad-hoc: EDIS (VPS) appears ~20 times, NameSilo (cheap domains) ~14, and Impreza (VPS) ~6, alongside sporadic mentions of Namecheap (domains) and Temok (Domains). The net effect is a normalized procurement sheet for intrusion, domains, tenancies, and accounts queued for operators, meticulous in bookkeeping but sloppy in OPSEC.

0-SERVICE-payment BTC.csv 

This sheet contains financing data that stitches itself into a meticulous ledger of fifty-five entries spanning from October 4, 2023 through December 11, 2024. The totals form tight constellations around approximately $1,225, with average outlays of about $56 or 0.0019 BTC per transaction. At least thirty-two unique Bitcoin addresses populate the Wallet field. Many rows also reference internal service numbers, “#44,” “#70,” and others, that mirror entries in the companion service sheet (“#23,” “#30,” “#103”). Together they form a verifiable connection between request, payment, and activation. A closed accountability loop meant to satisfy internal auditors while preserving outward anonymity. Each crypto receipt aligns neatly with a ticket number and an allocated cost, revealing an ecosystem of deliberate, ledger-bound order beneath the façade of decentralization.

1-NET-Sheet1.csv 

This sheet closes the loop with network addresses: IPs and /29–/30 allocations (e.g., 185.103.130[.]16/30, 185.212.193[.]240/29, 109.230.93[.]128/29, 195.191.44[.]73) with Persian-annotated connectivity and location notes (TD-LTE, service tiers, city markers). These rows correspond to live infrastructure observed in provider dashboards (EDIS, Impreza), matching the same pseudonymous customer identities and service SKUs seen in the invoices. In aggregate, the three files describe an industrial relay: the service sheet assigns and tracks assets, the BTC register funds and reconciles them (with ticket-level joins), and the network sheet manifests them as routable hosts. The tradecraft here isn’t improvisation; it’s administration, an evidentiary chain that converts hierarchy into infrastructure, with the very spreadsheets that ensure renewals also exposing credentials, wallets, and IPs.

The Economics of Access

For Tehran’s cyber apparatus, access isn’t stolen, it’s procured, budgeted, and renewed. Episode 4 makes unmistakably clear that intrusion has an economy. Each Virtual Private Server (VPS) payment, logged in euros, is a link in a chain of Iran’s information operations. All paid through Cryptomus, each transaction is small enough to slip under every compliance radar. There are no lump sums, no visible treasury trails, only the steady drip of crypto micro-payments routed through resellers in Cyprus, the Netherlands, and Central Europe. Each one buys continuity, not capability: another month of hosting for a phishing kit, another quarter of uptime for a command-and-control node, another renewal for a cover domain. This is cyber operations as a subscription service.

Charming Kitten’s internal operators follow a logic refined not in cyberspace, but in decades of sanctions survival. The same micro-smuggling economy that keeps Iranian goods moving across closed borders now underpins their digital operations. Instead of hiding budgets behind complex front companies, they fragment everything: dozens of disposable aliases, each spinning up a low-cost VPS, each paying through anonymous crypto gateways, each leaving behind a receipt that looks indistinguishable from civilian freelance infrastructure work. To an external reviewer, the activity resembles a swarm of hobbyists tinkering with side projects. Inside the apparatus, every alias corresponds to a tasking order, a performance measure, and a supervisory check.

Moses Staff: APT 35’s Hidden Hand

If the first half of Episode 4 exposed the clerical machinery that sustains Iran’s cyber-espionage bureaucracy, then the next revelation shows what that bureaucracy actually builds: the operational scaffolding behind Moses Staff, the regime’s most theatrical and destructive façade.

For years, Moses Staff appeared to the outside world as a self-contained hacktivist brand, a militant cyber-propaganda unit releasing stolen Israeli data, encrypting servers, and posting defiant manifestos instead of ransom notes. Security vendors catalogued their campaigns from 2021 onward: the destructive intrusions against Israeli logistics firms, public data dumps, the sudden appearance of custom toolsets like PyDcrypt, DCSrv, and the later StrifeWater RAT. Analysts saw ideology and chaos; what they lacked, until this dump, was administration.

The artifacts contained in Episode 4, spreadsheets, invoices, and hosting dashboards, bridge that gap. Buried in the 0-SERVICE-Service.csv ledger, among the usual lists of domains and ProtonMail credentials, sits a quiet entry: moses-staff[.]io.

Moses-Staff[.]io Domain WHOIS History 2021-25

The artifacts are clear, APT 35, the same administrative machine that runs Tehran’s long-term credential-phishing operations, also ran the logistics that powered Moses Staff’s ransomware theatre. The overlap is not merely stylistic , it is infrastructural. The same ProtonMail accounts (bbmovement@protonmail[.]com, meriyalee@protonmail[.]com, cybersonix@protonmail[.]com) appear both in Charming Kitten correspondence and in the hosting ledgers that birthed Moses Staff domains. The same registrars recur, modernizmir.net, TheOnionHost, Namecheap. The same payment conduit repeats, crypto micropayments through Cryptomus routed via Cyprus.

Through these documents, the ideological mask of Moses Staff collapses into the administrative skeleton of APT 35. The supposed hacktivists and the government cyber-unit share not only tooling and targets but also the same accounts-payable system. The propaganda arm and the espionage arm are two products of a single workflow: different “projects” under the same internal ticketing regime.

The campaign record matches the timestamps perfectly. When public vendors documented Moses Staff’s early destructive wave in late 2021, the first lines of the ledger began appearing. As new RAT variants (like StrifeWater) surface in 2023 reports, the invoices show new server rentals across Europe. Each operational evolution leaves a paper trail, a cost code, an invoice number, and a renewal date. The bureaucracy of intrusion is also its audit log.

What these artifacts ultimately reveal is that Iran’s cyber strategy is not improvised, it is administered. Moses Staff is not a rogue collective or an ideological outgrowth; it is the production wing of a well-organized ministry of access. Its campaigns are budgeted, scheduled, and logged with the same diligence as a government infrastructure project. Behind every public defacement sits a clerk who approved the invoice, a supervisor who confirmed the payment, and a system that measures operational tempo in euros, not ideology.

By uniting the visible fury of Moses Staff’s leaks with the quiet paperwork of Charming Kitten’s ledgers, Episode 4 shows how Tehran’s cyber theatre truly operates. It is a play in two acts: the spectacle of resistance on the front stage, and the hum of bureaucracy behind the curtain,  where compliance, logistics, and finance keep the revolution online.

Moses Staff Victimology:

For Iran’s cyber apparatus, Moses Staff represents not just a hacking group but a psychological weapon forged for the long war with Israel, a digital insurgency cloaked in ideology and bureaucracy. Between 2021 and 2025, the group’s victim set reads like a cross-section of the Israeli state itself: soldiers, defense engineers, municipal employees, lawyers, and IT administrators, all woven into the same web of exposure. The data shows an operation built to mirror Iran’s doctrine of asymmetric warfare, one where humiliation and disruption can achieve what direct confrontation cannot. Moses Staff’s leaks are not random; they’re curated performances designed to erode trust, advertise reach, and export Tehran’s revolutionary narrative into the digital domain.

The victimology reveals a disciplined targeting strategy. At the top are the institutions that define Israeli power, its military, intelligence services, and defense contractors. Leaks from IDF personnel files and infrastructure maps are as much about psychological warfare as intelligence gain, meant to demonstrate vulnerability in the most sacred strata of the state. Below that tier sit the defense industries, Rafael, ZAHAL suppliers, and Mossad-linked contractors, raided for R&D data, internal communications, and resumes that can feed Iran’s own weapons programs or counterintelligence matrices. The campaign widens further to the civilian layer: the Israel Electric Corporation, small municipalities, and local law firms. These targets serve dual purposes, reconnaissance for future disruption and manipulation of public sentiment. By breaching cloud providers and IT service firms like UST Global Israel, Moses Staff extends reach laterally, turning trusted intermediaries into unwitting vectors.

Each compromise serves a strategic function within Iran’s broader playbook. The leaks and defacements broadcast messages of defiance, ‘we see you, we can touch you, and your secrets are ours.’ The exposure of Mossad contractors undermines recruitment, the leaks from law firms plant doubt about client confidentiality, and the focus on infrastructure mapping telegraphs a latent capacity for sabotage. None of this is random opportunism; it’s statecraft through spreadsheets and stolen archives. The operations echo the IRGC’s longstanding emphasis on soft power projection and psychological warfare: destabilize morale, complicate defense logistics, and inject fear into the bureaucratic machinery of governance. What emerges from the Moses Staff campaign is a template for twenty-first century conflict; non-kinetic information operations (IOs) act as extensions of Iran’s regional struggle, executed not with missiles but with leaks, defacements, and the quiet precision of digital attrition.

IRGC Moses Staff Motives: Political and Operational Motives

Moses Staff’s activity fits squarely within Iran’s long-standing doctrine of using cyber power as an instrument of asymmetric statecraft: not to match an adversary blow for blow, but to exploit vulnerabilities, gather intelligence, and exert political pressure without kinetic escalation. Analysts have repeatedly observed Tehran prioritizing disruption, information operations, and psychological effects over outright destructive campaigns, a posture that leverages lower-cost, deniable operations to punch above Iran’s conventional weight (CSIS).

Politically, the leaks, shaming posts, and public data dumps perform several simultaneous functions. They undermine confidence in Israeli institutions, signal capability to domestic and regional audiences, and provide tangible propaganda for allied proxies. Publicly exposing IDF personnel, defense-industry documents, and contractor records is designed to erode morale, complicate recruitment, and broadcast Tehran’s reach, all while avoiding direct military confrontation. This blend of intelligence collection and public humiliation is consistent with Iranian playbooks that combine cyber espionage with psychological operations (SentinelOne).

Operationally, Moses Staff’s target set and methods indicate pragmatic, goal-oriented priorities: collect military and R&D data to inform countermeasures and procurement; map critical-infrastructure networks for later disruption; and pivot through IT service providers to expand access and persistence. The group’s focus on Israeli government, defense, utilities, and support firms points to a layered campaign that values both immediate intelligence yields and the option to escalate to operational sabotage if the political calculus demands (MITRE ATT&CK).

Economically and bureaucratically, these campaigns are run like a government program, producing different incentives and tradeoffs: consistency, traceable procurement and staffing, and an emphasis on service continuity (renewals, vetted resellers, repeatable toolchains) rather than opportunistic monetization. In practice, that means operations are resilient and persistent but also bound by the limitations and inefficiencies of state logistics, which can produce predictable patterns for defenders to track (Sekoia.io Blog).

Taken together, the political objective is coercive image-making and long-term attrition; the operational objective is to create persistent, actionable intelligence and latent disruption options. Moses Staff’s strikes are therefore best understood as a non-kinetic extension of Iran’s regional strategy: to degrade adversary cohesion, buy strategic advantage in intelligence, and shape the information environment without crossing thresholds that would invite overt military retaliation (secalliance.com).

Infrastructure Footprints: Domain Ecosystem and Operational Purpose

The domain ecosystem uncovered in the dump reflects the familiar operational grammar of Iranian threat actors: disposable brands, thematic cover identities, and parallel infrastructure branches tailored to function, mission, and deniability. Rather than a single monolithic C2 cluster, the operators distribute their presence across loosely coupled domains that mimic recruitment agencies, talent portals, religious fronts, job boards, and generic operational shells. The result is an environment where each hostname appears mundane in isolation, yet collectively they form a coherent operational lattice aligned with Tehran’s playbook for cyber operations, influence campaigns, and access maintenance.

The most explicit cluster centers on Moses Staff, whose public-facing leak infrastructure has repeatedly cycled through domains such as moses-staff.io, moses-staff.to, and moses-staff.se. These domains serve as the group’s broadcast layer: data-leak sites, intimidation platforms, and staging points for propaganda distribution. The existence of multiple TLD variants underscores a resilience strategy; when one domain is seized or blocked, the narrative continues uninterrupted via a sibling domain. Certificates, TOR mirrors, and cloud-based mirrors found in the dump suggest a deliberate redundancy model: a propaganda architecture hardened by duplication rather than stealth.

A second cluster revolves around bbmovements.com, which appears tied to earlier Iranian influence operations masquerading as grassroots civic activism. In the dump, it sits adjacent to VPS and ISP management notes, pointing to a broader role than mere messaging: it likely served as a multi-use façade capable of hosting recruitment funnels, persona emails, and low-grade operational staging. This pattern mirrors other IRGC and Ministry of Intelligence (MOIS)  information operations, where social-movement branding is blended with technical infrastructure to blur attribution and intent.

Several domains, such as tecret.com, cavinet[.]org, kanplus[.]org, termite[.]nu, and dreamy-jobs.com, show no preexisting footprint in public threat intelligence, which itself is revealing. These domains exhibit the hallmarks of internal-use operational infrastructure: short-lived, singly purposed, and designed to blend into the noisy churn of small business web presence. Their naming conventions track closely with CharmingKitten’s habitual use of career-themed, service-oriented, or vaguely technical branding, perfect for phishing lures, credential-harvesting portals, or as stand-ins for command-and-control endpoints disguised as SaaS tools. Historical WHOIS behavior from similar APT35 operations suggests these domains likely hosted cloned login portals for Microsoft, webmail, VPN, and cloud admin panels.

Another subset, including wazayif-halima[.]org, israel-talent[.]com, and israel-talent[.]xyz, reflects the APT’s long-running interest in targeting Israeli organizations through employment-themed social engineering. These domains mirror job-placement branding common to Israel’s tech and defense workforce, offering a credible lure surface for spear-phishing campaigns aimed at engineers, analysts, and corporate staff. In typical Charming Kitten fashion, the operators diversify across multiple TLDs, .com, .org, .xyz, to increase survivability and widen the radius of mis-typo capture for inbound victims.

The final layer consists of remnants of project-coded infrastructure, Abrahams Ax, kashef, and Bulgaria-based servers, that appear in the operators’ internal notes as hostnames, VPN exit nodes, or C2 pivots. While not domains themselves, the appearance of these labels alongside the real domains anchors the entire set within an organized procurement cycle: operators stand up a domain, bind it to a VPS host, wrap it in an alias persona, and log it in the operational spreadsheet. The repetition of this pattern across all domain families demonstrates that Charming Kitten does not innovate on infrastructure; it iterates. Domains are spun, burned, and replaced through a playbook that prioritizes administrative continuity over sophistication.

Bitcoin Wallets, Transactions, and Payments: What the Ledger Shows

The financial layer in Episode 4 is striking for its simplicity: tiny, repeatable purchases funded via a crypto gate, recorded against obvious operational artifacts. When we shift focus from invoices and VPS to the on-chain fragments and address artifacts embedded in the ledger, three points stand out: small amounts, fragmented transaction provenance, and direct domain ties.

Wallets and Payments:

The network of wallets and transaction fragments aligns closely with the domains and services in use. Within the operational records, domains appear side by side with payment entries, forming a self-contained system of attribution. When an on-chain artifact, such as the address beginning with 3A5M, appears, it sits directly beside a corresponding hosting entry like secnetdc.com, creating a syntactic and functional pairing between cryptocurrency movement and specific infrastructure assets.

A second class of payment evidence emerges through Cryptomus transaction fragments. Though these fragments conceal the actual blockchain addresses, their repetition across multiple entries points to a common gateway and a stable set of receiving clusters. This consistency implies that crypto flows were routed through a single, reusable payment processor, maintaining continuity across purchases while obscuring direct traceability.

The financial behavior itself is telling: transactions are deliberately modest, typically between €12 and €18, suggesting micro-purchases calibrated to sustain long-term infrastructure while remaining invisible to financial compliance systems. Their scale minimizes AML or OFAC scrutiny and blends seamlessly with ordinary online commerce.

Taken together, the recurring use of the Cryptomus gateway and the EDIS reseller reveals a structured procurement method. Cryptocurrency payments are funneled into a limited number of merchant endpoints, while the ledger documents the dispersed operational footprint, domains, virtual servers, and service nodes. The isolated appearance of an explicit on-chain address signals occasional lapses in operational hygiene, providing a rare and valuable foothold for blockchain correlation and broader attribution.

The Operational Collection Wallet: 1K93styPFkDGsTYNjgqaDN6xWy5NmUDLhh0

The above Bitcoin address, 1K93styPFkDGsTYNjgqaDN6xWy5NmUDLhh, is a central wallet that, according to the snapshot, transacted 90 times on-chain and received 0.15369121 BTC (displayed USD equivalent $15,259.37) before being fully emptied. The dashboard reports a total sent value identical to the total received (0.15369121 BTC), a total lifetime volume of 0.30738242 BTC (~$30,518.75), and a current on-chain balance of 0.00000000 BTC. That combination, many small transactions in, periodic consolidation and an ultimately zero balance, is a classic pattern for a service/collection wallet used to aggregate micro-payments and forward them onward.

Transaction-level indicators support that reading. The above image shows multiple incoming micro-payments of 0.0005 BTC (and similar small amounts) and at least one large consolidation spend (an outgoing TX of -0.05863265 BTC that lists 94 inputs) with a relatively large miner fee (the UI shows a fee of ~139.5k sats, displayed ≈ $138.53). Ninety total transactions with dozens of tiny inputs plus multi-input consolidation spends strongly suggest the wallet accumulated many small (Unspent Transaction Output’s typical of customer payments, routing from mixers, or automated payouts) UTXOs and then periodically consolidated or forwarded those funds in bulk.

Operational interpretation

  • Role: collector/aggregation wallet for micro-payments (or small receipts) rather than a long-term cold storage or exchange custody address.
  • Behavioral signals: repeated small inbound amounts (0.0005 BTC) indicate either automated service payments, funneling from many upstream payers, or staged outputs from a mixing service. The later consolidation with many inputs shows someone consolidated value — either to forward to a service/exchange or to obfuscate origin via coinjoins/mixers.
  • Current state: emptied at snapshot time, meaning funds were forwarded elsewhere; those downstream hops are the logical next step to trace for attribution or cash-out points.

Operational Tradecraft: The Business of Operating an Intelligence Operation

The operational material in the dump turns what might seem like scattered activity into a clear and repeatable workflow. Each cycle begins with the creation of a ProtonMail persona, followed by the registration of a domain crafted for a specific campaign or cover purpose. Once the domain is secured, the operators purchase a low-cost VPS from a European reseller, pay through a cryptocurrency gateway, log the ticket number and credentials in the shared service ledger, and then deploy a phishing page or command server. Different aliases perform the steps, but the method never changes. What appears improvised from the outside is, in fact, a stable routine executed with bureaucratic discipline.

Shifts in alias or billing identity do not disrupt the choreography. A hosting purchase tied to “Maja Bosman” in December 2022 follows the same pattern as a purchase tied to “Levis Cross” in April 2023, with identical hosting tiers, vendors, price bands, and reliance on the same crypto payment rails. The consistency across time and personas shows that these are not isolated procurement events but templated cycles aligned with the rows preserved in the service ledger. The repeated use of EDIS Global in Cyprus as a hosting provider, combined with Cryptomus as the payment conduit, reveals a supply chain optimized for pseudonymous acquisitions. The operators gain anonymity, yet each transaction produces a traceable fragment that links the infrastructure back to the underlying workflow.

The ledger entries tie the entire system together. Domains such as sskmt[.]com and misvps[.]io were purchased using the same KVM BASIC PLUS hosting packages priced around seventeen euros per month, and the same price points, SKUs, and hosting strings appear throughout the spreadsheet. These recurring signatures allow investigators to follow a direct path from procurement to deployment, exposing an industrialized process in which inexpensive VPS instances are acquired in small units, paid for with micro-crypto amounts, cataloged in a shared sheet, and activated as persistent infrastructure for Charming Kitten and Moses Staff campaigns. The paperwork is not peripheral; it is the blueprint of the operation, revealing the workflow, the dependencies, and the pressure points where the infrastructure can be disrupted.

Operational Tradecraft: Creation and Use of Aliases and Email Addresses

The group’s operational tradecraft is clearest in the way it manufactures and discards identities. Each alias exists only long enough to perform a single task such as registering a domain, submitting a support ticket, or purchasing hosting. These personas are not cultivated, expanded, or reused. They are burned immediately after use, leaving only a fleeting entry in a provider’s logs. Historical WHOIS patterns confirm this discipline. While the domains are registered privately, the surrounding metadata shows that none of the names or ProtonMail addresses reappear in earlier domain ownership records, public forums, credential dumps, or any other online history. Each identity is created for the lifespan of a single procurement event and then abandoned.

The names themselves follow a deliberate aesthetic. Some resemble Israeli contractors, others Russian freelancers, American small business owners, or European students. This diversity helps the operators blend into the global background noise of hosting and domain purchases. ProtonMail provides the perfect backbone for this approach because it is widely used across regions where these personas would plausibly exist. Each alias receives a fresh ProtonMail address formatted to look like an ordinary personal account, active only long enough to complete registration tasks or answer provider messages. The absence of recurrence across the broader internet reflects a tightly controlled identity-lifecycle model.

This strategy creates airtight compartmentalization. A persona used for one domain is never used for another, and no name ever appears across different clusters of hosts or campaigns. To hosting providers, the activity looks like a scattered set of unrelated customers making small purchases. To an investigator with access to the internal ledgers, the pattern resolves into a single workflow: routine creation of ProtonMail inboxes, rapid procurement of low-cost infrastructure, strict one-time use of identities, and immediate disposal. The result is an identity management system engineered to eliminate persistent markers, frustrate long-term correlation, and make each procurement step appear isolated despite being part of a unified operational machine.

Disposable Identities and Their Intersection with Domains, Wallets, Timelines, and Ledger Patterns

The group’s disposable aliases only make sense when viewed alongside the four systems they touch: domain acquisition, cryptocurrency payments, campaign timing, and the shared service ledger. Each fabricated persona appears briefly at the junction of these pillars, completing a single procurement action before vanishing. While the names leave almost no independent footprint, the artifacts they generate across these other systems reveal how structured and interconnected the operation truly is.

The domain clusters form the first pattern. Each one consists of a small burst of registrations made within minutes or hours of each other, all protected by private WHOIS. Every cluster is assigned its own set of one-time identities, ensuring that no alias appears across separate groups of domains. The financial layer reinforces this structure. Even though payments are routed through Cryptomus, recurring transaction fragments and micro-payments align with specific hosting renewals and activation dates. These fragments persist long after the aliases are discarded, creating durable technical markers that link procurement events to infrastructure timelines.

The final coherence comes from the campaign chronology and the service ledger. Domains and VPS instances often sit dormant for weeks after being purchased, then activate shortly before a phishing campaign or intrusion attempt. This gap between procurement and use reduces exposure while keeping the workflow efficient. The ledger ties all of this together. It records hosting SKUs, timestamps, credentials, and reminders that match the payment logs and vendor records, proving that each alias is simply a single-use instrument within a unified operational system. Taken together, the four pillars show how the group balances anonymity with internal discipline: identities disappear instantly, but the infrastructure they trigger follows a consistent and well-documented lifecycle.

In this way, the group is able to create an appearance of scattered and unrelated activity across the internet while maintaining a very tight internal process. The aliases provide camouflage. The infrastructure tells the real story.

Why This All Matters

In the world of cyber threat intelligence, attention often fixates on the glamorous front end of intrusion, the zero-days, the implants, the command-and-control frameworks. But what the APT35 and Moses Staff leaks expose is the hidden half of cyberwarfare: the bureaucratic engine that funds, equips, and sustains it. These files strip away the mythology of elite operators and reveal a machine that runs on invoices, crypto payments, and shared spreadsheets. What makes this revelation significant isn’t just what Tehran is hacking, but how it keeps hacking, through systems of procurement, payment, and administration that mirror legitimate state accounting.

Each transaction tells a story of adaptation under constraint. With Iran’s access to global finance curtailed by sanctions, operators have built a parallel economy of intrusion: crypto gateways like Cryptomus and NowPayments act as anonymized intermediaries; small-scale European VPS resellers such as EDIS Global and Impreza Host become unwitting facilitators; and administrative ledgers track every euro spent as if it were a budget line in a ministry. The pattern reveals a sanctioned nation’s workaround, a form of gray-market logistics that turns the limitations of isolation into operational discipline. In this system, anonymity isn’t a byproduct of evasion; it’s a standardized function, designed to allow IO operations to persist without disruption.

By documenting the minutiae, the BTC addresses, the service tickets, the 12-euro hosting payments, this dump transforms our understanding of Iranian cyber capability. It’s not the product of rogue ingenuity but of institutional persistence, a bureaucratic adaptation of espionage to economic isolation. Cryptomus and similar payment rails effectively launder state funding into operational liquidity, while European resellers provide the legal and infrastructural scaffolding that make Tehran’s influence operations indistinguishable from ordinary e-commerce. In that sense, the lesson of these leaks is strategic: cyber power is not just built on code or exploits but on supply chains, accountants, and renewal schedules. The spreadsheet, not the malware, is what keeps Iranian operations online.

APPENDIX A: IOC’s

Operations Domains:

  • bbmovements.com
  • cavinet.org
  • secnetdc.com
  • tecret.com
  • termite.nu
  • dreamy-jobs.com
  • wazayif-halima.org
  • israel-talent.com
  • israel-talent.xyz
  • kanplus.org

MOSES STAFF DOMAINS:

  • moses-staff.io
  • moses-staff.se
  • moses-staff.to

Recurring Hosting Providers:

  • EDIS Global (Limassol, Cyprus) 57169
  • CloudDNS nodes linked to moses-staff infrastructure AS203391
  • “Server Samane” (internal operator label) AS16509
  • Bulgaria-based VPS nodes (3CX / VPN / “Karaj” references) AS21340

Email Addresses and Cover Names:

  • bbmovement@protonmail.com
  • meriyalee@protonmail.com
  • cybersonix@protonmail.com
  • john.porter857@protonmail.com
  • carlos.patel@protonmail.com
  • lolita259@proton.me
  • rona_yanga@proton.me
  • cou.nic@protonmail.com
  • timothyefimov@protonmail.com
  • gdavies007@proton.me
  • nansi.morad@protonmail.com
  • juliusyermolayev@protonmail.com
  • clark.norman@protonmail.com
  • mekhaeelkalashnikova@proton.me
  • shirley7070@proton.me
  • b.laws32@proton.me
  • molden5@protonmail.com
  • jhjbmuugtfftdd@proton.me
  • sanjilankopylova@proton.me
  • bashiriansul@proton.me
  • mlw.services.313@protonmail.com

Aliases:

  • Maja Bosman
  • Levis Cross
  • Sheldon Bayer
  • Edgar Evseev
  • Mekhaeel Kalashnikova
  • Shirley Bishop
  • Clark Norman
  • Julius Yermolayev

Bitcoin Wallets:

  • 3F2KWMSkjFdskQ2gV6pm4NA7JH2dx3jfCA
  • 16JMV9srqVDrK9u6z5cgKQjxnbJJp6gSxi
  • 32HF3h685344uJe7RMhhp5s5oBjaQq6BQh
  • bc1q567mrap7x4mwva2wlea3x9nc78pgp7dxspe6su
  • bc1qw0fqr597dqh3j8pe3c9gnl7vvkpgumxsak646g
  • 3Ck5dxmGXG3u1i3H7CM4vBpTeohDweJuYL
  • 3DN4UZ8gTmoCDaWP7ejmDYj4ByTQmKkmwU
  • 383j9rbvXyf4ZVaTPLPB1QfpkDJZfMEziG
  • 3MCyrpDmEUAWjx5rg5L3uqcZDux6e9Ns78
  • bc1qmasss9tj2wcyr8vyjajhn8qu9xr3g9hl0r0ne7
  • 34bvn64Hn9rgwahJJVveh8xTgseLtY8KpJ
  • bc1q2peh44qqjx9xg32xqfwzmrcrj42lean57vg6j4
  • 3BMbdmfc9sKKEtX9EFKbxbS75xTuKEzRjF
  • 35eL5XLnKWbpJPdQGULvqhQpNQEkBSPisN
  • bc1qxjmw2lknnne5hr0c4va2fjx0kzc9la4vhuaqex
  • 13Ue2i4Pombmd1NUGKgT8P1SCm8jw5F2Kj
  • 1K93styPFkDGsTYnjgqaDN6xWy5NmUDLhh
  • 19cChyRjku4zMKPr7PtkNSAdp9JE6AmiL2
  • 1HcPgNVrb7RvYkaGSu286qz2WF5UVBPP1R
  • 38Ai21L6mt7Qe2jnpxAZvjTLqKCYfjx9Am
  • bc1qtf2a865s7ncxcsdcwee8yyyqjhhkk9nn7ww98q
  • 32LvatxLwVfxpteiJc14HCyDDv2t2BRfj5
  • 31we2wugu5z7Mc3irnmZu9H7rXPrEqsuTf
  • 3Fv1X3we164eiBkme9wzHDU1iHpXuWcx8h
  • bc1qfzke9vknxdvtm6yrkru3ddzfl74ducx7s6rke2
  • 33PMgvq7HN8gdpd82WFCxKpVtsnSUWbLFx
  • bc1q9a8k39xpxeflsetdw92mzd98kg7gpcwsm2malh
  • bc1qpq0pk3xskqs70wg9werg3ypl8e255euzd5g4nq
  • 391baZHDES5TvotnYSnWwqnyYDXf2taWWb
  • 38SvFcEVRsfADhuxk7FS1p3TJfXYHewzGe
  • bc1q7xk8vk2cttvz92xjh2r4tfry0964rvvedeqpls
  • 17cHK7neWyAq1imHgjc6wKqoX3gqPcUx4N

IP Clusters:

  • 128.199.237.132 – DigitalOcean (WordPress scanner patterns)
  • 212.175.168.58 – Türk Telekom
  • 212.12.178.178 – Nour Communication Co. Ltd, Saudi Arabia
  • 1.235.222.140 – KRNIC (Korea)
  • 109.125.132.66 – Pishgaman Tejarat Sayar DSL, Iran
  • 83.96.77.227 – Fast Communication Co. Ltd, Kuwait

MITRE ATT&CK Technique Mapping

Aligned to Charming Kitten / Moses Staff Identity, Infrastructure, and Operational Tradecraft

TA0043 – Reconnaissance

T1595 – Active Scanning
Operators stage VPS nodes to probe target systems and deliver phishing infrastructure.

T1598 – Phishing for Information
Domains such as dreamy-jobs.com, israel-talent.com, and wazayif-halima.org are designed to lure specific industries for credential harvesting.

TA0001 – Initial Access

T1566 – Phishing
Job-themed, credential-harvest pages deployed on low-cost VPS nodes purchased through EDIS and Impreza Host.

T1078 – Valid Accounts
Harvested credentials fed into further access attempts, often timed shortly after domain activation.

TA0002 – Execution

T1204 – User Execution
Operators deploy phishing pages requiring victim interaction (login forms, document lures).

TA0003 – Persistence

T1098 – Account Manipulation
Use of harvested credentials to maintain foothold where applicable.

T1136 – Create Account
Single-purpose ProtonMail inboxes created for procurement (operational persistence at the infrastructure layer).

TA0004 – Privilege Escalation

(Not a focus of the dump, but implied in reference to Charming Kitten’s broader history of targeting Microsoft Exchange and Ivanti appliances.)

T1068 – Exploitation for Privilege Escalation

TA0005 – Defense Evasion

T1036 – Masquerading
Use of aliases that imitate Israeli, Russian, European, and American names; job-themed domains; fake recruitment brands.

T1070.004 – File Deletion
Use of single-use ProtonMail identities deleted or abandoned immediately after procurement.

T1112 – Modify Registry
(Not directly in the dump, but historically used in Moses Staff post-exploitation phases.)

T1027 – Obfuscated/Encrypted Files
TOR mirrors, private WHOIS, and encrypted communication channels.

T1564.003 – Hidden Artifacts: Disposable Email Identities
Strict one-time usage of procurement emails to prevent cross-cluster linkage.

TA0006 – Credential Access

T1056 – Input Capture
Credential-harvesting login portals deployed on purchased domains.

T1110 – Brute Force
Occasional activity against Israeli organizations (documented in public reporting of Moses Staff operations).

TA0007 – Discovery

T1087 – Account Discovery
Infrastructure scans for valid credentials through job-themed lures.

T1046 – Network Service Scanning
EDIS-hosted servers used to probe Israeli networks prior to planned intrusions.

TA0008 – Lateral Movement

T1021 – Remote Services
Use of harvested valid accounts through VPN portals and cloud dashboards.

TA0009 – Collection

T1530 – Data from Cloud Storage
Compromises of cloud/email providers in the civilian tier.

T1114 – Email Collection
Phished credentials provide mailbox access enabling data theft.

TA0011 – Command and Control

T1071 – Application Layer Protocol
C2 nodes hosted on low-tier VPS servers via HTTP(S).

T1105 – Ingress Tool Transfer
Payloads staged on purchased domains and KVM BASIC VPS instances.

T1568.002 – Dynamic DNS
Operators rotate hosts rapidly; CloudDNS references seen around Moses Staff mirrors.

TA0010 – Exfiltration

T1048 – Exfiltration Over Alternative Protocol
TOR mirrors used for anonymity during leaks.

T1567.002 – Exfiltration to Web Services
Leak sites operated under moses-staff.io, .se, .to.

TA0040 – Impact

T1491 – Defacement / Psychological Operations
Public leak sites intended to intimidate Israeli institutions.

T1485 – Data Destruction
Moses Staff’s destructive toolchain, already known in historical operations.

Supporting Operational Tradecraft Mappings

Identity Infrastructure Techniques

T1585.001 – Establish Accounts: Email Accounts
Single-use ProtonMail addresses for procurement.

T1583.003 – Domain Registration
Clusters of domains purchased for credential harvesting and campaign staging.

T1583.001 – Acquire Infrastructure: Virtual Private Servers
Routine procurement from EDIS, Impreza Host, Bulgarian VPS sellers.

T1586.002 – Compromise Accounts: Webmail
Credential theft from phishing operations.

Financial / Payment–Layer Techniques

T1586 – Obfuscation via Payment Providers
Cryptomus used to anonymize infrastructure transactions.

T1587 – Develop Capabilities
Infrastructure provisioning using micro-crypto payments in a repeatable pattern.

T1599 – Network Boundary Bridging
By paying through crypto and using global VPS hosting, operators evade regional filtering.

Learn More
Research
Chinese Malware Delivery Domains Part IV

Chinese Malware Delivery Domains Part IV uncovers 1,900+ new sites targeting Chinese-speaking users. Get a deep dive into infrastructure, TTPs, and AI-powered threat analysis.

Evolution of Infrastructure and AI-Powered Security Analysis

Summary

Since January 2025, DomainTools Investigations has been tracking a large cluster of malware delivery domains that’s been active since June 2023. We’ve published three reports on the cluster in the past 11 months, and in the latest Part III report in July 2025 we surmised that the cluster comprised over 2,800 domains. Since then, we’ve observed more than 1,900 additional malware delivery domains we suspect are tied to the same super cluster. This high volume of malware delivery domains makes for an excellent case study of AI facilitated analysis to take on the burden of website analysis, binary analysis and detection authoring. 

This report provides updates on the cluster following Part III and introduces a new experimental approach to defensive hunting and tracking malware delivery clusters such as the one described through the deployment of agentic AI systems that enable analysis workflows at the scale and speed necessary to match threat actor operations. Using a combination of task based AI orchestrator and sub agents, one security researcher achieved a 10x improvement in analysis throughput (assuming the websites were resistant to traditional solutions) with agents tirelessly processing over 1,900 malware delivery websites in the time traditionally required for roughly 200-400 manual investigations.

At a glance, the threat actor continues to demonstrate remarkable persistence and scale in their malware delivery operations, maintaining a wide variation in infrastructure deployment, lure sites, and malware delivery, which consistently appears to be  targeting Chinese-speaking users across the globe. Our analysis from May to November 2025 reveals notable operational evolution across distinct clustering patterns and continued spoofing of common software download websites as lures to deliver trojans and credential stealers. However, the same operational security weaknesses prevail in the form of highly leveraged SOA emails, tracking IDs for SEO manipulation, unique registrant names, and relatively unique infrastructure combinations. These factors allow for distinct campaigns to be linked together, forming the super cluster of approximately 5,000 malware delivery domains that has been active since 2023. 

Sample of the malware delivery websites spoofing common application download pages:

Part 1: Campaign Evolution Analysis

1.1 Infrastructure Evolution

At a high level, the actor's infrastructure has undergone an overall fragmented evolution with a few exceptions since our July 2025 report (Part III), which documented 2,800 domains created from January to May 2025. Current analysis indicates that approximately 1,900 additional domains have been created in the period from May 2025 to November 2025. 

Initially, domain hosting infrastructure was highly consolidated prior to May 2025, but this gradually gave way to diversification in August. By November, the infrastructure fractured further into smaller, more fragmented clusters with a focus on localization and operational security improvements as well as leveraging domestic Chinese registrars and randomized domain naming patterns.

The infrastructure evolution appears to demonstrate distinct transitions with a particular surge in overall variability from August to November:

Consolidated (May-July 2025)

  • Primary infrastructure: Alibaba Cloud Hong Kong
  • Registrar: WebNIC (98% concentration)
  • Focus: Chrome, Chinese VPN, and WPS Office spoofs

Diversified (August-September 2025)

  • New targeting: Signal and Telegram messengers
  • Domain clustering tightens (higher specificity scores)

Localization (October-November 2025)

  • Majority use of Chinese domestic registrars
  • 四川域趣网络科技有限公司 becomes primary registrar
  • Random domain naming patterns emerge
  • Possible OPSEC improvement attempts

[Diagram 1.2: Infrastructure Evolution Sankey] Flow diagram showing: Campaigns → Registrars → ISPs → Countries

1.2 Campaign Comparison Matrix

1.3 Operational Adaptations

Comparing domain registration trends from January through June 2025 with June through November 2025, several adaptations emerged:

Infrastructure Resilience

  • Reduced reliance on single ISPs (from 90% to 40% maximum concentration)
  • Geographic distribution across 5 countries (previously 3)
  • Registrar diversification: 8 unique registrars vs 3 previously

OPSEC Improvements

  • Increased use of privacy protection services
  • Shorter domain active lifespans (average 30 days vs 60 days)

Technical Evolution

  • Enhanced anti-automation JavaScript (20+ unique evasion signatures)
  • Multiple packer usage (VMProtect, ASPack, ASProtect, MPRESS)
  • Certificate pinning in Cloudflare-hosted domains

Part 2: Technical Threat Analysis

2.1 Malware Delivery Evolution

Analysis of 2,393 domains reveals continued targeting of Chinese-speaking users through spoofing campaigns. The actor maintains their core tactic of mimicking legitimate software download sites while expanding their portfolio.

[Diagram 2.1: Domain Naming Word Cloud] Word clouds showing naming patterns by campaign

Patterns in Spoofed Application Categories

Communication Tools (391 domains, 24.2%)
  • WhatsApp variants: 243 domains
    • Pattern: xx-whatsapp[.]com[.]cn, whatsapp-xx[.]com[.]cn
    • Examples: dk-whatsapp[.]com[.]cn, whatsapp-us[.]com[.]cn, ph-whatsapp[.]com[.]cn
  • WhatsApp Web: 34 domains
    • Pattern: web-*-whatsapp[.]com[.]cn, app-*-whatsapp[.]com[.]cn
    • Examples: web-apc-whatsapp[.]com[.]cn, app-hs-whatsapp[.]com[.]cn
VPN Services (363 domains, 22.4%)
  • LetsVPN/Kuailian (快连): 129 domains
    • Pattern: kuailian*[.]com[.]cn, kuaillian-xx[.]com[.]cn
    • Examples: kuailianwq[.]com[.]cn, kuailianod[.]com[.]cn, kuaillian-rd[.]com[.]cn
  • Kuailian variants: 43 domains
    • Pattern: xx-kuailian[.]top, kuailian*-kuailian[.]top
    • Examples: vd-kuailian[.]top, kuailian3-kuailian[.]top
Productivity Software (229 domains, 14.2%)
  • Google (search/services): 148 domains
    • Pattern: cn-*-google[.]com[.]cn, zh-*-google[.]cn, web-*-google[.]cn
    • Examples: cn-app-google[.]com[.]cn, zh-cn-google[.]cn, web-gg-google[.]com[.]cn
  • Youdao (translation/dict): 19 domains
    • Examples: youdao-youd[.]com[.]cn, web-youdao[.]com[.]cn
  • WPS Office: 18 domains
    • Pattern: wps-office-*[.]com[.]cn, wps-*[.]com[.]cn
    • Examples: wps-office-cnzh[.]com[.]cn, wps-jinshan[.]com[.]cn
Web Browsers (109 domains, 6.7%)
  • Chrome: 53 domains
    • Pattern: guge-*[.]com[.]cn, chrome-*[.]com[.]cn
    • Examples: guge-cn[.]com[.]cn, guge-chrome-app[.]com[.]cn, chrome-cnzh[.]com[.]cn
Cryptocurrency Tools (54 domains, 3.3%)
  • ImToken: 38 domains
    • Multi-TLD strategy: .com, .org, .top, .xyz, .shop, .click
    • Examples: imtz1[.]xyz, mtoken[.]shop, imtoken-im[.]click
Financial/Trading Platforms (51 domains, 3.2%)
  • AICoin: 27 domains (extensive infrastructure)
    • Multi-TLD strategy: .com, .org, .biz, .vip
    • Examples: aiiceoin[.]com, xz-aicoin[.]com, aicoin-zh[.]org
  • AICoin Download variants: 11 domains
    • Examples: us-aicoin[.]com, aicoin-xz[.]com, home-aicoin[.]com
Input Methods & Translation (43 domains, 2.7%)
  • Sogou Input: 15 domains
    • Pattern: *-sougoushurufa[.], *-sogou[.], sogou-*[.]
    • Examples: cnzh-sougoushurufa[.]com[.]cn, app-sougoushurufa[.]com[.]cn, shurufa-sogou[.]top, shurufa-sogou[.]top, sogou-pc[.]cn

2.3 Binary Analysis Results

From workflow analysis data, we recovered and analyzed 47 unique binary samples across campaigns:

[Diagram 2.2: Binary Analysis Overview] 

Malware Families Identified

Of the 1,900 domains processed, there were approximately 116 unique executables or archive files retrieved. In many cases, the same files were being delivered across multiple sites. 

Several samples identified were detected in VirusTotal; however there was a relatively consistent pattern of having large file downloads (100-250mb) from clusters. This likely prohibits most users from uploading to services like VirusTotal to scan the files without using the API. 

Other prominent patterns were protected files with VMPprotect or UPX and other packers of suspected droppers.

The experimental improvements to the website analysis and malware retrieval approach for research purposes provided additional insights into the malware delivery cluster: namely, that there is a relatively wide variation in the types of malware being delivered from relatively consistent web templates and relatively close clustering of domain management operations compared to previous findings in January and May 2025. This suggests that this long lived cluster dating back to 2023 has been or is evolving into a service platform where end users might bring their own malware in attempts to lure targets of opportunity.

2.4 Geographic and Temporal Patterns

Prior investigations found that domain registration patterns largely aligned with East Asia business hours (8am-5pm UTC+8) in terms of overall volume, continued activity through US holidays but cessation before Chinese New Year, and approximately 40% weekend reductions. The recent data from May to November 2025 does not appear to corroborate similar findings.

[Diagram 2.3: Registration Timing Heatmap 2025-05 to 2025-11] Hour/day heatmap showing timezone working patterns

[Diagram 2.4: Registration Timing Heatmap 2024-06 to 2025-06] Hour/day heatmap showing timezone working patterns

Working Hours Analysis

Peak Activity

Primary Peak: UTC 22:00 (276 domains, 13.9%)

  • Beijing: 06:00 (pre-business) | US East: 17:00 (end of day) | Moscow: 01:00 (night)
  • Note: 191 of 276 domains (69%) came from a single Oct 16 bulk registration event
  • This peak is anomalous for any standard timezone's business hours

Secondary Peak: UTC 15:00 (179 domains, 9.0%)

  • Beijing: 23:00 (late night) | US East: 10:00 (morning) | Moscow: 18:00 (evening)

Weekend Activity

  • Weekday/Weekend ratio: 3.26:1 (30% above expected 2.5:1 uniform ratio)
  • Thursday anomalies: 26.1% of activity (expected ~14%) 

Holidays:

Holiday Type Activity Level
Chinese early holidays (May) Reduced (15-44% of normal)
US holidays Reduced (20-30% of normal)
Chinese Mid-Autumn (Oct) Increased (235% of normal)

Infrastructure Geography

Indicator Value Implication
.cn/.com.cn TLDs 78.90% China-focused infrastructure
Chinese-language registrars 38.70% Operators comfortable in Chinese
Asia-Pacific registrars ~80%+ Regional procurement preference
DNS providers 100% China-based Infrastructure anchored in China

It's important to note that domain registrations can be done via API and in this case may well be, meaning they could be registered at any arbitrary time. Similarly, the TLD and regional hosting providings are typically globally accessible. Though previous analysis of this cluster found that a strong pattern of domain registrations and first observed DNS traffic were common during East Asia working hours, that is no longer evident from the data. What can be still inferred is that the focus remains consistently on primarily targeting Chinese language users. This inference was amplified by the cluster’s record spike in malware delivery website configurations during the Chinese Mid-Autumn festival, suggesting the intentions of this cluster are or have been primarily targeting Chinese users.

Part 3: Agentic AI for Analysis

3.1 Architecture Overview

The game-changing capability in our analysis comes from the deployment of specialized AI agents that operate in coordinated workflows. Unlike traditional automated tools that follow rigid scripts, the agentic system demonstrates adaptive intelligence in analyzing threats.

[Diagram 3.1: Agent Orchestration Flow]

Two-Layer Agent System

Layer 1: Orchestration

  • Receives analysis requests
  • Coordinates specialized agents
  • Synthesizes findings into threat intelligence

Layer 2: Specialized Analysis Agents

  • ScannerAgent: Browser automation and traffic capture
  • CodeAnalyzerAgent: JavaScript semantic analysis
  • BinaryAnalyzerAgent: Multi-tool malware analysis
  • YARAGeneratorAgent: Automated rule creation

3.2 AI-Powered Analysis Workflow

The power of agentic AI is best illustrated through an actual analysis sequence. Here's how the agents collaboratively investigated a suspicious domain:

Sampling of the website code analyzer subagent looks for malicious characteristics, identifies malware delivery behaviors, and determines if Yara rules could be generated for any identified malicious code on the site. If so, they get tasked to and created by another subagent and are immediately put to use going into future analysis so the system can learn as it goes.

Screenshot of the malware delivery website spoofing as a Google Chrome download site for Chinese language users:

Agent analysis summary of the website code, visual inspection, and network traffic analysis:

In a second example, the experimental AI service analyzed a gambling site that attempts to profile users and has anti-bot mechanisms to attempt to prevent scanners and web scrapers, and lacks a clear programmatic delivery mechanism. The screenshot below is of a download site for a purported online gambling mobile app. It serves an APK file that is packed and suspected of sideloading stealer malware.

Agentic code analysis of the site:

3.3 Scaling Defense with AI

The use of AI agents changes the economics of defense. In investigating malicious websites for example, the primary pain points for a human analyst can be determining the website characteristics to identify and retrieve malware and knowledge management to discern if similar site configurations have been observed before. The timing for a human analyst doesn’t scale to many websites or the many investigation avenues sometimes needed such as combining code and interactive analysis actions. Agents can run those same tools and action those same or similar interactions.

[Diagram 3.2: AI vs Manual Analysis Comparison]

Metric Manual Analysis AI-Powered
Domains per Day 20 - 50 2,000 - 4,000
Deep Analysis Time 10 - 90 minutes 1-10 minutes
YARA Rule Generation 5 - 30 minutes 30 seconds
Pattern Recognition Limited to analyst knowledge Comprehensive across corpus
Consistency Variable Deterministic
Scale Ceiling ~350 domains/week ~14,000 domains/week

Processing Transparency

It's important to note our actual performance metrics. During this experimentation phase we gave minimal system resources and allowed for 3 agent workers to process 1 domain each through the workflow at a time. These were their approximate completion time averages broken down by the core tasks within the analysis workflow. In one bulk processing run with 3 workers, 2,000 malware delivery domains were processed in approximately 10 hours.

  • Average Processing Time: 1-10 minutes per domain
  • Variance Factors:
    • Simple static sites: ~1 minute
    • Heavy JavaScript: ~3 minutes
    • Binary download and analysis: ~5 minutes
  • Parallel Processing: Up to 3 concurrent analyses
  • Daily Throughput: 400 - 4,000 domains

Conclusion

The threat actor continues to demonstrate capabilities in maintaining large-scale malware distribution infrastructure targeting Chinese-speaking users. Through our analysis of approximately 1,900 domains from May to November 2025, we observed an evolution in their operational tradecraft, including infrastructure diversification, enhanced evasion techniques, and additional spoofed entities such as popular Chinese AI and entertainment apps.

This investigation experimented with AI-powered analysis. The deployment of specialized AI agents enabled full coverage in analysis throughput while maintaining relatively high precision; however, agentic detection authoring remained a persistent weak point that continues to require further refinement. This capability changed the defender's equation, enabling complex and dynamic analysis workflows to scale to the volume of a large malware delivery campaign.

Special recognition goes to the AI agents that processed thousands of domains tirelessly and consistently. However, we must acknowledge limitations in our analysis. Processing times of 1-10 minutes per domain, while revolutionary compared to manual analysis, still require substantial computational resources for internet-scale defense. Attribution confidence, while high for core clusters, relied on spoof themes and infrastructure patterns rather than incorporating website and binary characteristics into clustering parameters as well. Future work seeks to incorporate an increase in the learn-as-we-go approach to identify additional sites with common malware delivery and detection evasion characteristics as well as further efforts in binary analysis integration.

As we look forward, the cybersecurity landscape has found itself balancing on the scales of an AI-pervasive era where criminals and defenders alike might empower their roles. 

The malware delivery campaign highlighted in this report provides insights into the scale of modern threats and an opportunity to show that with appropriate application of AI, defenders can keep pace.

Appendices

Appendix A:  IOC List

A complete list of all domains, file URls, and hashes can be found on our GitHub.

Disclaimer: This report contains analysis of malicious infrastructure for defensive purposes. All malware samples and malicious domains should be handled with appropriate security controls. The processing times and performance metrics stated are estimates based on our specific infrastructure and may vary in different environments.

Registrant Emails

1204504046[@]qq.com
18589929790[@]163.com
2035712403[@]qq.com
21033193[@]qq.com
2235053526[@]qq.com
2274677885[@]qq.com
2633067209[@]qq.com
286847215[@]qq.com
2957999579[@]qq.com
3283028829[@]qq.com
3653564961[@]qq.com
3799492994[@]qq.com
3839020959[@]qq.com
3926066154[@]qq.com
3951087743[@]qq.com
408367846[@]qq.com
515563424[@]qq.com
531679449[@]qq.com
614199941[@]qq.com
616489685[@]qq.com
624310867[@]qq.com
631599288[@]qq.com
646996136[@]qq.com
744812326[@]qq.com
9324928[@]qq.com
a685569961[@]outlook.com
aa16858895555[@]outlook.com
aisuite[@]hotmail.com
bnpk443[@]163.com
calaw19890912[@]gmail.com
chengwangyi1971[@]hotmail.com
cllhut005[@]gmail.com
ericq1027[@]gmail.com
eyuqicocafi68[@]gmail.com
fifermarti968[@]gmail.com
gfan8581[@]gmail.com
hs1726936602[@]163.com
huuhad791[@]gmail.com
jinqianj5722[@]163.com
kathyehk[@]gmail.com
liujing3721[@]outlook.com
logged567[@]gmail.com
nameibuhaore[@]outlook.com
nnaomalan042[@]gmail.com
pandashen0505[@]gmail.com
pluto_1111[@]hotmail.com
pokiohgff[@]gmail.com
raficponomarov5t[@]gmail.com
sophiahernandezv[@]hotmail.com
wc18973[@]outlook.com
westabuse[@]gmail.com
winrmbcc[@]gmail.com
yaarluq55342[@]outlook.com
yaqyfwhv476149[@]outlook.com
yilufa168899[@]163.com
yiyi95788[@]gmail.com
yojoy01[@]proton.me
zmpnz951938[@]outlook.com

Trackers

GoogleAnalytic4 codes
G-37ZJLQFQXW
G-3GR90RW2M5
G-936N0684JB
G-C31Z08KKX3
G-DDT7Z270WS
G-EK25PQRY5L
G-EVL1PWCP0M
G-GVGY3o1lft
G-MZNK5Z1NKP
G-PAWLWnpAps
G-QK5S7CE4J7
G-S0CCFL96VK
G-YYPYEVWJ1F
G-ZJuHGBTqxJ

gtm_codes
GTM-5P954SP
GTM-5XB9N2J
GTM-KW3XKWM
GTM-MG73JRC
GTM-MNBPZXP
GTM-PBZC932
GTM-PR42FSR
GTM-T3SK6H7
GTM-W5DBT74
GTM-WSTQ4SK
GTM-WX6RDCT

fb_codes
2140700472996352
3440778589358687
661275193346491
671933482235831
712357421178146

baidu_codes
1605bee5a12fc31c0b5bb9232d281e8f
2c583a8a0d28d3ddbec451e77062bdf6
352bf0fb165ca7ab634d3cea879c7a72
39f7c9431fdd7a3d6e06a177938de82a
4a4f0b2ee2183a70f09a260e209f9862
576cf858288eef7dc02ba30394d47747
61b4c1f7dc904a88452ac6e61b0d00e9
70ecc7c3a318165ed69d14518756aa48
749a9b99a1c14a45712efed8c3b8fedd
95878ed03acd631a38b80bc9056a0299
97881b7a6885b25d63db19094af0f5ca
db42cea977dda461f6890e8ba9c296e4
ec5f0730b33e6a7d5f6a246f8afed764
f4b3788b2247dd149fb7fdffe8aece79
fd3d9d32c2357b48b20735652ba569cd

yandex_codes
98466329

matomo_codes
https://tongji[.]mc52[.]com/


SHA256 File Hashes

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

Download URLs

http[:]//guge-cn[.]com[.]cn/ChromeSetup[.]exe
http[:]//wuquan[.]org[.]cn/static/file/WuQuaanrs[.]zip
http[:]//xunlei-pc[.]com[.]cn/XunLeiWebSetup12[.]4[.]7[.]3858xl11[.]exe
http[:]//cnzh-sougoushurufa[.]com[.]cn/sogou_pinyin_guanwang_15[.]8[.]exe
http[:]//qishuiyinyyue[.]com[.]cn/static/file/SodaMusic-v2[.]7[.]0-official-win32_x64[.]exe
http[:]//qishuiyinyuedouyi[.]com[.]cn/static/file/SodaMusic-v2[.]7[.]0-official-win32_x64[.]exe
http[:]//qishuiiyinyue-app[.]com[.]cn/SodaMusic-v2[.]7[.]0-official-win32_x64[.]exe
http[:]//chrome-m[.]com[.]cn/ChromeSetup[.]exe
http[:]//chrome-cm[.]com[.]cn/ChromeSetup[.]exe
http[:]//chrome-me[.]com[.]cn/ChromeSetup[.]exe
http[:]//qishui-yinle[.]com[.]cn/SodaMusic-v2[.]7[.]0-official-win32_x64[.]exe
http[:]//guge-chrom[.]com[.]cn/ChromeSetup[.]exe
....

Domains

7ov-kuailian[.]com[.]cn
8989faka[.]cn
aa-kuailian[.]com[.]cn
aaa666[.]cn
ackuailian[.]com[.]cn
acs-imtoken[.]com
ae-telegram[.]com[.]cn
aeg-kuailian[.]com[.]cn
aes-kuailian[.]com[.]cn
aicnion[.]com
aicoiene[.]com
aicoin-cn[.]biz
aicoin-down-pc[.]biz
aicoin-down-pc[.]com
aicoin-down-pc[.]org
aicoin-down-pc[.]vip
aicoin-down-soft[.]com
aicoin-down-soft[.]org
aicoin-download[.]com
aicoin-download[.]us[.]com
aicoin-home[.]com
aicoin-home[.]org
aicoin-home[.]us[.]com
aicoin-pc-download[.]biz
....
Learn More
Research
Threat Intelligence Report: APT35 Internal Leak of Hacking Campaigns Against Lebanon, Kuwait, Turkey, Saudi Arabia, Korea, and Domestic Iranian Targets

Unmasking APT35 (Charming Kitten). New report analyzes leaked internal documents, revealing their operational profile, Exchange attack chains (ProxyShell, EWS), and quota-driven compromise strategies.

Executive Summary

In October, 2025, internal documents from APT35 (also referenced as “Charming Kitten”) were leaked on github. Analysis of the leaked documents reveals a regimented, quota-driven cyber operations unit operating inside a bureaucratic military chain of command. The paperwork reads like internal administration documentation, monthly performance reports, signed supervisor reviews, and redacted KPIs, all oriented around measurable outputs rather than ad hoc opportunism.

Operators routinely file monthly performance reviews that enumerate hours worked, completed tasks, phishing success rates, and exploitation metrics; supervisors then aggregate those inputs into daily and campaign level reports that record credential yields, session dwell times, and high value intelligence extractions. Specialized teams are clearly delineated: exploit development (notably Ivanti and Exchange/ProxyShell tooling), credential replay and reuse, Human Engineering and Remote Validation (HERV) style phishing campaigns, and real time monitoring of compromised mailboxes to sustain HUMINT collection. The paperwork and logs show tasking, handoffs, and oversight , a workflow designed for repeatable collection.

From May 2022 onward, the group executed a region wide Exchange exploitation campaign that paired broad reconnaissance with precise post-exploitation tradecraft. The operation sequence is consistent across the material: build prioritized target queues focused on diplomatic, government, and corporate networks; run ProxyShell, Autodiscover, and EWS attacks; validate shells and extract Global Address Lists (GALs); weaponize harvested contacts with HERV phishing; and maintain sustained intelligence collection through mailbox monitoring and credential reuse. Internal logs, credential dumps, and “performance KPI” templates corroborate this end-to-end tradecraft and reveal deliberate, repeatable processes.

Taken together, the documents show a bureaucratized intelligence collection apparatus with structured tasking, measurable outputs, supervisory oversight, and specialized teams with a focus on systematic access, sustained collection, and exploitable intelligence yields. 

The Dump: Files Analyzed

The uploaded materials form a tightly linked forensic trail that maps both technique and organization. At the technical edge (e.g. infrastructure attacks), memory and server artifacts include an LSASS dump (mfa.tr.txt) containing plaintext credentials and NTLM hashes from MFA.KKTC (Apr 2022), and Dec 2023–Jan 2025 web access logs. These logs show RDP mstshash probes, .env/SendGrid fetch attempts, and wide-ranging curl path scans which document hands-on compromise and opportunistic scanning activity. Exchange artifacts (the ad.exchange.mail_* GAL exports) and annotated ProxyShell target lists (ProxyShell_target_*) show the precise targets and attack surface: diplomatic, government, and large commercial mail systems in Turkey/TRNC, Saudi Arabia, Lebanon, Kuwait, and Korea, with operator notes identifying successful shells, failures, legacyDN issues, and webshell paths.

Complementing the technical indicators are playbooks and conversion notes that reveal how vulnerabilities were weaponized: the Ivanti technical review (Ivanti سند بررسی...pdf) translates appliance CVEs into remote code execution paths, while the internal phishing framework (phishing herv.pdf) supplies HERV, style lure templates, campaign metrics, and operational procedures for turning harvested GALs into active collection nodes. Daily operational bookkeeping,  HSN / MJD Daily Reports (1403 series) and MJD Campaign Reports (May–July 1403), provide the human layer: KPI tables of lures sent, credentials captured, and mailbox dwell times, plus supervisor commentary and escalation logs into HUMINT and analysis units.

Crucially, the dataset ties virtual access back to a physical workplace: an on premises entry/exit log (entry_exit_form.pdf) confirms operator attendance and supports a picture of centralized tasking and oversight. Image based Farsi PDFs converted via OCR into structured IOC tables and actor maps close the loop by turning visual artifacts into machine-readable indicators (Actor Maps / OCR Extracts). All items are cross-referenced in a DTI evidence repository, producing an end-to-end evidentiary chain from vulnerability research and exploitation, through credential harvesting and phishing, to long term mailbox monitoring and human intelligence exploitation.

Base File Structure of Dump
Attack Reports
ID badges for employees attending conference

File Description
mfa.tr.txt LSASS dump (Mimikatz) from MFA.KKTC, Apr 2022 ,  plaintext creds & NTLM hashes.
access.log.txt Dec 2023–Jan 2025 web, access logs ,  RDP mstshash probes, .env/SendGrid fetches, curl path scans.
ad.exchange.mail_* Exchange GAL exports for Turkey/TRNC, Saudi (Al Rabie), Lebanon (customs.gov.lb), Kuwait, Korea.
ProxyShell_target_* Annotated target lists (TR, SA, KW, KR, IR) with notes: Shell / failed / legacyDN / path .aspx.
Ivanti سند بررسی...pdf Technical exploitation notes converting Ivanti appliance CVEs to RCE.
phishing herv.pdf Internal phishing, framework manual with sample HTML templates and metrics.
HSN / MJD Daily Reports (1403 series) Operator KPI tables: lures sent, credentials captured, mailbox dwell times.
MJD Campaign Reports (05–07 1403) Roll, ups of daily results; supervisor commentary; escalation logs to HUMINT & Analysis units.
کوروش and امیرحسین On, premises access log confirming physical operator attendance ,  evidence of centralized workplace.
Actor Maps / OCR Extracts Structured IOCs from image, based Farsi PDFs (converted to CSV).

Attribution Assessment

Analysis of the operational data, supporting documentation, and recovered artifacts strongly indicates that the campaigns represented in this dataset were conducted by an element of the Islamic Revolutionary Guard Corps Intelligence Organization (IRGC IO), specifically the cluster widely tracked as APT35, also known as Charming Kitten, PHOSPHORUS (Microsoft), TA453 (Proofpoint), or APT42 (Mandiant/Google). This grouping represents the IRGC’s cyber-intelligence arm, dedicated to long term espionage and influence operations.

The alignment between these materials and the known modus operandi of the Charming Kitten ecosystem is unmistakable. The Exchange exploitation wave documented in the leak, which leverages ProxyShell chains, EWS enumeration, and PowerShell automation for Global Address List (GAL) and mailbox extraction, precisely mirrors the tradecraft historically attributed to APT35 and its offshoots. 

This focus on diplomatic and governmental mail servers, combined with credential theft and OAuth token replay for persistent access, reflects a campaign objective centered on strategic intelligence collection rather than opportunistic compromise.

The bureaucratic structure observed across the leaked Iranian language documents provides additional confirmation. The templated KPI reports, supervisor approvals, attendance sheets, and quota driven performance metrics all indicate a state-managed, hierarchical organization rather than a criminal or contractor model. These features parallel descriptions from previously leaked internal APT35 materials, which showed identical reporting structures and efficiency-based ranking systems, an unmistakable signature of an institutionalized IRGC unit operating within military command oversight.

Further reinforcing this attribution is the target set. The campaign’s focus on ministries of foreign affairs, customs authorities, energy and telecommunications providers, and other high value sectors in Turkey, Lebanon, Kuwait, Saudi Arabia, South Korea, and domestic Iran aligns precisely with IRGC intelligence priorities. The inclusion of politically sensitive and economically strategic entities demonstrates a dual-purpose mission: HUMINT collection and geopolitical leverage. Such objectives are consistent with the IRGC IO’s remit to gather information for foreign policy, security, and counter intelligence purposes.

While some technical overlaps exist with other Iranian clusters, most notably the use of Ivanti and ProxyShell vulnerabilities, which have also appeared in APT34 (OilRig) and MuddyWater operations, the operational outcome here diverges sharply. Those MOIS-linked groups typically emphasize initial access and infrastructure disruption; in contrast, this actor emphasizes mailbox-level persistence, HUMINT extraction, and iterative phishing loops based on harvested address books. The sophistication and continuity of this collection cycle align squarely with APT35/TA453/APT42 activity patterns observed globally.

In sum, the available evidence points to a state-directed intelligence collection campaign orchestrated by an IRGC IO (Information Operations) subunit operating under the Charming Kitten/APT35 umbrella. The unit’s hallmarks – structured governance, Exchange-centric tradecraft, credential, based persistence, and regionally focused targeting – identify it as a disciplined, mission-driven element within Iran’s broader cyber-intelligence apparatus, functioning as a modern digital extension of the IRGC’s traditional human intelligence mission set.

Organizational Structure & Command Hierarchy

The leaked materials reveal a structured command architecture rather than a decentralized hacking collective, an organization with distinct hierarchies, performance oversight, and bureaucratic discipline. Across the translated Farsi reports, KPI tables, and personnel documentation (including the entry_exit_form.pdf and the 1403, series operator reports), the same formalized layout repeats: a tasked cyber-intelligence regiment operating under the supervision of the Islamic Revolutionary Guard Corps Intelligence Organization (IRGC, IO).

Command and Oversight

At the apex sits the Campaign Coordination Unit, responsible for issuing daily directives, assigning operational quotas, and approving mission scopes. These coordinators function as the managerial arm of the IRGC IO cyber wing, translating strategic intelligence requirements, diplomatic collection, political influence, or economic mapping into discrete, trackable campaigns. Each campaign corresponds to a named lead analyst, who oversees operational sub teams tasked with exploitation, credential harvesting, phishing operations, and real time mailbox monitoring (RTM).

The hierarchy extends downward into operator cells, each specializing in a technical domain:

  • Exploit Development Team weaponizing Ivanti, ProxyShell, and PowerShell chains into reusable scripts and RCE playbooks.
  • Credential and Access Team conducting LSASS dumps, token replay, and OAuth abuse for persistence.
  • HERV (HERV – Human Engineering and Remote Validation ) Phishing Unit refining HTML templates, MFA, bypass techniques, and KPI, driven lure campaigns.
  • RTM and HUMINT Liaison Team monitoring compromised mailboxes, tagging “HIGH, VALUE” accounts, and routing intelligence to human analysts for contextual exploitation.

Supervisors in each unit aggregate performance data into standardized “daily performance tables”, measuring metrics such as tasks completed, credential yields, efficiency rate, and dwell time. Every operator signs their report, while supervisors annotate performance with remarks like “approved,” “escalate to analysis,” or “retrain on template variance.” These records, when viewed sequentially, function like military after action reports: formalized, evaluated, and subject to review by higher command.

Physical Centralization and Attendance

The entry and exit forms corroborate that these operators work from a centralized, secured facility. Each badge entry corresponds to the same personnel named in operational documents, confirming an on premises command center rather than a remote contractor model. Time-In/Time-Outlogs align precisely with the timestamps of phishing campaigns and Exchange exploitation bursts, implying synchronized shifts and supervised execution windows. Badge identifiers visible in the uploaded imagery show IRGC-affiliated institutional branding, likely part of a controlled government or contractor complex used for joint HUMINT–SIGINT operations.

Bureaucratic Culture and Chain of Custody

The bureaucratic tone of these documents suggests a military style rhythm of accountability: operators submit, supervisors validate, analysts escalate, and coordinators report upward to IRGC IO command. Even internal communications reflect hierarchical addresses, operators refer to superiors by title, reports are formatted in identical templates, and comments reference “efficiency improvements” and “mission adherence.” The precision of this structure transforms what might otherwise appear as scattered cyber incidents into a reproducible intelligence pipeline governed by measurable output.

In sum, the hierarchy revealed by these materials depicts a state run intelligence apparatus organized as a production line of cyber espionage. The structure mirrors conventional IRGC command principles, with centralized oversight, delegated specialization, and performance accountability all adapted to the digital domain. This is not a loose network of freelancers; it is a regimented institution whose workflows, personnel controls, and managerial review cycles directly mirror those of Iran’s established military and intelligence bureaucracy.

Personnel and Organizational Structure 

The personnel and structural data contained within the APT35 corpus illustrates an institutionalized hierarchy typical of Iranian state cyber units operating under the broader IRGC umbrella. Across the extracted monthly performance reports (بهمن ماه) and campaign summaries, personnel are consistently listed by engineering title, operational alias, or numeric identifier. The pattern mirrors internal Iranian defense-sector bureaucracies, where formalized role tracking, quota systems, and hierarchical reporting enable central oversight of technical operations.

Rather than a loose federation of contractors, the materials depict a workforce of salaried operators functioning inside a command-and-control bureaucracy. Monthly reports are logged, audited, and annotated by supervisors. Personnel are reviewed on exploit deployment speed, data exfiltration success, and compliance with tasking instructions.

Identified Personnel and Operational Handles

Command and Operational Oversight

At the apex of the structure stands Abbas Rahrovi (عباس راهروی) also known as Abbas Hosseini, an IRGC-affiliated official responsible for creating and managing a network of front companies that serve as the administrative and technical cover for ongoing cyber-espionage campaigns. Under Rahrovi’s direction, this advanced persistent threat (APT) group has conducted offensive operations targeting telecommunications, aviation, and intelligence sectors across the Middle East and Gulf region, including Türkiye, the UAE, Qatar, Afghanistan, Israel, and Jordan.

Structural Hierarchy and Subordinate Cells

Within this organization, Vosoughi Niri (وثوقی نیری)) appears as a mid-to-senior-level coordinator tied to Rahrovi’s enterprise layer. Based on the corroborative evidence and document formatting observed in the uploaded “گزارش عملکرد ماهانه” (monthly performance reports), Niri likely fulfills a technical-administrative liaison role bridging field operators and the supervisory cadre. His name surfaces in contextual alignment with sections discussing efficiency optimization, task validation, and mission-adherence feedback loops, suggesting direct involvement in performance oversight and workflow standardization, hallmarks of IRGC command doctrine.

Niri’s placement within Rahrovi’s command hierarchy mirrors the IRGC’s hybrid intelligence model: a centralized leadership overseeing functionally specialized cells. Each cell reports through uniform reporting templates, reinforcing an internal culture of quantifiable accountability and military-style chain of command.

Activities and Counter-Intelligence Mandate

Operating under the guidance of the IRGC Counterintelligence Division, Rahrovi’s APT has expanded its mission set beyond foreign espionage. Internal communications and extracted documents show domestic surveillance of Iranian nationals deemed “regime opponents,” both inside and outside Iran. This dual focus, external intelligence collection and internal repression, typifies Iran’s fusion of SIGINT and HUMINT operations, where cyber units act as both offensive tools abroad and internal security enforcers at home.

Evidentiary Data from Dump

The exposure of this network is underpinned by an extensive evidentiary chain, including:

  • Official IRGC-linked documents retrieved from the APT’s internal network;
  • Personnel imagery correlating individuals to specific operations;
  • Attack and target reports indicating clear tasking cycles;
  • Translation and analysis documents reflecting multilingual target exploitation;
  • Chat logs from internal communications tools such as Issabelle, 3CX, and Output Messenger — all of which validate the group’s internal coordination, task assignment, and reporting cadence.

Collectively, these findings dismantle any plausible deniability the actors once held under the IRGC’s institutional cover. The discovery of structured managerial oversight, including figures such as Rahrovi and Vosoughi Niri, demonstrates that these are not freelance cybercriminals but state-directed operatives functioning within a bureaucratized intelligence apparatus engineered for persistence, precision, and deniable control.

Operators:

Within the dump, nineteen ID badges were found from a conference in Iran on Israel. The conference badges titled “Israel: The Fragile Mirror” («اسرائیل آینه شکننده») adds a rare human dimension to the dataset, linking technical operators, long attributed to Iranian cyberespionage activity, with physical attendance at a domestic ideological event. This conference, held in multiple sessions across 2023 and organized under the banner of Sahyoun24 and affiliated cultural-security institutes, functioned as both a propaganda symposium and an analytic forum on Israel’s strategic vulnerabilities. The theme, “Israel as a fragile mirror reflecting its own internal divisions, social decay, and geopolitical exhaustion”, was a deliberate rhetorical inversion of Israeli intelligence narratives about Iran. Official writeups describe panels on psychological warfare, media confrontation, and “the post-Zionist collapse of social cohesion.” It was hosted in Tehran’s Baq Museum of Sacred Defense (باغ موزه دفاع مقدس), a location symbolically linked to the IRGC’s self-image as the custodian of revolutionary defense.

The badges of fifteen named individuals carrying sequential registration numbers and standardized QR codes were recovered from the operator dump,  demonstrating that this was not merely a propaganda event but a managed, security-community gathering. The attendees listed (Norouzi, Sharifi, Hatami, Mousavi, Najafi, Nasimi, and others) correspond to the same internal communications clusters and device traces identified in the APT35 material. The overlap in formatting, file naming conventions, and local storage directories within the leak shows these badges were archived as part of the operators’ personal documentation, suggesting the attendees were members or affiliates of the same IRGC-linked technical units responsible for the Exchange exploitation campaigns detailed elsewhere in this corpus.

Within that operational ecosystem, attendance at Israel: The Fragile Mirror served several functions. First, it anchored the ideological justification for the group’s cyber campaigns, recasting intrusion and information theft not as espionage but as “defensive jihad” in the cognitive domain. Second, such conferences acted as recruitment and networking venues, where media officers, technical specialists, and propaganda units under the IRGC Cultural-Cyber Directorate intersected. These in-person sessions likely reinforced cross-unit collaboration between the operators running phishing and Exchange intrusion operations and those producing disinformation content targeting Israeli, Gulf, and Western media audiences.

The event’s agenda, particularly, the focus on psychological war, Zionist information operations, and digital sovereignty, mirrors the tactical doctrine embodied in APT35’s campaigns. The same operators photographed at or registered for the conference later executed targeted phishing and credential-theft operations using Israeli and Western diplomatic pretexts. The ideological framing provided by “The Fragile Mirror” conference positioned such cyber activity as a counter-narrative exercise: undermining adversary morale and exploiting perceived divisions within Israeli society. This linkage between cultural programming and operational tasking illustrates how Iran’s cyber apparatus merges soft-power indoctrination with offensive tradecraft, training its personnel to view digital espionage as a continuation of psychological warfare by other means.

In practical terms, the conference provided a semi-official aegis through which cyber operators could travel, convene, and exchange intelligence under the cover of academic or cultural engagement, consistent with IRGC and Ministry of Intelligence patterns observed since 2018. The badges’ sequential numbering and uniform QR encoding suggest centralized registration and identity management, potentially through the same administrative offices that coordinate the Thaqeb and Saqar technical institutes linked in prior datasets. By contextualizing APT35’s technical output within this ideological environment, the evidence affirms that their cyber operations are not rogue initiatives but state-aligned, bureaucratically normalized activities rooted in a shared worldview promoted through sanctioned events like “Israel: The Fragile Mirror.”

In sum, the conference stands as a bridge between rhetoric and operation: a physical manifestation of the belief system that animates APT35’s cyber doctrine. The operators who coded malware and exfiltrated credentials from foreign ministries also attended lectures on the “collapse of the Zionist regime.” Their presence at this event underscores that the Iranian state’s cyber units are not detached technologists but ideologically socialized cadres, trained simultaneously in faith, propaganda, and digital warfare.

The internal documentation reveals a structured ecosystem of named and numbered operators functioning under a disciplined command hierarchy. Personnel are consistently identified by a mix of professional titles, initials, and numeric designations, reflecting both bureaucratic formality and operational compartmentalization. Each name corresponds to a defined functional lane – engineering, exploitation, analytics, or administration — suggesting a deliberate division of labor designed to ensure continuity and accountability across campaigns. The repeated use of the honorific “Engineer” (مهندس) underscores the technical stature and formal employment status of several individuals, while numeric “Operator” tags indicate pseudonymous, task-based identities. Collectively, these records demonstrate that the unit operates as an organized workforce rather than an ad hoc hacker collective, with performance tracked, reviewed, and signed off by supervisors in a manner analogous to military or intelligence command structures.

Engineer Reza (مهندس رضا)

Referenced repeatedly as a technical lead overseeing infrastructure maintenance and deployment of Exchange-based exploits. Reza’s name appears in at least two separate performance reports, tied to scanning operations and uptime monitoring. Contextual indicators suggest a mid-level managerial role coordinating sub-teams responsible for access maintenance.

Engineer Kian (مهندس کیان)

Appears as a senior analyst or supervisor. The phrase “Team Kian” (تیم کیان) is used interchangeably with his name, implying that Kian manages a discrete operator cell. His team’s metrics emphasize exploit refinement, suggesting a focus on post-exploitation tooling and persistence.

Majid S. (مجید س.)

Associated with enumeration, lateral movement, and network scanning. The format of his report entries mirrors those of technical specialists who handle discovery and mapping of vulnerable services.

Seyed Mohammad Hosseini (سید محمد حسینی)

Mentioned in several analytic summaries, typically in administrative or oversight roles. Context implies he acts as an internal liaison between operational units and upper command.

Ali-Reza Karimi (علیرضا کریمی)

Described in the context of systems support and network configuration. Karimi’s work aligns with internal infrastructure maintenance and possibly VPN routing within Iranian ISP space.

M. Rahmani (م. رحمانی)

Appears in the monthly KPI spreadsheets as a performance tracker and reporting officer. His role appears clerical but critical — he consolidates operator statistics into higher-order analytic reports for command review, functioning as an internal metrics analyst.

Operator 04 / Operator 07 (اپراتور ۰۴ / اپراتور ۰۷)

Numeric identifiers tied to Exchange exploitation operations. Each “operator” designation corresponds to a unique user within the log corpus, implying either pseudonymous staff accounts or task-specific credential sets. Operator 04 is repeatedly observed in May–June 2022 Exchange exploitation records; Operator 07 appears in follow-on persistence activity.

Team Shahid (تیم شهید)

Referenced as an auditing or training subdivision, possibly connected to internal quality control. The term Shahid (شهید – martyr) is frequently used in Iranian military nomenclature for units named after fallen personnel.

Technical and Exploit-Focused Personnel

M. Kazemi (م. کاظمی)

Appears in Ivanti Connect Secure exploitation testing notes. Kazemi’s entries involve patch verification and vulnerability regression checks, indicative of a red-team engineering role tasked with exploit validation.

A. Mousavi (ع. موسوی)

Named in the phishing-infrastructure section, likely responsible for domain registration and control of operational mail servers. Mousavi’s profile suggests a hybrid technical–operational role bridging the gap between social engineering campaigns and backend infrastructure.

S. Ghasemi (س. قاسمی)

Connected to credential-harvesting playbooks and exfiltration scripts. Ghasemi’s responsibilities likely include automation of credential capture pipelines and data normalization for reporting.

Organizational and Institutional Context

IRGC Cyber Unit 13 (یگان سایبری ۱۳ سپاه پاسداران انقلاب اسلامی)

The structural relationship between APT35 and Unit 13 aligns with known IRGC cyber-force command chains, where Unit 13 functions as the technical backbone supporting both offensive operations and defensive R&D.

Structural Convergence: IRGC IO Unit 50, APT35, and the Integrated Command Apparatus

The recent exposure of IRGC Intelligence Organization (IO) Unit 50, internally codenamed “Thaqib,” completes the organizational puzzle long inferred from the APT35/Charming Kitten document set. Unit 50 represents the institutional fusion of Iran’s technical intrusion directorates and psychological-operations elements, revealing how bureaucratic oversight, cyber-espionage, and counter-intelligence are integrated within the IRGC’s command ecosystem.

At the top of this structure stands Abbas Rahrovi (aka Abbas Hosseini), identified as a senior IO-IRGC cyber command authority. Rahrovi’s role — confirmed through invoices, personnel files, and operational correspondence from internal program material — parallels the “senior coordinator” function described in APT35’s internal performance reports. His control over front companies, including entities such as Andishan Tafakor Sefid (“White Thought Depths”), provides the administrative façade through which APT operators receive compensation, assignments, and task metrics, erasing the divide between military and civilian employment.

Beneath Rahrovi, Manouchehr Vosoughi Niri emerges as an administrative signatory and performance-management officer. His name on employment and operational records corresponds directly to the managerial language and template uniformity seen in the monthly performance reports  (گزارش عملکرد ماهانه) recovered from the APT35 leak. Identical phrasing, “efficiency improvements,” “mission adherence,” “task verification”, and the standardized tabulation of operator hours indicate that Niri’s office served as the bureaucratic bridge between technical operators and IRGC leadership. The same hierarchy present in those internal Farsi reports — operator → supervisor → coordinator → command — appears in Unit 50 under Rahrovi, confirming that APT35’s workflow was embedded within IO-IRGC’s institutional chain of command.

On the technical side, the Thaqib RAT associated with Unit 50 represents the evolutionary successor to the Ivanti and ProxyShell exploitation workflows documented in the APT35 corpus. Both rely on identical tradecraft: phishing and supply-chain compromise for initial access, PowerShell-based persistence, credential theft, and staged exfiltration through controlled Iranian ISPs, particularly Zitel (AS50810), which also appears in the analyzed access-log dataset. The shared tool lineage and infrastructure reveal a unified development pipeline maintained under IO-IRGC supervision, with Unit 50 serving as the engineering and operational nucleus for multiple outward-facing APT teams.

Operationally, the overlap extends beyond technical objectives. Unit 50’s dual mandate—to conduct external espionage against regional and Western targets while monitoring domestic dissidents, mirrors APT35’s known blending of HUMINT, SIGINT, and influence operations. The recovered references to internal collaboration platforms (3CX, Issabelle, Output Messenger) further confirm a shared communications ecosystem coordinating campaigns across both “Thaqib” and APT35 workstreams.

Taken together, the evidence demonstrates that APT35 is not an isolated threat actor but a subordinate subdivision of IRGC IO Unit 50, reporting through Rahrovi’s command cell and administered by Vosoughi Niri’s office. The internal monthly reports, program artifacts, and infrastructure telemetry form a continuous evidentiary chain depicting a single, state-run enterprise that unites technical intrusion, information operations, and domestic counter-intelligence under one command architecture. What were once categorized as discrete clusters – APT35, Charming Kitten, Phosphorus – are in practice, modular teams within the IRGC IO cyber-espionage production line overseen by Unit 50.

Network and Target Infrastructure References

A recurring set of international IP addresses appear in associated logs, reflecting both operational relay points and foreign targets. These address patterns confirm that APT35 leveraged both domestic ISPs (for staging) and international IP space (for target access), maintaining operational separation through regionally diverse infrastructure.

Campaign and Codename Taxonomy

  • APT35 umbrella codename for the leaked corpus, representing the internal reporting and exploit-management environment of APT35.
  • Operation Kourosh, Operation Shayan, Operation Amir Hossein — likely internal monthly or operator-specific codenames correlating to بهمن ماه performance cycles.
  • Campaign Jordan (کمپین جردن) — externally oriented operation directed at Middle Eastern targets; cross-references suggest the campaign focused on government and telecom entities.

Operational Analytic Assessment

The recurring personnel patterns, structured performance tracking, and formalized hierarchy reinforce that APT35 represents a bureaucratically managed, state-directed offensive-cyber enterprise. Personnel titles and engineering designations mirror those of Iranian defense-sector agencies, indicating that operations were executed under institutional oversight rather than freelance initiative.

The integration of clerical, technical, and managerial functions (e.g., Rahmani’s metrics tracking, Reza’s technical supervision, Kian’s team leadership) demonstrates an intelligence organization where success is quantitatively measured and tightly supervised. The presence of formal education affiliations (Imam Hossein University) and front companies (Pardazesh Sazeh Co.) further corroborate IRGC influence.

This structure enables Iran’s cyber apparatus to align day-to-day operational output with strategic intelligence objectives, monitoring adversary communications, maintaining regional situational awareness, and ensuring persistent visibility into diplomatic and infrastructure networks across the Middle East and Asia.

Operational Themes

The documentation depicts a tightly governed system in which every operator adheres to a uniform reporting template rather than ad hoc notes. Each form records standardized metrics, tasks completed, efficiency rate, and supervisor remarks, transforming individual actions into quantifiable performance data. This bureaucratic structure allows supervisors to rank, reassign, and reward personnel, effectively turning the template into a scorecard that enforces consistency, auditability, and disciplined, repeatable behavior over opportunistic freelancing.

Reconnaissance is explicitly dual-mode. At scale, the unit runs internet-wide discovery, broad scanning to map services, identifies exposed endpoints, and prioritizes classes of vulnerable software. Those mass recon passes are then refined into country and sector-specific hit lists: curated ProxyShell target sets, prioritized Exchange estates, and hand-picked hosts for manual exploitation. The result is a funnel, producing high-value target queues tailored to regional objectives.

The collection is Exchange-centric by design. The group weaponizes Exchange attack chains (ProxyShell, Autodiscover, EWS enumeration, and PowerShell driven tasks) to extract mailbox contents and Global Address Lists. Those artifacts serve as both intelligence and infrastructure: GALs seed phishing lists; mailboxes become long, running HUMINT sensors; harvested messages reveal follow-on targets and operational context.

Meanwhile, persistence is credential-driven. Memory and token theft tools (Mimikatz style dumps), automated token-replay, and abuse of delegated OAuth flows are used to convert initial access into sustainable footholds. Rather than relying solely on fragile webshells, the operators bake credential reuse and token persistence into their lifecycle, enabling repeated access even as individual hosts are remediated.

Finally, exploitation and social engineering are integrated into a closed loop. HERV-style phishing operations generate credentials that feed the exploitation teams; compromised mailboxes both validate access and produce fresh lures and contact lists.This creates a self-sustaining cycle where reconnaissance, exploitation, credential harvesting, and phishing continuously replenish each other under programmatic control.

Geopolitics of Targeting & Campaign Goals

Focus and observed objectives across the dataset point to a strategically targeted, region wide intelligence effort rather than random opportunism. The geographic footprint centers on Türkiye, the Turkish Republic of Northern Cyprus (TRNC), Lebanon, Kuwait, Saudi Arabia, Jordan, South Korea, and domestic Iranian targets, with operations tailored to each locale’s political and technical landscape. Sector selection repeatedly favors high value collection points,  multifactor authentication gateways, customs agencies, telecom operators, energy firms, hospitals, managed service providers, and food and manufacturing supply chains, all places where access yields both operational intelligence and strategic leverage.

The group’s operational goals are explicit and multi-layered. First, strategic HUMINT focuses on sustained mailbox monitoring and GAL exploitation to collect diplomatic traffic and internal communications. Second, political leverage comes from selective disclosure and escalation of sensitive material as a coercive tool. Third, economic reconnaissance aims to map supply chains and critical infrastructure to inform targeting and potential future operations. Fourth, capability development is achieved by actively weaponizing newly disclosed CVEs and codifying those techniques into repeatable playbooks. Together, these focus areas describe an actor prioritizing persistent intelligence collection, influence, and the continuous maturation of offensive capabilities.

Intent Analysis by Targeted Entity

Across the documented campaigns, the unit’s intent mirrors a clear, target-specific calculus. Against government and critical-infrastructure organizations the objective is sustained intelligence collection and long-term access for strategic exploitation. With large commercial and telecommunications providers the focus shifts to credential harvest and lateral pivoting to upstream customers and partners, and against small-to-medium regional targets the operations emphasize scalable account takeover and data harvesting to build volume for broader campaigns. This prioritization, guided by centralized tasking and KPI-driven workplans, reflects an operational doctrine that values persistent footholds, credential multiplicity, and the ability to trade discreet access for wider network advantage.

Türkiye: Türk Telekom (212.175.168.58)

Observed activity: Exchange-centric intrusion attempts, credential harvesting funnels (GAL → HERV), persistent access scripts validated by Team Kian.

Likely intent:

  • Regional situational awareness: Turkish government and critical telecom routing are high-value for monitoring regional politics, Syria/Iraq corridors, and NATO-adjacent traffic.
  • Negotiation leverage: Access to telco mail flows yields insight into lawful intercept requests, roaming agreements, and government guidance to carriers.
  • Access brokerage: Telco footholds enable pivoting into downstream enterprise customers.
    • Why this entity matters: A national carrier concentrates VIP communications, roaming metadata, and cross-border peering visibility—rich for SIGINT and target development.
    • Confidence: High (KPI alignment + Exchange/persistence emphasis).

Saudi Arabia: Nour Communication Co. Ltd (212.12.178.178)

Observed activity: Phishing infrastructure mapped to credential theft, mailbox rule creation, and RTM tagging (“HIGH,” “VALUE”).
Likely intent:

  • Energy/diplomatic visibility: Follow Saudi policy and energy sector signals; anticipate negotiation positions in OPEC+, Yemen, and U.S. relations.
  • Target discovery: Enumerate subsidiary and hosted customer estates for second-order exploitation.
  • Narrative operations support: Email insight can enable selective leaks, timing-based influence, or coercive messaging.
    • Why this entity matters: Saudi carriers and service providers sit at the core of GCC communications.
    • Confidence: Medium-High (campaign notes + phishing/HERV handoffs).

Kuwait: Fast Communication Company Ltd (83.96.77.227)

Observed activity: Exchange account intrusion attempts, post-exploitation tooling validation, credential collection.
Likely intent:

  • GCC situational awareness: Track policy alignments, defense procurement, and oil/gas logistics.
  • Regional pivot: Use Kuwaiti access to identify shared vendors and managed-service footholds into neighboring ministries and SOEs.
    • Why this entity matters: Smaller state telecom/hosting providers can be stepping stones into ministries and national oil entities.
    • Confidence: Medium (campaign references + shared TTPs).

South Korea: IRT-KRNIC-KR (1.235.222.140)

Observed activity: Mailbox targeting, GAL export attempts, and KPI-tracked follow-ons.
Likely intent:

  • Tech and defense intelligence: Seek bidirectional visibility into R&D, export controls, and defense supply chains.
  • Crisis exploitation: Maintain latent access to leverage during peninsular or sanctions crises; harvest identity data for later impersonation.
    • Why this entity matters: KR provides high-value technology intel and alliance perspective; access to registries and service operators unlocks broad enumerations.
    • Confidence: Medium (entity class + Exchange workflow alignment).

Türkiye/Jordan Campaign Overlap:  “Campaign Jordan (کمپین جردن)”

Observed activity: Use of Team Kian’s persistence scripts in field ops, coordinated phishing and credential harvest, Exchange post-exploitation.
Likely intent:

  • Government and diplomatic monitoring: Track Jordan’s security cooperation, refugee policy, and regional coordination with KSA/UAE/Egypt.
  • Transit node mapping: Identify cross-border data flows and hosting providers used by NGOs and government bodies.
    • Confidence: Medium-High (direct campaign doc references to Team Kian tooling).

Singapore RIPE hosted relay (128.199.237.132)

Observed activity: Operational relay / egress node, not a victim per se.
Likely intent:

  • Operational security: Traffic laundering, geographic blending, and separation of staging from Iranian IP space.
  • Latency and availability: Stable cloud region used to front C2 or scraping tasks.
    • Why it matters: Indicates tradecraft maturity: clean separation of staging, collection, and command infrastructure.
    • Confidence: High (infrastructure role is consistent across operations).

Iran (Domestic) Pishgaman Tejarat Sayar DSL Network (109.125.132.66)

Observed activity: Operator side usage; staging, internal VPN, or development/test access.
Likely intent:

  • Operator base network: Workstation egress, internal tooling pulls, or QA against live targets.
    • Why it matters: Provides vantage for timing analysis and potential legal/telecom cooperation to identify operator shifts.
    • Confidence: High for “operator use,” not an “attacked entity.”

Toolset and Operational Practices 

The internal reports, campaign post-mortems, and technical write-ups produced by the actor cluster we are tracking reveals a deliberate, repeatable toolchain optimized for large-scale, quota-driven compromise operations: broad, automated discovery; prioritized exploitation of enterprise mail and VPN appliances; rapid persistence and credential harvesting; covert exfiltration; and bureaucratic measurement of results. The tools are a mix of widely available offensive frameworks and bespoke utilities, tied together by standardized playbooks and KPI reporting. The unit’s posture is that of an operationally mature, state-directed cyber organization: methodical, adaptable, and focused on measurable throughput rather than opportunistic one-offs.

The actor operates a hardened, process-driven offensive stack centered on high-yield enterprise targets: Microsoft Exchange (ProxyShell/ProxyLogon exploit chains and automated ASPX/.NET webshell deployers), Ivanti/Pulse Secure and similar VPN appliance exploit kits, and application delivery controller (F5) modules used to bypass patched Exchange instances. Reconnaissance is performed at scale with Masscan/Nmap-style scanners wrapped in custom orchestration, internal “shodan-like” scanning platforms, and lightweight HTTP probes that look for exposed admin endpoints, .env files, and RDP fingerprints to feed prioritized target lists. Initial access is routinely followed by rapid persistence (ASPX webshells with HTTP beaconing, scheduled tasks, PowerShell and WMI lateral execution), credential harvesting (EWS/Exchange scraping scripts, HTML credential collectors from phishing kits, LSASS dumping via Mimikatz-style utilities), and MFA defeat techniques including token-relay/AiTM patterns and token replay. Post-exploitation tooling is a mixed ecosystem of .NET webshells, Python parsers packaged with PyInstaller, modified Cobalt Strike–like beacons, and bespoke Windows loaders; exfiltration channels include encrypted 7zip archives staged to cloud storage (Mega, Dropbox, ProtonDrive), SMTP/compromised Exchange relays, DNS tunneling, and custom HTTP C2 beacons, while Telegram bots and API scripts provide operational telemetry and KPI ingestion for centralized reporting.

Organizationally, the unit is bureaucratic and organized into specialized discrete cells for scanning (Engineer Reza), exploit refinement and persistence engineering (Team Kian), phishing and credential ops (Engineer Shayan), and data staging/reporting, which produces high throughput and rapid tooling iteration. Their operational doctrine blends commodity offensive frameworks with in-house wrappers and tailored obfuscation to blend malicious traffic into normal enterprise patterns including the use of legitimate cloud providers for staging, VPN chaining and consumer VPNs to mask operator origin, and careful phishing templates localized to target regions. Intelligence implications are severe: this is a resilient, state-tasked capability optimized for mass credential capture and long-term access. Immediate defensive priorities are clear: harden and monitor Exchange/EWS/OWA with focused logging and retention; patch and segment remote-access appliances (Ivanti, F5); enforce phishing-resistant MFA such as FIDO2; hunt for ASPX webshell signatures and anomalous LSASS dumps or scheduled tasks; and deploy detection rules for scanning patterns, token-relay behavior, and unusual cloud staging traffic to disrupt the adversary’s kill-chain and their KPI-driven feedback loop.

The leaked materials reveal more than tools and targets, they expose a bureaucratized workplace culture that governs operator behavior through rigid templates, quotas, and supervision. Standardized KPI forms, efficiency metrics, and supervisor remarks turn tradecraft into measurable output, pushing operators to prioritize volume, more lures, faster credential harvests, shorter dwell times, even at the cost of OPSEC. Specialization across exploit, credential, and phishing teams (e.g., HERV units) increases technical proficiency but also moral distance, framing each task as a detached contribution to a collective mission. Centralized attendance logs confirm a shared worksite where peer pressure, oversight, and managerial review reinforce compliance and suppress deviation. The result is a sociotechnical system that produces consistent behavioral signatures, template-based phishing, reused webshell paths, and uniform reporting rhythms. This makes the actor efficient yet predictable, and therefore exploitable once defenders understand its metrics and workflow.

Malware Analysis

The uploaded data documents a mature, operator-driven intrusion toolkit built around two complementary components: a Windows-focused remote access trojan family (RAT-2Ac2 and associated stagers) used for persistence, credential theft, and data collection, and lightweight operator client tooling plus webshells that provide an interactive control channel for hands-on management of compromised systems. Evidence for the RAT, including developer notes describing modules for keylogging, browser credential theft, file collection, an encrypted length-prefixed command channel, and a canonical drop path under C:\ProgramData\Microsoft\diagnostic\ is present in the engineering reports and stager examples.

The client tooling is simple but operationally effective: multiple Python clients implement an interactive REPL that sends operator commands to server-side webshells by embedding the command inside an HTTP header (notably Accept-Language), accompanied by a static header token used by the operator clients as a handshake/fingerprint. Two clients use a fixed substitution cipher to obfuscate commands prior to transport, while another sends commands raw; all three hardcode different webshell endpoints and identical header fingerprints, showing reuse of the same control method across multiple targets.

Deployment and execution follow a consistent behavioral pattern. Initial access appears to rely on phishing and on Exchange/Autodiscover chains documented elsewhere in the corpus. Once an initial foothold exists, operators upload a webshell (commonly named using the m0s.* pattern), connect with the client, and issue commands to stage a more persistent artifact on internal hosts. Those artifacts are placed into ProgramData and masqueraded under plausible Windows service names (for example, Java/Update-style names or a vmware-tools.exe filename), then executed to create reverse tunnels or RDP-style connections back to external C2s. The operational control UI observed in the files constructs WMIC and net use commands programmatically, which the operator then dispatches to targeted hosts, enabling rapid lateral movement and hands-on exploitation.

From a capability perspective, the toolkit supports the full mid-stage lifecycle required for broad intrusions: credential harvesting and reuse, remote execution (WMIC, SMB admin share mounts), privilege persistence (service-style dropper placement), encrypted C2 with framing and optional TLS wrapping, and collection modules that capture documents, keystrokes, and browser-stored credentials. The presence of crash logging and developer guidance in the notes indicates an active development lifecycle and repeated testing in internal test ranges prior to production C2 rotation.

Operational fingerprints suitable for detection are clear and high-value. Host-level hunts should prioritize anomalous execution from ProgramData paths that mimic system services, the presence of vmware-tools.exe or JavaUpdateServices.exe under C:\ProgramData\Microsoft\diagnostic\, and svchost.bat helper scripts. Network and webserver detection should look for m0s.* endpoints and unusually long or non-language payloads in Accept-Language headers, and the static Accept-Captcha token string found in the client code, as that token provides an immediate, precise signature for operator traffic.

For containment and remediation, the priority actions are straightforward: treat any accounts and credentials observed in scripts as compromised and rotate them immediately, block outbound connectivity to identified C2 IPs and domains, and hunt for the ProgramData stager paths and web UI artifacts (including services masquerading under benign names and a local operator web UI typically served on port 8000 in these artifacts). When hosts are confirmed compromised, isolate and capture volatile memory, webserver logs, and disk images before remediation to preserve forensic evidence and enable robust reverse engineering of the stagers.

Confidence in the internal linkage across these artifacts is moderate to high. Multiple documents reuse the same linguistic style, operator names, filenames, and patterns, the dashboard and KPI tables reinforce an organizational, metrics-driven approach to operations, while the developer notes and client scripts reveal the technical underpinnings and the protocol choices operators relied upon. Taken together, the corpus points to an evolving, in-house capability that combines tailored RAT development with simple, reliable operator tooling and established operational tradecraft for lateral movement and persistence.

HUMINT & Counterintelligence Opportunities

The leaked materials reveal a bureaucratized ecosystem where structured templates, quotas, and supervision dictate operator behavior. Standardized KPI forms and supervisor annotations turn cyber operations into measurable output, tasks completed, efficiency rates, and quota attainment, pressuring personnel to maximize volume and speed at the expense of operational security. Highly specialized teams handle discrete phases of the attack chain, from exploit development to credential harvesting and HERV phishing, fostering technical proficiency but also moral distance from the consequences of their actions. Centralized attendance logs confirm an on-site workforce governed by peer norms and managerial oversight, reinforcing conformity and deterring dissent. Together, these dynamics produce a sociotechnical rhythm that makes the unit efficient, disciplined, and auditable, but also predictable, allowing defenders to anticipate and exploit recurring behavioral and procedural patterns.

The human-centered features of the operation create multiple pragmatic avenues for HUMINT and counterintelligence:

  • Exploit incentive loops. Because operators chase measurable outputs, injecting false or poisoned inputs (e.g., decoy GAL entries, seeded contacts that lead to dead ends, plausibly privileged but monitored accounts) can produce observable follow-through that exposes infrastructure, timelines, or personnel.
  • Target behavioral chokepoints. Handoffs (GAL export → HERV) and switchboards (RTM tags like “HIGH, VALUE”) are logical places to interpose deception or monitoring; a single tampered GAL can produce downstream intelligence on collection paths.
  • Leverage physical–digital correlation. Aligning badge logs with intrusion timestamps can help identify likely shifts, escalation windows, and even the specific teams running a campaign, enabling tailored HUMINT or legal avenues of pressure.
  • Encourage insider instability. Performance driven cultures generate internal stress. Well-crafted HUMINT approaches that emphasize career risk, poor performance, or the moral costs of operations can sometimes induce cooperation or mistakes, especially among lower tier operators who are most exposed to quota pressure.

Defensive & Operational Recommendations (HUMINT aware)

For effective defense, it is crucial to instrument human handoffs and monitor the signals that travel between people and systems: alert on GAL exports, anomalous mailbox access patterns, and KPI workflow metadata (filenames, templates, and report stamps). At the same time, deploy high-fidelity deception, seed plausible contacts, documents, and mailbox content designed to make adversaries reveal tooling, extraction paths, or C2 when they act on the bait. Where lawful HUMINT or partner cooperation is available, correlate badge entry/exit logs with intrusion timestamps to map shifts and likely operator windows, and use carefully timed notifications, managed false positives, and controlled exposure to introduce measurable friction into their metric-driven processes to slow their cadence without risking sensitive data. Rather than only chasing novel malware,defenders should prioritize detection engineering for repeatable artifacts, template-based phishing HTML, reused webshell paths, script headers, and standardized PowerShell idioms, and combine these technical measures with lawful HUMINT and legal process to target the social and supply-chain nodes that sustain centralized operations.

  • Instrument human handoffs: Monitor and alert on GAL exports, unusual mailbox access patterns, and the specific metadata used in KPI workflows (filenames, report templates).
  • Deploy high-fidelity deception: Seed plausibly genuine contacts, documents, and mailbox content that will cause adversaries to reveal tooling, extraction paths, or C2 endpoints when they act on the bait.
  • Correlate physical logs with cyber events: In environments where legal HUMINT or partner cooperation is possible, correlate badge entry/exit with intrusion timing to identify windows of activity and likely operator shifts.
  • Stress-test their incentive structure: Use notification timing, false positives, and managed exposure to create perceptible friction in the adversary’s metric-driven processes — enough to slow their cadence without exposing protected data.
  • Prioritize detection of repeatable artifacts: Focus defenders’ detection engineering on template-based markers (phishing HTML structures, webshell paths, script headers, and standardized PowerShell idioms) rather than on novel malware signatures.
  • Pursue lawful HUMINT and legal channels: Where policy allows, combine human-source collection, legal process, and cyber threat intelligence to target the social nodes (contractors, facilities, supply chains) that sustain centralized operations.

Malware, Implants & Tooling

The collected artifacts reveal a focused tooling suite and a clear operational tradecraft. At the center of their Exchange-facing work sits a ProxyShell/Exchange exploit chain: weaponized PowerShell scripts and automated routines designed to extract Global Address Lists and full mailbox contents. Memory-level theft and dumper tools, notably LSASS captures processed with Mimikatz-style workflows,  supply plaintext credentials and NTLM hashes that are immediately reused for lateral movement and persistent access.

Social engineering and credential theft are handled by a mature HERV toolkit that includes  configurable HTML credential harvesters, OAuth token theft and relay mechanisms, and campaign plumbing that turns harvested identities into reusable session tokens. Successful footholds are frequently backed by lightweight ASP.NET webshells placed under predictable paths (aspnet_client/, owa/auth/, exchange/temp/) to provide persistence and remote command execution.

Operators also employ custom stagers and minimal PowerShell and .NET loaders masquerading as benign administrator scripts  to bootstrap in memory implants and evade detection. For specialized targets, bespoke Ivanti wrappers and one-off exploit scripts convert appliance CVEs into reliable RCEs, demonstrating an ability to translate vulnerability research into targeted operational code. Together, these components form a compact, interoperable toolset optimized for Exchange compromise, credential capture, sustained presence, and rapid weaponization.

Indicators of Compromise

The dataset includes a mix of high-value domains, internal hosts, and telltale network indicators that together sketch the group’s target set and reconnaissance techniques. Observed domains of interest include government and corporate mail estates such as mfa.gov.ct.tr, alrabie.com, customs.gov.lb, and cnthoth.com, alongside commercial webmail gateways like mail.yousifi.com.kw and webmail.kccec.com.kw. The collection also documents multiple Iranian internal mail hosts with operator-annotated webshell paths, linking specific hosts to successful post exploitation activity.

Network-level evidence reinforces the pattern – sample source IPs tied to scanning and probing activity include:

  • 128.199.237.132 RIPE
  • 212.175.168.58 Turk Telecommunications
  • 212.12.178.178 Nour Communication Co. Ltd Saudi Arabia
  • 1.235.222.140 IRT, KRNIC, KR Korea
  • 109.125.132.66 Pishgaman Tejarat Sayar DSL Network Iran
  • 83.96.77.227 Fast Communication Company Ltd Kuwait

HTTP logs show a mix of automated reconnaissance and opportunistic credential harvesting that includes Cookie: mstshash= payloads indicative of RDP-style probes, attempts to fetch .env and SendGrid configuration files, and WordPress enumeration hits such as /?author= and /wp, json/wp/v2/users. Crawling activity is sometimes identifiable by user agent strings like Pandalytics/2.0, which the operators used for domain discovery and prioritization. Together, these domain, host, and HTTP indicators map a coherent reconnaissance to exploitation pipeline focused on mail infrastructure, credential harvesting, and rapid post-compromise expansion.

Tradecraft Evolution & Timeline

This section documents the actor’s operational evolution across the dump: an initially Exchange-centric, human-driven collection effort in spring–summer 2022 that progressively scaled into a multi-vector intelligence program through 2023–2025. Early activity focused on high-value mailbox access and HUMINT, ProxyShell/EWS exploitation, GAL exfiltration, and hands-on mailbox monitoring that fed HERV phishing cycles. Over time the group automated discovery and credential harvesting, codified exploit playbooks (including Ivanti appliance wrappers), and integrated those capabilities into KPI-driven phishing and persistence workflows. In short, the campaign shifted from a scalpel to a manual, leveraging targeted Exchange intrusions, to a hybrid scalpel-and-net model that adds large-scale scanning, appliance RCEs, and reusable credential infrastructures while retaining the original HUMINT endgame.

Timeline (key milestones and supporting artifacts)

  • April 2022 — Initial domain compromise evidence
    The LSASS/Mimikatz capture (mfa.tr.txt, Apr 2022) demonstrates early success at memory-level credential theft and domain compromise. These artifacts show plain text admin/service passwords and NTLM hashes that enabled immediate credential replay and lateral movement.
  • May–July 2022 (1403 series in the leaks) — Exchange-centric campaign wave
    The MJD campaign reports and HSN daily KPI tables (May–July 1403) document a concentrated Exchange exploitation wave: ProxyShell and EWS chains were used to validate shells, export Global Address Lists (GALs), and pull mailbox contents. Those GAL exports then seeded HERV phishing campaigns and longer term mailbox monitoring for HUMINT collection.
  • Late 2022 – 2023 operational consolidation and automation
    Post campaign internalization of lessons is visible in the templated KPI reports and playbooks: weaponized PowerShell scripts for GAL exports, standardized webshell placement paths, and automated token replay mechanisms. Operators shift toward operational repeatability; the same attack sequences appear across different target sets with only minor variance in lure content.
  • 2023–Jan 2025 broad reconnaissance and opportunistic harvesting
    Access logs spanning Dec 2023–Jan 2025 show mass internet scanning, RDP-style< Cookie: mstshash= probes>, .env and SendGrid configuration fetch attempts, and WordPress enumeration (</?author=, /wp, json/wp/v2/users>). This period marks an expansion to wide net discovery and opportunistic credential/config harvesting to supplement targeted exploitation.
  • 2023–2025 (intermixed) Ivanti and appliance exploitation
    The Ivanti technical review (internal “سند بررسی …” PDF) and the later Ivanti wrappers evidence indicate the group converted appliance CVEs into one-off RCE scripts. These capability additions broadened the attack surface beyond Exchange, enabling access to VPN and network appliances that could be used to reach additional mail estates or privileged management interfaces.
  • Ongoing closed-loop phishing and HUMINT sustainment
    Throughout the timeline, the HERV toolkit, RTM reports (mailbox dwell times, “HIGH, VALUE” tagging), and attendance logs show the persistent operational goal: turn access into sustained collection. Harvested GALs and mailbox contents feed new lures measured by campaign KPIs, creating a replenishing cycle of exploitation → harvest → phishing → monitoring.

Implications for defenders

  • Watch for hybrid indicators: Exchange abuse indicators (ProxyShell, suspicious GAL export activity) correlated with mass-scan signatures (RDP, style cookies, .env probes) often indicate the same operator lifecycle.
  • Prioritize detection of credential theft and token abuse (LSASS dumps attempting exfiltration, unusual OAuth consent flows), and instrument GAL export monitoring and alerting.
  • Treat appliance CVE advisories as operationally relevant to email estate security — appliance RCEs are being used to pivot to mail infrastructure.

Closing Narrative

The APT35 leak exposes a bureaucratized cyber-intelligence apparatus, an institutional arm of the Iranian state with defined hierarchies, workflows, and performance metrics. The documents reveal a self-sustaining ecosystem where clerks log daily activity, quantify phishing success rates, and track reconnaissance hours. Meanwhile, technical staff test and weaponize exploits against current vulnerabilities, most notably in Microsoft Exchange and Ivanti Connect Secure, before passing them to operations teams for coordinated use. Supervisors compile results into analytic summaries with success ratios and recommendations, forwarding them up the chain for review. This level of procedural rigor shows that APT35 functions less like a criminal group and more like a government bureau executing defined intelligence mandates.

Strategically, the materials confirm that APT35’s operations serve Tehran’s broader security objectives: maintaining awareness of regional adversaries, exerting leverage in geopolitical negotiations, and monitoring domestic dissent. Its Exchange-centric targeting underscores a deliberate focus on email ecosystems as both intelligence sources and control hubs, while the rapid weaponization of Ivanti and ProxyShell exploits illustrates an operational doctrine built on speed, persistence, and long-term access. The leak transforms analytic suspicion into evidence of a state-directed enterprise, a centralized system integrating SIGINT, psychological operations, and technical reconnaissance under military oversight. Together, these files mark a turning point in understanding Iran’s cyber apparatus: a professionalized intelligence service that has institutionalized the digital battlefield, erasing the boundary between espionage and warfare.

APPENDIX A: Leaked Document List

A consolidated list of every document that contains, references, or was used in assessing the individuals associated with APT35 / Charming Kitten (مهندس کیان, مهندس رضا, م. رحمانی, سید محمد حسینی, etc.)

Each entry includes the filename (exact as uploaded) and the personnel or entity references confirmed or inferred within it.

Documents Containing Personnel References

1. MMD-1403-01-27.pdf

Mentions / Context:

  • Aggregate monthly performance summary for the cyber unit.
  • Contains tables with operators’ metrics and identifiers.
  • Individuals: مهندس رضا (Engineer Reza), مهندس کیان (Engineer Kian), م. رحمانی (M. Rahmani), سید محمد حسینی (Seyed Mohammad Hosseini).
    Relevance: Baseline administrative report connecting supervisors to operator cells.

2. گزارش عملکرد ماهانه (بهمن ماه کوروش).pdf

(Monthly Performance Report — Bahman Month, Kourosh)
Mentions / Context:

  • Parallel structure to Kian’s report; indicates multiple team leads (Engineer Kourosh).
  • Cross-references Team Kian and Team Shayan as comparative performers.
  • Individuals: مهندس کیان, مهندس کوروش, م. رحمانی.
    Relevance: Confirms existence of multiple parallel technical teams under a unified metric system.

3. 4d6bf3834e9afb8e3c3861bf2ad64a68d9c7d870_گزارش عملکرد ماهانه (بهمن ماه_ (2).pdf

Mentions / Context:

  • Duplicate or revised Bahman-month report.
  • Mentions تیم کیان (Team Kian), تیم شایان (Team Shayan), اپراتور ۰۴, اپراتور ۰۷.
    Relevance: Key linkage document showing the operator numbering convention (04, 07) tied to Kian’s cell.

4. گزارش عملکرد ماهانه (بهمن ماه شایان).pdf

(Monthly Performance Report — Bahman Month, Shayan)
Mentions / Context:

  • Another operator-cell summary.
  • Individuals: مهندس شایان, مهندس کیان (for comparative KPI).
    Relevance: Confirms multiple peer teams; provides comparative success percentages.

5. _گزارش عملکرد ماهانه (بهمن ماه_REDACTED.pdf

Mentions / Context:

  • Redacted performance document, partially anonymized.
  • Visible metrics fields reference اپراتور ها and رحمانی.
  • Individuals: م. رحمانی, مهندس رضا.
    Relevance: Provides evidence of Rahmani’s central KPI consolidation function.

6. 544bf4f9e5fdb4d35987b4c25f537213ce3c926a_گزارش عملکرد ماهانه ( بهمن ما_REDACTED.pdf

Mentions / Context:

  • Another variant of the Bahman-month corpus.
  • Individuals: سید محمد حسینی (reviewer), مهندس رضا, م. رحمانی.
    Relevance: Reinforces hierarchical oversight and clerical structure.

7.

2d5b8da0d0719e6f8212497d7e34d5f1b1fa6776_All_target_report_20220508.pdf

Mentions / Context:

  • English-language operational summary of Exchange and Ivanti exploitation.
  • Individuals (roles cross-mapped to Persian reports): M. Kazemi, A. Mousavi, S. Ghasemi, Operator 04, Operator 07.
    Relevance: Connects technical operators and exploit engineers to foreign target campaigns.

8. 4d6bf3834e9afb8e3c3861bf2ad64a68d9c7d870_گزارش عملکرد ماهانه (بهمن ماه_.pdf

Mentions / Context:

  • Near-identical to the other Bahman-month reports; confirms Team Kian hierarchy.
  • Mentions تیم شهید (Team Shahid) in a quality-control context.
    Relevance: Establishes linkage between Kian’s technical branch and the auditing/training unit.

9. گزارش عملکرد ماهانه (بهمن ماه امیرحسین).pdf

(Monthly Performance Report — Bahman Month, Amir Hossein)
Mentions / Context:

  • Focused on مهندس امیرحسین (Engineer Amir Hossein).
  • References Team Kian and مهندس رضا in comparative task metrics.
    Relevance: Adds another operational cell; confirms standardized reporting and KPI structure.

10. گزارش اقدامات کمپین جردن.pdf

(Campaign Jordan Report)
Mentions / Context:

  • Operational summary for a specific campaign targeting regional entities (Jordan, Saudi Arabia, Kuwait).
  • References تیم کیان (Team Kian) tools in use during external exploitation.
  • Individuals: مهندس کیان, ع. موسوی (A. Mousavi), س. قاسمی (S. Ghasemi).
    Relevance: Demonstrates deployment of Team Kian’s persistence scripts in live operations.

11. Ivanti سند بررسی و تلاش برای اخذ دسترسی با استفاده از آسیب پذیری.pdf

(Ivanti Exploitation Analysis Document)
Mentions / Context:

  • Technical document describing weaponization of Ivanti Connect Secure CVEs.
  • Individuals: م. کاظمی (M. Kazemi), مهندس کیان.
    Relevance: Validates Kazemi’s role in exploit testing and Kian’s integration of the resulting payloads.

12. phishing herv.pdf

Mentions / Context:

  • Describes GAL→HERV workflow and credential-collection automation.
  • Individuals: ع. موسوی (A. Mousavi), س. قاسمی (S. Ghasemi).
    Relevance: Maps phishing infrastructure and data handoff pipeline to Kian’s credential integration.

13. گزارش نفوذ به ایمیل.pdf

(Email Intrusion Report)
Mentions / Context:

  • Describes compromised Exchange accounts and operational feedback loops.
  • Mentions اپراتور ۰۴, اپراتور ۰۷, تیم کیان.
    Relevance: Direct evidence linking Kian’s operators to live intrusions.

Cross-Reference Summary

Document Key Individuals Mentioned Function / Context
MMD-1403-01-27.pdf Reza, Kian, Rahmani, Hosseini Core admin summary
گزارش عملکرد ماهانه (بهمن ماه کوروش).pdf Kian, Kourosh, Rahmani Peer metrics
گزارش عملکرد ماهانه (بهمن ماه شایان).pdf Kian, Shayan Parallel team
گزارش عملکرد ماهانه (بهمن ماه امیرحسین).pdf Kian, Reza, Amir Hossein Comparative KPI
_گزارش عملکرد ماهانه (بهمن ماه_REDACTED).pdf Rahmani, Reza Metrics aggregation
544bf4f9e5fdb4d35987b4c25f537213ce3c926a_گزارش عملکرد ماهانه.pdf Hosseini, Reza, Rahmani Oversight document
4d6bf3834e9afb8e3c3861bf2ad64a68d9c7d870_گزارش عملکرد ماهانه.pdf Kian, Team Shahid QC and audit link
All_target_report_20220508.pdf Kazemi, Mousavi, Ghasemi, Operator 04/07 External campaign mapping
گزارش اقدامات کمپین جردن.pdf Kian, Mousavi, Ghasemi Field deployment
Ivanti سند بررسی و تلاش برای اخذ دسترسی.pdf Kian, Kazemi Exploit testing
phishing herv.pdf Mousavi, Ghasemi Phishing handoff chain
گزارش نفوذ به ایمیل.pdf Operator 04, Operator 07, Team Kian Intrusion follow-up

APPENDIX B: Analytic Attribution of IRG Operators

Command and Coordination Layer

Handle / Name Observed Role in Documents Probable Affiliation
Seyed Mohammad Hosseini (سید محمد حسینی) Appears as sign-off authority and reviewer in several بهمن ماه performance reports. His name sits above the technical leads and beside remarks referencing “approval” or “summary to command.” Fits the profile of a mid-grade officer within IRGC Cyber Unit 13 or its supervisory branch inside the Intelligence Organization of the IRGC (IO-IRGC).
M. Rahmani (م. رحمانی) Collects and normalizes team metrics, produces KPI dashboards, and forwards to Hosseini. No exploit or campaign coding language associated with him. Administrative / Performance Office, subordinate to IRGC Cyber Unit 13—responsible for quota control and reporting compliance.

2. Technical Leads – Engineering Cells

Handle / Name Document Evidence Functional Group Inferred
Engineer Reza (مهندس رضا) Appears in multiple Bahman-month reports and in redacted summaries as “responsible for network maintenance, uptime, and internal testing.” Infrastructure Engineering Cell — likely within the Technical Support and Operations Section that maintains internal VPNs and staging servers.
Engineer Kian (مهندس کیان) Head of “Team Kian.” Focus on Exchange and Ivanti exploit refinement, persistence scripting, and HERV-RTM hand-off modules. Exploit Development and Persistence Cell, part of the offensive R&D division attached to Unit 13’s Technical Directorate.
Engineer Kourosh (مهندس کوروش), Engineer Shayan (مهندس شایان), Engineer Amir Hossein (مهندس امیر حسین) Parallel team leads in other Bahman reports. KPI structure mirrors Kian’s, implying identical mission sets. Same APT35/Charming Kitten Engineering Division—independent operator teams sharing exploit templates.

3. Field Operators / Exploitation Tier

Handle / Name Role Indicators Probable Group
Operator 04 (اپراتور ۰۴) Appears in Exchange and email-intrusion logs, often first in sequence—suggests initial exploitation. Initial Access Team under Engineer Kian.
Operator 07 (اپراتور ۰۷) Seen in persistence follow-ups after 04’s events; may specialize in lateral movement and privilege escalation. Post-Exploitation Team, same cell.
Majid S. (مجید س.) Mentions of enumeration and scanning tasks. Reconnaissance and Target Discovery Section, infrastructure under Reza.
Ali-Reza Karimi (علیرضا کریمی) Responsible for routing, internal VPN configuration, and network stability. Network Operations and Support Unit under Reza’s supervision.

4. Specialized Technical Staff

Handle / Name Evidence Probable Group
M. Kazemi (م. کاظمی) Cited in the Ivanti vulnerability document as performing validation tests. Exploit Validation Lab — overlaps with Kian’s R&D but may report to an R&D section within the IRGC’s Electronic Warfare Organization.
A. Mousavi (ع. موسوی) Registered phishing domains and controlled mail infrastructure in phishing HERV.pdf. Social Engineering & Infrastructure Team, supporting the Exploitation Branch.
S. Ghasemi (س. قاسمی) Built credential-harvesting and exfiltration scripts; connected to Mousavi’s infrastructure. Data Collection and Exfiltration Team, downstream from Mousavi.

5. Training and Oversight

Handle / Name Role Probable Group
Team Shahid (تیم شهید) Cited in multiple Bahman reports as “آموزش / بازبینی” (training / audit). Quality-Control and Training Section, analogous to an internal red-team or curriculum unit under Unit 13.
Imam Hossein University (دانشگاه امام حسین) Educational origin repeatedly listed in personnel bios. Recruitment Pipeline for IRGC Cyber Force.
Pardazesh Sazeh Company (شرکت پردازش سازه) Appears as external cover / contractor. Front Company used for procurement and possibly payroll shielding.

Functional Constellations

IRGC Cyber Unit 13 (Command)

├── Coordination & Metrics

│   ├─ Seyed Mohammad Hosseini

│   └─ M. Rahmani

├── Technical Infrastructure Branch

│   ├─ Engineer Reza → Majid S., Ali-Reza Karimi

│   └─ Front Company: Pardazesh Sazeh Co.

├── Exploit R&D Branch

│   ├─ Engineer Kian (Team Kian)

│   │    ├─ Operator 04

│   │    ├─ Operator 07

│   │    ├─ M. Kazemi

│   │    ├─ A. Mousavi ↔ S. Ghasemi

│   │    └─ Team Shahid (audit/training)

│   ├─ Peer Teams: Kourosh, Shayan, Amir Hossein

│   └─ Toolchain: HERV ↔ RTM Modules

└── Recruitment / Training

    └─ Imam Hossein University

Synthesis

  • The corpus shows a matrixed command, typical of the IRGC Cyber Unit 13 / APT35 ecosystem:
    • Top Layer: strategic oversight and performance auditing.
    • Middle Layer: engineering leads managing semi-autonomous operator cells.
    • Bottom Layer: technicians handling exploit deployment, credential theft, and infrastructure upkeep.
  • The repeated educational and cover-company references indicate state-employment relationships, not independent contractors.
  • Each engineer-named team (Kian, Reza, Kourosh, Shayan, Amir Hossein) forms a production line feeding into shared toolkits (Exchange, Ivanti, HERV modules).

Analytic Confidence

Source Type Confidence Notes
Persian monthly reports High Direct internal metrics naming each engineer/team.
Ivanti & Phishing docs High Explicit technical authorship lines.
English campaign summaries Medium Contextual linkage via operator numbers.
Inferred hierarchy (IRGC Unit 13) Medium–High Matches open-source IRGC cyber command patterns (Unit 13 ↔ APT35 equivalents).

APPENDIX C: Malware Analysis & IOC’s Technical Section

Summary

The corpus contains two complementary toolsets used by the same operator ecosystem: (A) an in-house Windows RAT family (RAT-2Ac2 / stagers) used for persistence, credential theft, file collection, and encrypted C2, and (B) lightweight operator client tooling / webshell controllers used to interactively manage compromised hosts through webshell endpoints. The RATs are deployed under plausible Windows-looking filenames in C:\ProgramData\… and use reverse/RDP-style tunneling to external C2s (e.g., 103.57.251.153), while the client tooling uses unusual HTTP header channels (Accept-Language) and an Accept-Captcha static token to carry commands.

Category Name / Reference Description & Context Source Document(s)
Custom Remote Access Tool (RAT) “PowerShort / PowerShortLoader” (پاورشورت) Internal shorthand seen in scripts and task logs referring to a PowerShell-based implant used for lateral movement and credential exfiltration. Functions as a lightweight RAT with command execution and file collection features. Appears in گزارش عملکرد ماهانه (بهمن ماه شایان) and Ivanti exploitation report.
Credential-Harvester HERV Phishing Kit Used in coordinated credential campaigns against government and telecom sectors. Corresponds to the phishing document phishing herv.pdf. phishing herv.pdf, monthly performance reports.
Exchange Exploitation Toolkit ProxyShell / Autodiscover / EWS Chain Custom automation for exploitation of Exchange vulnerabilities (CVE-2021-34473/34523/31207). Follow-on modules collect GALs and mail data. All_target_report_20220508.pdf, Exchange GAL dumps references.
Persistence / Privilege Escalation Ivanti Exploitation Module Described as a local-privilege escalation vector against Ivanti Connect Secure appliances; includes payload loader for internal RAT. Ivanti سند بررسی و تلاش برای اخذ دسترسی با استفاده از آسیب پذیری.pdf
Information-Stealer Add-On KEYSAVE (کلیدسیو) Mentioned in Farsi performance logs; a credential-capture plug-in integrated into PowerShort. Extracts browser and RDP credential caches. گزارش عملکرد ماهانه (بهمن ماه امیرحسین)
Lateral Movement Tool RemoteTask.exe / TaskClient.ps1 Executable/Powershell pair for executing queued commands via internal message bus; equivalent to an internal job runner. MMD-1403-01-27.pdf
Communications Layer Output Messenger / 3CX / Issabelle Not malware per se, but operational chat systems used for C2 tasking, status reporting, and coordination. Monthly performance reports, attack summaries

Samples / artefacts observed (evidence list)

  • RAT engineering notes and stager examples (RAT-2Ac2): dropper path and example reverse command lines referencing C:\ProgramData\Microsoft\diagnostic\vmware-tools.exe and C2 103.57.251.153:443.
  • Operator client scripts: three Python clients that implement an interactive webshell controller: connect.py (encoded commands), rce5.py (encoded), and RCE4.py (raw commands). These include three hardcoded webshell endpoints and a static header token.
  • Operator web control / IIS panel: script that constructs WMIC / stager commands and serves Execute/Upload forms (operator admin UI), plus logs indicating a local operator web UI on port 8000 and masquerade service name Java Update Services.

Capability matrix (what the malware does)

  • Initial access / account capture: Phishing / credential harvesting lures and previously observed Exchange exploitation enabled credential access; RAT includes browser credential theft modules.
  • Command & Control: Custom encrypted channel with <len><base64(payload)> framing for RATs (test port 8080), and reverse/RDP tunneling to external C2s (103.57.251.153) using stager executables. Client tooling uses HTTP(S) GETs with commands encoded in Accept-Language header and an Accept-Captcha header token.
  • Execution & Persistence: Droppers install to ProgramData, spawn service-like processes and helper scripts (e.g., svchost.bat, JavaUpdateServices.exe), and remove installers after launch.
  • Lateral movement: Use of net use \\<ip>\C$, WMIC remote process creation (wmic /NODE:) to execute cmd.exe /c remotely.
  • Collection / exfiltration: file collection (documents, attachments), keylogger, clipboard monitor, and browser stealer modules noted in developer notes.

Code / protocol fingerprints (useful for detection)

  • Stager path & filenames: C:\ProgramData\Microsoft\diagnostic\vmware-tools.exe, C:\ProgramData\Microsoft\diagnostic\svchost.bat, C:\ProgramData\Microsoft\Diagnostic\JavaUpdateServices.exe, JavaUpdateServices.ps1.
  • Client header fingerprint: header Accept-Captcha: 2EASs2m9fqoFsq4E0Ho3a3K1yHh5Fl3ZtWs5Td1Qx63QWsZKJ9mV9... (static token present in Python clients) and usage of Accept-Language as a command carrier.
  • Webshell filename pattern: m0s.* (m0s.php, m0s.aspx, m0s.phto) used across multiple targets.
  • Client User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 ... Chrome/120.0.0.0 Safari/537.36 as used in operator clients.

Typical attack flow (behavioral timeline)

  1. Recon & phishing / Exchange exploitation to harvest credentials (documented campaign KPIs).
  2. Initial webshell deployment to public-facing host (webshell filename m0s.*).
  3. Operator connection using Python client which sends obfuscated or raw commands in Accept-Language header to the webshell endpoint (interactive REPL).
  4. Stager deployment on internal hosts into ProgramData with masqueraded names and service registration; reverse tunnel established to external C2 (e.g., 103.57.251.153).
  5. Lateral movement using WMIC / SMB (net use) to expand access; data collection via RAT modules.

MITRE ATT&CK mapping (high-level)

  • T1190: Exploit public-facing application (Exchange/Autodiscover/EWS in corpus).
  • T1566: Phishing.
  • T1071.001 / T1071.004: C2 over HTTP(S) and use of web protocols as command channel (Accept-Language carrier).
  • T1021.004 / T1021.001: Remote Services (WMIC, RDP tunneling).
  • T1027 / T1564: Obfuscated files / information (substitution encoding in clients, hiding in ProgramData).

Detection guidance (high-confidence detections)

Network detections

  • Alert on outbound connections to known C2 103.57.251.153 and 212.175.168.58.
  • IDS/Proxy rule: flag HTTP(S) requests where Accept-Language header length > baseline (e.g., >100 chars) or containing non-language tokens/command-like characters. Also detect presence of the static Accept-Captcha token.

Host detections

  • EDR / endpoint hunts for files executed from:
    C:\ProgramData\Microsoft\diagnostic\vmware-tools.exe and C:\ProgramData\Microsoft\Diagnostic\JavaUpdateServices.exe or JavaUpdateServices.ps1.
  • Process creation events (Windows Event 4688) where cmd.exe is spawned with net use \\ or wmic /NODE: command lines.

Webserver detections

  • IIS / web logs: search for GET/POST to */m0s.* paths and unusual header patterns (Accept-Language with long/encoded strings).

Recommended YARA / signature examples (for defensive use)

Below are defensive detection signatures (search for these strings or patterns on endpoints / file repositories). These are benign, detection-only rules — they do not enable use of any malware.

YARA-style example (conceptual — adapt to your YARA environment):

rule RAT_2Ac2_stager_path {

  meta:

    description = "Detect reference to known RAT-2Ac2 stager path"

    source = "session_uploads"

  strings:

    $s1 = "C:\\ProgramData\\Microsoft\\diagnostic\\vmware-tools.exe" nocase

    $s2 = "C:\\ProgramData\\Microsoft\\diagnostic\\svchost.bat" nocase

  condition:

    any of ($s*)

}

Signature for operator-client header token (IDS/snort approach — conceptual):

  • Match HTTP header Accept-Captcha containing 2EASs2m9fqoFsq4E0Ho3a3K1yHh5Fl3Zt... (full token in original scripts).

Forensic and containment playbook (concise)

  1. Immediate: block outbound traffic to listed C2 IPs and domains; rotate all credentials observed in scripts.
  2. Hunt & isolate: EDR hunt for ProgramData stagers and recent wmic/net use activity; isolate confirmed hosts and capture memory + full disk images.
  3. Preserve logs: collect IIS/webserver logs (requests to m0s.*), proxy logs (Accept-Language payloads), and firewall logs for the suspect IPs.
  4. Malware analysis: analyze any recovered vmware-tools.exe/JavaUpdateServices.exe in a disconnected lab, extract network protocol fingerprints, and produce YARA and Suricata signatures for deployment.

Confidence & provenance

  • Confidence in linkage: Moderate–high. Attribution to the same actor cluster is supported by: repeated Farsi-language artifacts, consistent project / operator names (Reza / Kian), reuse of filenames and paths, and reuse of webshell filename patterns and header-carrier technique across multiple client scripts and operational dumps.
Learn More
Research
Inside the Great Firewall Part 3: Geopolitical and Societal Ramifications

Part 3 analyzes the GFW as geopolitical infrastructure: economic protectionism, the export of cyber sovereignty norms, and the emergence of an authoritarian coalition (Russia, Iran).

The Great Firewall as Geopolitical Infrastructure

The Great Firewall of China (GFW) represents far more than a technical construct; it is the digital expression of a strategic doctrine, one rooted in state control, authoritarian stability, and a redefinition of sovereignty in cyberspace. Where earlier generations of internet architecture were built around openness and interoperability, the GFW stands as a counter-model: a system that enforces not just censorship but also discipline, not merely blocking information but engineering a compliant digital citizenry.

Through this lens, the GFW becomes a cornerstone of China’s broader governance model, extending internal social control mechanisms into the digital realm while also projecting power abroad. It is both shield and sword: insulating the domestic population from undesired narratives and foreign influence, while exporting technologies, protocols, and ideological models of digital sovereignty to other authoritarian or aspiring technocratic regimes. What began as a reactive security tool has evolved into a dynamic governance platform, tightly integrated with national infrastructure, industrial policy, propaganda channels, and law enforcement systems. Its architecture, as seen in the leaked data, supports real-time behavioral tracking, regionally adaptive enforcement, and centralized orchestration across ISPs, ministries, and military-linked vendors.

Internal Social Control: Domestic Implementation and Ideological Containment

China’s domestic deployment of the Great Firewall (GFW) is not merely a digital barrier, it is an infrastructure for surveillance engineering that operates in service of ideological conformity and political control. The infrastructure revealed in the dataset showcases a system that is deeply embedded within the national internet architecture, capable of granular content classification, multi-layered traffic inspection, and adaptive suppression mechanisms. Every facet of user interaction, from HTTP headers and TLS handshakes to DNS queries and application telemetry, is a potential input for censorship decisions.

At its core, the GFW’s domestic function is ideological containment: a technical means to preempt the circulation of narratives, symbols, or software deemed threatening to Party legitimacy. The filtering mechanisms are not static, they exhibit dynamic heuristics that flag circumvention traffic patterns, encrypted tunnels, and access attempts to banned services such as Twitter, YouTube, Wikipedia, and GitHub. Logs and routing tables within the leaked data reveal strategic targeting of:

  • Foreign software update servers, to prevent the installation of tools like Signal or Tor,
  • Cloud services and content delivery networks (CDNs) associated with media organizations and dissident communities,
  • Online education portals and democracy-linked content, particularly around anniversaries of events like Tiananmen Square,
  • Religious and ethnic advocacy content, especially concerning Tibet, Xinjiang, and Falun Gong.
Functional repression logic map

By mapping these access patterns to regions, user sessions, and endpoints, the GFW enables adaptive, real-time suppression, a form of algorithmic censorship that not only blocks, but surveils. The presence of regionally distributed “probe agents,” remote configuration push systems, and memory-optimized Redis-based blacklist updates shows a scalable enforcement model designed to track and shape the narrative landscape at population scale. This is not passive filtering; it is proactive thought boundary enforcement, engineered to neutralize dissent before it propagates.

Economic Engineering and Domestic Substitution

By systematically blocking foreign SaaS and collaborative software, China nurtures its own domestic ecosystem. Excel-based audits from the dump show targeted suppression of applications such as Google Docs, Zoom, Dropbox, and Trello. These gaps are filled by Tencent Docs, DingTalk, and Huawei-developed platforms, illustrating how the GFW enables economic protectionism masquerading as cyber defense. This pattern is not incidental but strategic: the firewall constrains market access for foreign competitors under the guise of national security, while ensuring that data flows remain within the control of state-aligned corporations.

The substitution effect creates a dual outcome. First, it accelerates the adoption of domestic platforms that are deeply integrated with state surveillance and content moderation requirements, ensuring ideological conformity and technical compliance. Second, it generates an economic moat for Chinese firms by shielding them from the competitive pressures of global incumbents, allowing state-championed companies to scale rapidly in an artificially insulated market. What emerges is a model where censorship and market engineering are inseparable, cyber sovereignty and industrial policy reinforcing one another.

Economic Engineering Logic Map

At a macro level, this reveals how the GFW is not only an instrument of political control but also a lever of techno-nationalism. By positioning domestic software as the only viable option for collaboration, communication, and file sharing, the state ensures that innovation pipelines, venture capital flows, and user data remain under Beijing’s regulatory umbrella. The firewall thus becomes a structural barrier to globalization, producing not only ideological isolation but also a controlled economic environment where China’s champions can thrive at the expense of suppressed foreign rivals.

The Splinter Net or Balkanization of the Internet Map Effects

On the geopolitical stage, this model contributes to the fragmentation of the global internet. As China’s approach is emulated by other authoritarian regimes, the result is a “splinter-net” or a “Balkanization of the internet”, where national borders dictate not just content but also economic flows and digital standards. Beijing leverages its ecosystem as a form of soft power, exporting platforms like Huawei Cloud and Tencent Meeting to Belt and Road partner states, presenting them as secure alternatives to Western software while embedding latent channels of influence and surveillance. In doing so, the GFW does not simply defend China’s information space, it actively reshapes global digital norms, setting precedents for a world where censorship and economic self-sufficiency converge as tools of statecraft.

Regional Influence and the Export of Cyber Norms

As Beijing cements control internally, it also exports its digital governance model. Observed similarities in data retention mandates, DPI (Deep Packet Inspection) deployment, and application whitelisting mechanisms in countries such as Iran, Vietnam, and Russia suggest the emergence of a “cyber sovereignty coalition” modeled after the GFW. These states borrow not only the technical blueprints but also the ideological framing: the notion that national borders should extend into cyberspace, with governments controlling what citizens can access, publish, and share.

Chinese firms such as Huawei and ZTE play a central role in enabling this diffusion. By providing turnkey infrastructure, core routers, traffic gateways, and 5G networks, these companies ensure that the hardware and software underlying new digital environments embed the same logics of inspection and control that define the Chinese model. This makes Beijing’s digital governance framework not just a domestic fixture but an exportable package, bundled with financing through the Digital Silk Road initiative. The export is both technical and political, shaping authoritarian states’ capacity to replicate China’s approach under the banner of sovereignty and “information security.”

Logical Mapping of the Framework and Geographical / Political Players

The effect is a gradual normalization of state-mediated connectivity. Countries adopting GFW-style controls are not simply importing equipment; they are adopting a philosophy that treats information as a threat vector rather than a public good. Over time, this fosters interoperability among authoritarian regimes, creating channels for knowledge transfer, intelligence sharing, and shared censorship protocols. The outcome is a fragmented, parallel internet sphere where repression is standardized and commercialized, with China as the principal vendor of both ideology and infrastructure.

Societal Impact and Resistance

Since the Tiananmen Square protests in 1989, the Chinese Communist Party has treated the free flow of information as an existential threat to regime stability. The development of the Great Firewall must be understood in that context: it is not simply a security apparatus, but a continuation of the Party’s broader strategy to prevent mass mobilization by limiting access to ideas, narratives, and organizing tools. Over the decades, censorship has evolved from blunt blocking of foreign websites to a finely tuned system of VPN blacklists, URL tracebacks, and application-level analytics. These capabilities allow authorities to correlate individual users with dissent behavior in near-real-time, ensuring that politically sensitive searches, conversations, and digital gatherings are identified and neutralized before they can coalesce into movements. In effect, the firewall transforms the internet into an extension of the state’s security services, eroding anonymity and embedding surveillance into the mundane acts of browsing, messaging, or sharing.

Yet despite this pervasive control, resistance is both persistent and adaptive. Beginning with early proxy experiments in the 2000s, Chinese developers themselves have been central to the creation of circumvention tools. Shadowsocks, created in 2012 by a developer known as clowwindy, pioneered lightweight encrypted proxying that could slip past deep packet inspection. When Shadowsocks nodes began to be actively targeted, the community iterated with V2Ray (Project V), a modular platform with multiple transport protocols and obfuscation layers. This in turn inspired Trojan, which disguises proxy traffic as ordinary TLS to resist probing, and later Brook and Xray, forks that pushed further into stealth and flexibility. Each of these tools originated within Chinese coding circles, highlighting how resistance emerges from inside the very environment being controlled.

Cultural Dissent Map since Tiananmen 

Culturally, dissent also manifests in creative forms. Social commentary critical of censorship and the Party circulates widely on Weibo, Bilibili, and WeChat before deletion, often employing satire, homophones, memes, or coded references to evade keyword filters. These “edge-ball” expressions illustrate both the limits of algorithmic censorship and the cultural resilience of Chinese netizens. Meanwhile, diaspora communities amplify resistance by publishing bypass techniques, hosting mirrors of blocked content, and maintaining repositories of circumvention code on platforms like GitHub, ensuring knowledge is never entirely erased inside the firewall.

The interplay between suppression and resistance thus produces an ongoing arms race. Each new round of GFW countermeasures provokes new tools, tactics, and cultural adaptations. While the firewall is formidable, it paradoxically nurtures an oppositional ecosystem that continually innovates around its constraints. Far from extinguishing dissent, the system creates a feedback loop of repression and resistance, embedding digital counterculture as a permanent feature of Chinese society. The result is a paradox: the GFW sustains authoritarian control, yet at the same time guarantees the continual reinvention of the very forms of resistance it seeks to eradicate.

Strategic Positioning in Global Cyber Norms

China’s long-term vision is visible through its participation in multilateral forums such as the UN’s Group of Governmental Experts (GGE) on ICT security and the Belt and Road Initiative’s “Digital Silk Road.” These initiatives provide diplomatic cover for Beijing’s promotion of “internet sovereignty” as a legitimate model of governance. In practice, this means embedding the logic of the Great Firewall into international policy discourse, presenting it not as censorship or repression but as a sovereign right of states to regulate information flows within their borders.

At the UN level, Chinese representatives have consistently argued for norms that emphasize non-interference in domestic internet policies, deliberately contrasting this with historical Western advocacy for a “free and open” internet. By reframing censorship as an extension of sovereignty, Beijing attempts to normalize state control as a global principle, effectively insulating its own practices from critique while empowering other governments to follow suit. The Digital Silk Road, meanwhile, operationalizes these ideas by providing infrastructure, financing, and governance templates to partner countries. Through fiber optic cables, 5G buildouts, and “smart city” packages, China creates an export pathway for both technology and ideology, linking development assistance with the adoption of Beijing’s governance model.

This approach positions China as more than a participant in global internet governance, it casts Beijing as a rule-setter. By aligning economic incentives with political norms, China gradually shifts the Overton window of global digital policy. What once would have been viewed as authoritarian overreach is rebranded as legitimate digital self-determination, creating a parallel order where the GFW’s logic is not an exception but an accepted standard.

Future Resistance and Possible Outcomes of Intensified Surveillance

If China accelerates its trajectory toward deeper electronic surveillance and repression, the societal and geopolitical consequences are likely to manifest in both predictable and disruptive ways. At the domestic level, a more comprehensive fusion of AI-driven monitoring, predictive policing, and ubiquitous biometric collection would further entrench a climate of self-censorship and fear. The integration of surveillance with economic and social systems, already evident in the Social Credit framework, would amplify the daily costs of dissent, making deviation from state narratives punishable not only through arrest but through exclusion from essential services, employment, and mobility. In such an environment, formal opposition is unlikely to survive, but informal networks of coded communication and underground technological innovation could expand, creating a dual society where repression coexists with hidden circuits of resistance.

Historically, such intense monitoring regimes often produce unintended consequences. The more pervasive and intrusive the surveillance, the more it incentivizes citizens and developers to innovate countermeasures, ranging from obfuscated communication protocols to subtle forms of cultural satire and resistance. As seen with Shadowsocks and subsequent projects, the very act of suppression can cultivate technical expertise and solidarity networks among those targeted. If the state further escalates, resistance may shift from individual acts of circumvention toward collective forms of digital underground culture, diaspora-supported communication hubs, and encrypted parallel ecosystems that remain resilient precisely because they are decentralized and adaptive.

Hypothetical Scenarios and Outcomes of Future Enhanced Surveillance

Externally, an increasingly repressive China risks catalyzing stronger responses from international actors. Multilateral organizations and democratic states may impose stricter technology export controls, sanctions on surveillance vendors, or coordinated support for civil-society circumvention efforts. At the same time, authoritarian-aligned states could take China’s model as a green light to expand their own controls, accelerating the Balkanization of the global internet. The result would be a sharper divide between “sovereign internets” that normalize repression and open networks that champion access, placing global institutions in a prolonged struggle over which model defines the standards of international governance.

The paradox, then, is that China’s tightening grip may secure short-term regime resilience at home while sowing the seeds of longer-term instability and resistance. As surveillance deepens, so too does the risk of overreach, where hyper-control undermines legitimacy and drives innovation in circumvention. On the world stage, Beijing’s hardening model could accelerate geopolitical polarization, forcing states to choose between integration into China’s censored, state-mediated sphere or alignment with more open, contested global frameworks. In both cases, the ultimate outcome is not stability, but fragility, a digital order defined less by uniform control than by the ceaseless negotiation between repression and resistance.

Conclusion

The Great Firewall is not just an internet control system, it is a pillar of China’s broader authoritarian toolkit. Its effectiveness lies in its quiet integration into daily digital life, shaping what can be seen, shared, or even imagined by hundreds of millions of citizens. Unlike blunt instruments of repression, the firewall functions with subtlety: it restricts choice by removing foreign competitors, embeds surveillance into domestic platforms, and fosters a normalized environment where censorship is an unremarkable fact of life. In this sense, the GFW is less a technical barrier than a lived reality, one that molds behavior and expectations in ways that reinforce the state’s authority.

China’s Authoritarian Toolkit

Its design reflects China’s governing philosophy of centralized control, national data sovereignty, and cyber hegemony. By asserting that information space is equivalent to territorial space, the firewall operationalizes Beijing’s belief that sovereignty extends to the digital domain. The system’s modular architecture, spanning deep packet inspection, SNI filtering, proxy interception, and state-managed content platforms,  embodies a deliberate strategy to consolidate both power and legitimacy. It is not merely defensive but expansive: a mechanism for shaping global discourse, setting technical standards, and projecting influence abroad through the export of both infrastructure and ideology.

The evidence parsed from this leak lays bare the breadth and ambition of that vision. At home, the firewall enforces compliance and blunts dissent, ensuring that political stability is reinforced through technological design. Abroad, it provides a model for regimes seeking to replicate China’s balance of control and growth, creating a coalition of states aligned around the principles of cyber sovereignty. Taken together, the GFW is less an isolated technology than it is a strategic doctrine, one that defines China’s path toward digital authoritarianism and seeks to normalize it as a global standard.

Learn More
Research
Inside the Great Firewall Part 2: Technical Infrastructure

See the Great Firewall's technical blueprint. DomainTools Investigations details the TSG core, packet interception methods, and routines that detect tools like V2Ray/Psiphon.

Summary

This second installment in our series on the Great Firewall of China (GFW) focuses on the intricate technical infrastructure, operational logic, and strategic design underpinning China’s censorship ecosystem. Drawing from over 7,000 files in the 500GB GFW data dump, including internal spreadsheets, Visio network diagrams, packet captures, and metadata-rich control logs, this analysis offers an unprecedented reconstruction of the surveillance architecture at the heart of China's digital control apparatus.

At the core is the Traffic Secure Gateway (TSG) system: a modular, exportable DPI platform capable of application-layer proxying, SSL/TLS interception, and centralized policy enforcement. Designed with scale in mind, TSG is deployed across both national ISP backbones and regional access points, working in tandem with centralized command hubs such as the YGN Center. Integration with tools like Cyber Narrator, a suspected GFW dashboard, enables real-time session inspection, keyword flagging, and ruleset propagation across decentralized enforcement nodes.

Filtering is layered: SNI-based TLS detection isolates encrypted circumvention traffic (e.g., Psiphon, Shadowsocks, V2Ray), while URL, host header, and DNS hijack strategies block, redirect, or monitor suspect endpoints. Logs extracted from Redis telemetry, gohangout sessions, and custom firewall agents reveal fine-grained behavioral fingerprinting, tying user sessions to device IDs, session states, and remote IP patterns in near real time. The system also captures malformed packets, port scan anomalies, and misconfigured mirrors, supporting active countermeasure deployment through automated probe and reset mechanisms.

From spreadsheets detailing app endpoint behavior, user monitoring intervals, and hardware configurations to blueprint files illustrating node relationships and control flows, the data illustrates a highly centralized yet distributed architecture, built on cooperation between state-run ISPs, telecom vendors, university research labs, policy-design entities like the NCSC (National Counterintelligence and Security Center) and teams linked to Fang Binxing, the so-called father of the Great Firewall.

This report not only reveals how the GFW works but maps the operational logic, software structure, and institutional alignment driving it, setting the stage for deeper adversarial modeling and red team exploration in future entries.

The Great Firewall’s Purpose

The Great Firewall (GFW) is not merely a tool for filtering websites, it is the centerpiece of China’s digital repression strategy. Its technical architecture is designed not just to block content, but to control the behavior and perceptions of its users. Through mechanisms like Deep Packet Inspection (DPI), Server Name Indication (SNI) filtering, and active probing, the system enforces a state-defined version of reality where politically sensitive terms, foreign platforms, and civil society organizing are algorithmically suppressed. But beyond the code and configurations lies a deeper objective: manufacturing consensus by eliminating dissent before it forms. Through the GFW, the Chinese state does not only censor, it conditions. Platforms are scrubbed of forbidden narratives, while alternatives are either inaccessible or functionally degraded. Algorithms elevate compliant content and bury or erase anything that deviates from sanctioned ideology. This digital architecture is authoritarianism by proxy, embedding the logic of repression into every protocol layer.

At the same time, the GFW plays a crucial role in insulating China from global digital ecosystems. This is not just about keeping foreign narratives out, it is also about shielding Chinese data, behavior, and innovations from foreign intelligence collection and influence. The segmentation of China’s IPv6 networks, DNS sinkholes, and blackholing of VPN traffic represent a strategic decoupling from the global internet. Services like YouTube, Twitter, and Google are not merely blocked for ideological reasons; they are systematically replaced by domestic alternatives (e.g., Weibo, Baidu, Youku) which the state can surveil and manipulate. This creates a bifurcated internet: a “Splinternet” in which Chinese users live in an entirely separate informational universe, one optimized for control and ideological alignment. In this way, the GFW is both sword and shield, censoring the flow of dangerous information and shielding the population from outside influence, while enabling precise surveillance through data centralization and metadata capture. We will cover more on these issues in part three of this series on the Great Firewall; Inside The Great Firewall Part 3: Geopolitical and Societal Ramifications.

Vendor Integration: Building the Hardware and Software Foundations of the Great Firewall

The Great Firewall (GFW) is not a single product built by one agency; it is a distributed ecosystem of hardware, firmware, and software contributed by dozens of Chinese technology companies, each providing specialized modules under the supervision of state ministries. While telecommunications giants like China Telecom, China Unicom, and China Mobile operate the backbone infrastructure, the technical scaffolding of the firewall is delivered by a tightly knit network of trusted vendors and research labs. These vendors supply the routers, DPI (Deep Packet Inspection) cards, cryptographic modules, firmware updates, and orchestration platforms that allow the GFW to adapt to new protocols, scale across regions, and enforce rules at both the packet and behavioral levels.

Vendor Map

One illustrative example from the leaked data is A Hamson Technology Co., Ltd., a company specializing in trusted computing, secure CPUs, cryptographic chips, and embedded operating systems. Corporate materials show that A Hamson counts among its customers the People’s Bank of China, State Grid, telecom carriers, and the Ministry of Public Security, all organizations appearing repeatedly in the metadata and spreadsheets of the GFW dataset. This vendor’s expertise in secure embedded systems and cryptographic modules aligns closely with what is visible in the leak: router firmware customized for keyword filtering, MAAT logs referencing embedded modules, and OA spreadsheets documenting device-level “责任人” (responsible person) fields for trusted platform modules. Such vendors effectively build the “trusted endpoints” of the GFW, routers, DPI blades, and gateways that are not just network devices but active surveillance nodes, capable of memory inspection, SNI fingerprinting, and remote policy injection.

Beyond A Hamson, the dataset also references vendors like Venustech, Topsec, and Huaxin, each of which has long been suspected of Ministry of State Security (MSS) affiliation. These firms provide everything from traffic shaping algorithms to exportable control interfaces and smart gateway solutions, which can be adapted for both domestic censorship and overseas “cyber sovereignty” projects. By coordinating multiple vendors under unified policy frameworks, the Chinese state achieves two objectives simultaneously: it keeps censorship infrastructure modular and upgradable, and it insulates the core policy apparatus from direct exposure by dispersing technical tasks to “private” firms under national security mandates. 

Logic Map

This structure explains the compartmentalized spreadsheets and Visio maps in the leak, regional operators work with vendor-supplied devices and dashboards but do not see the full system; vendors deliver modules that comply with MSS or MIIT standards without controlling overall policy. Together, this forms a state-industrial censorship complex that blends the agility of commercial R&D with the reach of government enforcement.

Core Technical Components

The Great Firewall (GFW) operates as a modular and hierarchical censorship system combining centrally managed orchestration with regionally distributed enforcement nodes. Its architecture, as revealed by internal logs and configuration schemas, revolves around dynamic packet inspection, traffic shaping, and fingerprint-based blocking, executed across both internet backbone infrastructure and local telecom gateways. At the core of this system lie Deep Packet Inspection (DPI) modules, which process TCP streams in real-time to extract HTTP headers, inspect TLS handshakes, and apply keyword filtering. These modules enforce protocol-aware blocking, often dynamically reacting to new patterns of encrypted circumvention traffic. Telemetry from MAAT (Monitoring and Analysis Audit Toolkit) exports and Gohangout logs show that DPI modules interface directly with Redis-backed rule engines to push immediate session resets or trigger stream flags. The presence of advanced JA3 and SNI fingerprinting, evidenced by log extracts matching V2Ray and Psiphon, demonstrates the GFW’s ability to identify encrypted channels even when domain information is obfuscated.

Large scale map pieced together of the Great Firewall from documentation within the dump

Additional files, including firewall monitoring exports and BGP route tables, indicate use of BGP prefix injection and routing hijacks, especially in cases of sinkhole or honeyport deployment. Sinkhole coordination appears distributed across regional telecom nodes, as seen in logs tied to "路由下发" (route issuance). IPv6 traffic is not exempt; spreadsheets such as “境内谷歌IPv6地址段” list specific address segments under active inspection, suggesting asymmetric routing filters or targeted isolation tactics. Finally, endpoint fingerprinting and active probing are routine: .vid telemetry exports show automated DNS/TLS/HTTP queries launched against suspected VPN exit nodes, with results fed into classification systems or further flagged for human review. This automation, spread across regionally deployed scanning agents, highlights a highly adaptive censorship strategy, one capable of matching user behavior to packet behavior in near real-time.

Monitoring and Logging Systems

The monitoring and logging infrastructure of the Great Firewall (GFW) is designed for pervasive visibility, continuous telemetry, and real-time policy enforcement. Key components include MAAT (Modular Automated Analysis Tool), Gohangout (a high-performance log processing framework), and Redis (a high-throughput in-memory data structure store), particularly the variant identified in logs as sd-redis. System logs such as firewall.sd.maat.status.txt capture status messages from firewalls across deployment nodes, indicating active polling of system states, service health, and traffic patterns. Meanwhile, MAAT acts as a central log aggregator and decision engine, ingesting stream data to feed classification engines. Gohangout configurations point to regex-based pattern extraction of domain names and behavior-triggered tags, likely used for classifying traffic by threat or censorship priority. Redis, via scripts like sd-redis-cli-info.txt, reveals in-memory statistics used for measuring response times, anomaly spikes, and user-session correlation.

More granular insights emerging from SQL-based telemetry indicate the GFW taps directly into production-level application telemetry, not just edge packet flows. This means that the system has visibility into how users are interacting with services in real-time, including authentication failures, long page loads, or forbidden response codes. These signals are likely used to dynamically update blacklists and whitelists, which are crucial components in filtering decisions. Blacklists identify VPN exit nodes, encrypted tunnel endpoints, and known circumvention platforms like Psiphon or V2Ray, while whitelists allow permitted services or government-approved content to flow without interference. Updates to these lists are driven by anomaly detection from the logs, matching both metadata (e.g., JA3/TLS fingerprints) and behavioral anomalies (e.g., repeated failed DNS queries or non-standard TLS extensions). This constant feedback loop demonstrates how the GFW is not just reactive but built for adaptive enforcement based on real-world usage patterns.

Endpoint and Device Mapping

One of the most revealing aspects of the Great Firewall (GFW) leak is the explicit linkage between physical infrastructure and the control logic that drives censorship operations. By cross-referencing internal spreadsheets along with telemetry logs from MAAT (Modular Application Audit Telemetry), we’ve reconstructed granular models that map the physical topology of surveillance networks to the logical flow of filtering and monitoring policies. Graphviz-based visualizations built from this data show how data packets are routed through a hierarchy of hardware, from edge-facing routers at telecom interchanges to midstream relays and deep packet inspection (DPI) modules. These DPI systems act as the primary content-filtering engines, enforcing keyword blacklists and TLS (Transport Layer Security) fingerprint-based rules. The data also identifies specific traffic redirection mechanisms, like sinkhole destinations, BGP (Border Gateway Protocol) rerouting triggers, and load-balancing scripts that dynamically respond to policy hits, suggesting an adaptive, programmable censorship environment.

What makes this infrastructure exceptionally traceable is the metadata present in device tracking sheets. Fields such as 设备类型 (Device Type), IP地址 (IP Address), 带宽 (Bandwidth), 使用率 (Usage Rate), and 责任人 (Responsible Party) expose a highly structured assignment of surveillance functions to individual device nodes and their regional operators. For example, specific router and relay MAC (Media Access Control) addresses are associated with application-layer inspection tasks or DNS query interception, depending on their role in the broader hierarchy. In tandem, OA (Office Automation) service logs and deployment documents indicate a centralized remote configuration push capability, allowing administrators in Beijing or provincial control centers to dispatch policy changes or firmware updates directly to edge units across the country. This strongly implies the presence of a secure command-and-control orchestration layer built atop LDAP-authenticated dashboards, with remote agents capable of rule enforcement and update ingestion in near real-time. The entire apparatus, as described in these files, operates as a tightly integrated censorship-industrial network with both technical and bureaucratic chains of command.

Behavioral Prediction Engines: Predictive Enforcement at National Scale

One of the most revealing discoveries from the leaked GFW dataset is the use of behavioral prediction systems that go beyond static rule enforcement. Evidence from application-layer sketch logs, memory and query telemetry and endpoint capture systems suggests the existence of real-time statistical baselining tools built to flag, and even act on, traffic that deviates from normal patterns before it explicitly violates any censorship policies.

This diagram shows the flow from session initiation through telemetry capture (CPU usage, memory, port activity, TLS parameters), then into the Redis-based MAAT logging system, which performs baseline comparisons against historical session profiles. Based on deviation and behavioral patterns, the session is assigned a risk score, which informs the enforcement logic, ranging from passive allowance to rerouting for deeper inspection or full termination.

These prediction mechanisms appear tightly integrated into the MAAT subsystem, where per-user session profiles are maintained and continuously compared against historical baselines. When a session exhibits abnormal latency, memory footprint, or access patterns, such as extended encrypted sessions, unexpected TLS version negotiation, or traffic bursts to unclassified IPs, the system preemptively routes the session through enhanced inspection modules, or terminates it altogether. This is done via a combination of Redis-based anomaly detectors, custom flagging in slow SQL query tables, and policy propagation recorded in MAAT static log sheets.

Notably, the system doesn’t only act after detection. For example, users opening encrypted proxies such as Shadowsocks or V2Ray may experience injection of failure responses or artificial latency even before their SNI or packet signatures match known blacklists. This illustrates that the GFW is not simply reactive, it is predictive. By monitoring systemic telemetry (CPU stats, session duration, port stability, TLS behavior), the firewall infers which sessions are likely to be circumvention attempts and flags them before content is even exchanged.

In essence, this subsystem makes the GFW function as a national-scale anomaly detection engine, assigning implicit trust scores to sessions in real time, and adapting its inspection depth accordingly. This significantly raises the bar for circumvention tool developers, as evading detection now requires mimicking not only protocol signatures but behavioral baselines, making tools like Psiphon or Lantern more vulnerable to dynamic fingerprinting.

Modular App Fingerprinting and Decision Systems

One of the most revealing components in the leaked dataset is the presence of a modular, multi-layered application fingerprinting system, which underpins much of the Great Firewall’s real-time traffic classification and enforcement logic. This system is not simply reliant on domain blacklists or static protocol rules but employs a dynamic, pluggable architecture where different modules, working in tandem, evaluate attributes of encrypted and plaintext traffic. The system performs deep traffic inspection based on JA3 TLS fingerprints (a method of profiling TLS client handshakes), Server Name Indication (SNI) strings, DNS query patterns, packet timing, and destination port behavior. Multiple heuristic layers are involved, where traffic is matched against known circumvention tools like Psiphon, Shadowsocks, and V2Ray, as well as commercial proxies and enterprise VPN suites.

The GFW’s fingerprinting pipeline does not stop at static rule matches. Once traffic flows are parsed by protocol modules, they are routed through behavioral filters that assess timing, packet size variability, and entropy characteristics. These traits are then scored by a lightweight machine learning classifier which, as seen in logs and decision outputs, assigns a confidence level to the classification. Depending on this confidence score, the decision engine passes traffic, flags it for review, or immediately disrupts the connection. This adaptive model, visible in both .maat telemetry and control command logs, suggests that the GFW does not operate purely on static lists, but instead evolves in near-real time by observing patterns and feeding results into training datasets. As a result, circumvention tools face a constantly shifting defensive surface, requiring continuous adaptation to avoid detection.

Decentralized Command Queues and Update Propagation

Another advanced feature uncovered in the dataset is the GFW’s tiered command-and-control architecture, which utilizes decentralized command queues to propagate filtering rules and scan directives and session control policies to regional enforcement nodes. This structure is not strictly top-down, but instead reflects a hub-and-spoke model whereby provincial or municipal GFW agents synchronize with national control hubs, receiving filtering updates while also reporting telemetry and detection feedback upstream. Evidence of this architecture is found in the spreadsheets and text files, which show user roles, scheduled update logs, and endpoint classifications across different administrative regions (e.g., Hebei, Guangdong, Shandong).

Decentralized command queue and update propagation architecture

Update propagation mechanisms leverage remote configuration push systems, likely built atop web-based dashboards and LDAP-authenticated portals. These dashboards, visible in screenshot metadata and firewall controller logs, allow mid-tier administrators to schedule specific control flows, like blacklisting domains, injecting TCP RST packets, or initiating SNI-based filtering routines, targeted to regionally scoped IP ranges. Importantly, logs document queue flushing events and propagation success messages, indicating that rule updates are both time-sensitive and segmented by endpoint type. This modular push architecture ensures that detection heuristics and filtering capabilities can be deployed asymmetrically, tailored to regional priorities, while maintaining coherence across the national censorship system. It reflects a careful balance between operational flexibility and central control.

TLS Fingerprinting and Misclassification Errors

One of the more subtle yet technically revealing aspects of the Great Firewall (GFW) uncovered in the dataset is its heavy reliance on TLS fingerprinting mechanisms, including SNI (Server Name Indication) filtering and JA3 hashing. These techniques allow the system to classify encrypted traffic streams based on patterns in the TLS handshake process without decrypting content. The presence of logs and spreadsheets detailing SNI strings, matched fingerprints, and decision rules indicates that GFW operators are deploying modern passive fingerprinting to identify circumvention tools such as V2Ray, Shadowsocks, and Psiphon, even when encryption obfuscates content.

However, the sophistication of this fingerprinting is limited by its deterministic nature. Probe logs and several domain block tables demonstrate that the firewall infrastructure occasionally misclassifies benign traffic, particularly when updates to JA3-based signatures lag behind app version changes or new cipher suite deployments. Several documented instances show IP addresses or domain names related to major cloud providers like AWS or Google Cloud being blackholed or scanned due to signature collisions with VPN protocols. These “false positives” result in degraded user experience, unjustified blocking of non-malicious content, and in some cases, traffic rerouting to sinkholes.

The logs also show evidence of manual overrides or rule exceptions being implemented in response to these false positives, particularly in files documenting snapshot telemetry or slow query logs. This suggests that while the GFW employs sophisticated fingerprinting techniques, its architecture still requires human intervention to fine-tune classifications and mitigate over-blocking. These observations speak to the brittle nature of relying on opaque machine-learned or static TLS fingerprints at scale, especially when interacting with a fast-evolving internet ecosystem. In practice, the GFW’s fingerprinting capabilities walk a tightrope between aggressive censorship and functional collateral damage, revealing exploitable pressure points for both adversarial red teams and policy advocates.

Protocol Deviation Handling and Anomaly Isolation

Another subtle yet technically sophisticated component set of the (GFW) is its capability to detect and respond to protocol deviations, instances where data flows do not conform to expected standards of HTTP, TLS, DNS, or QUIC traffic. These deviations are typically indicators of encrypted tunneling, obfuscation frameworks, or non-standard clients used for circumvention. The leaked telemetry logs, configuration spreadsheets, and packet inspection schemas provide evidence that the GFW uses a multi-layered response strategy against these anomalies.

At the first stage, stateful inspection engines scan for malformed packet structures, mismatched content-length headers, improper TLS handshake sequences, and DNS replies with unusual TTL values. Tools such as firewall.sd.maat.status.txt, slow query logs, and Redis-backed memory logs show that non-compliant behaviors are tagged with metadata flags like PROTO_DEVIATE, NONSTD_HEADER, or QUIC_FAULT. These sessions are then passed into either temporary quarantine routes, such as blackhole redirect IPs, or passed along for active probing to test for evasive tunneling behavior.

The second stage involves traffic replay and anomaly simulation, where the GFW replicates offending traffic patterns and injects them into sandboxed environments to confirm whether the payload corresponds to obfuscated VPNs, HTTP tunnels, or unauthorized encryption schemes. Logs document timed replay payloads and outbound test probes using crafted TLS or DNS packets. Some deviations are further escalated to manual triage teams or flagged in Graphviz-style flow control diagrams embedded in .vsd Visio files.

This protocol deviation handling system showcases not only the depth of the GFW’s reactive controls but also its ability to learn from emergent behavior, update heuristics dynamically, and enforce policy not just on known bad domains or IPs, but on the shape and rhythm of communication patterns themselves. This makes circumvention more difficult, as developers must now account for not only static blocklists but also behavioral anomaly detection systems embedded within China’s censorship infrastructure.

Integration of Surveillance Platforms and Data Fusion

One of the more formidable aspects of the Great Firewall’s (GFW) evolution is the integration of real-time network filtering with broader surveillance ecosystems. The data assessed from the 500GB breach confirms that firewall telemetry, such as flagged sessions, packet capture metadata, and access anomalies, is not siloed within the GFW apparatus. Instead, it feeds into centralized repositories where it is correlated with endpoint identity, system behavior, application telemetry, and even social profiling signals.

Logs analyzed from MAAT, sd-redis, and snapshot exports show distinct identifiers being used across firewall records, system monitors, and application-layer access logs. These identifiers, most notably persistent UUIDs, IMEI/IMSI hashes, and partial SSO tokens, indicate cross-platform tagging, likely used for behavioral correlation. This is supported by spreadsheets listing VPN server hits alongside cached user session data, and references to location-aware scanning logic embedded in regional configuration files. The presence of fields like 责任人 (responsible person) and user-role tags in spreadsheet metadata further indicates that system usage is attributable, not anonymized.

What emerges is a picture of data fusion at scale, where censorship enforcement is not merely technical, but linked to identity and reputation systems. It is likely that flagged activity within the GFW can escalate to surveillance review queues in platforms such as Skynet (天网) and Sharp Eyes (雪亮工程), integrating with national security databases. In this architecture, the GFW is not a wall, but a sieve, detecting, classifying, logging, and escalating infractions across bureaucratic and technological layers. The Chinese censorship regime thus operates not only as an information filter, but as a reputational sorting system, linking digital behavior to administrative consequences.

Remote Command Injection and Centralized Control Queues

One of the most significant revelations within the Great Firewall dataset is the use of remote command injection frameworks for real-time policy updates and enforcement. Analysis of the files, combined with metadata from .vsd network maps and firewall.sd.maat.status, suggests that the GFW supports a centralized command-and-control (C2) model for dynamically managing its censorship rules and behavioral triggers.

Unlike static firewall configurations typically associated with traditional network perimeter defense, the GFW employs push-based command execution. These are delivered to regional or localized DPI appliances, surveillance nodes, and edge routers via a tiered orchestration mechanism. The presence of fields like 部署方式 (deployment mode), 指令同步策略(instruction synchronization strategy), and 责任人 (responsible party) in the spreadsheet metadata illustrates a delegated enforcement model, where operators across various provinces and telecom backbones receive and execute filtering updates issued from a central authority, likely situated within Beijing or under Ministry of State Security (MSS) control.

The logs reveal that rule updates are batched and tagged with timestamps, UUIDs, and content categories, such as “VPN,” “sensitive term,” or “foreign platform.” In some cases, these are deployed with rollback triggers and can be toggled based on traffic spikes, public sentiment monitoring, or new circumvention tool detection. Custom scripts also suggest that updates can target infrastructure selectively, for example, only IPv6 subnets within 联通 (Unicom) in a specific region, or only mobile application traffic over TLS 1.3 from certain devices.

In essence, this system is not a passive firewall but a living censorship organism, capable of autonomous adaptation and centrally coordinated behavior modification. These command injection pathways are also likely tied into the metadata-based identity tracking system that feeds into China’s broader surveillance and social credit scoring architectures, ensuring that information control can be tuned at the individual, device, or regional level in real time.

China’s Social Credit Score System and the Great Firewall

The Chinese Social Credit Score System (SCS) is a sprawling, state-coordinated framework designed to promote "trustworthiness" and "moral behavior" among citizens, corporations, and institutions. Rooted in both government regulation and commercial participation, the system aggregates a wide array of behavioral, financial, legal, and social data to assign reputation-based scores to individuals and entities. The system is coordinated by central authorities like the National Development and Reform Commission (NDRC), the People’s Bank of China, and the Ministry of Public Security (MPS), with significant technical input from the Cyberspace Administration of China (CAC). These agencies collect data from legal rulings, bank transactions, police records, and even online activity logs. Citizens with high scores receive benefits such as loan approvals and travel priority, while low scores may lead to travel bans, throttled internet, and social blacklisting. Localized implementations by provincial governments and private corporations, such as Alibaba's Sesame Credit, create further layers of scoring, often blending regulatory enforcement with commercial incentives.

The Social Credit Score System in China Logic Diagram
Technical Logic Diagram of Great Firewall Infrastructure for GFW Social Credit Score System

Within this architecture, the Great Firewall (GFW) acts as a technical enforcement and behavioral surveillance mechanism. Data gathered through DPI (Deep Packet Inspection), TLS interception, domain access logs, and behavioral telemetry is used to infer intent and compliance with state-defined norms. For example, users accessing blocked VPN services, attempting to reach blacklisted content, or demonstrating encrypted communication patterns may be flagged in monitoring systems like MAAT or Gohangout. These logs, in turn, feed into centralized analytics platforms that may update regional or national blacklists. Importantly, this technical data is not just used for censorship, it is increasingly integrated into risk models that feed back into the social credit system. The GFW thus becomes more than a digital barrier; it acts as a behavioral sieve, shaping how trustworthiness is algorithmically defined and enforced across China. This convergence of technical infrastructure and socio-political governance represents a profound fusion of surveillance capitalism and state control, with escalating implications for digital human rights.

Conclusion

The Great Firewall’s architecture is not a singular construct but a federated, modular system that reflects a deeply integrated model of scalable repression and technical precision. Rather than centralized omniscience, the system operates through layered enforcement, with real-time monitoring nodes deployed at key internet exchange points (IXPs), backbone service providers, and regional telecom branches. These nodes feed data into centralized analysis engines and regional control centers, where behavioral patterns, encrypted traffic markers, and protocol anomalies are processed through tools like MAAT, Gohangout, and customized Redis-backed monitoring agents. At the application layer, heuristics detect circumvention behavior ,  such as the use of Psiphon, V2Ray, or Shadowsocks, using techniques like SNI filtering, JA3 fingerprinting, and connection scheduling flags. DNS responses are spoofed or dropped depending on classification rules, while sessions may be hijacked or redirected via sinkholes and TCP reset injections. The underlying telemetry reveals how regional operators execute policies set by central authorities, supported by MSS-linked vendors providing firmware, DPI modules, and command-and-control dashboards.

Despite this sophistication, the leaked data exposed fault lines, including regionally misconfigured mirrors that unintentionally broadcast blacklist UUIDs, and BGP anomalies suggesting overly aggressive routing filters. These lapses highlight both the bureaucratic silos and technical brittleness of enforcing censorship at scale. Nonetheless, the architectural strategy is resilient: it favors redundancy, localized enforcement autonomy, and reactive filtering rather than static rulesets. What emerges is not just a firewall in the traditional sense, but a living ecosystem of algorithmic governance. The next phase of analysis will step beyond the command-line telemetry and log files to examine the broader implications,  the geopolitical consequences of codified information suppression, and the mounting human cost of building a surveillance state at the scale of 1.4 billion people.

APPENDIX: A File List

File list of dump translated from Mandarin

Network Research Report.docx

27712684_attachments_20220419-Zhang Qingfeng-Daily Communication Record.docx

695411_attachments_Phishing Website Detection System Manual.docx

105873423_attachments_20240423 Meeting Minutes - Feedback Version.docx

27712684_attachments_20220420-Wang Meiqi-Daily Communication Minutes.docx

695411_attachments_Appendix 1: University of Chinese Academy of Sciences Graduate Dissertation Midterm Report.docx

105873423_attachments_clearn.docx

27712684_attachments_20220420-Gao Yue-Daily Communication Minutes.docx

695452_attachments_A Method and Device for Multipath TCP Protocol Function Restriction.docx

105873423_attachments_MAAT Regularization Test.docx

27712684_attachments_April 2022 Meeting Minutes.docx

695452_attachments_Explanation on Voluntary Waiver of Remuneration for Work-Related Inventions and Creations (Template)_1.docx

105873423_attachments_MAAT Test String Regularization Test Report.docx

27716205_attachments_【Reference】Departmental Approval.docx

695452_attachments_Amplification of Reflection Attack Detection System Code.doc

105873423_attachments_MAAT Network Flow Processing Configuration Unified Description Framework - Learning Annotation Version.docx

27716205_attachments_Graduate Registration Form.doc

695452_attachments_Amplification of Reflection Attack Detection System Specification.docx

105873423_attachments_Memory Growth Problem Troubleshooting Report 20240326.docx

27716205_attachments_Attachment 1-1: Departmental Approval.docx

695452_attachments_Voluntary Waiver of Invention Benefits Statement (Template).docx

105873423_attachments_Blocking Status Query Program Abnormal Troubleshooting Process.docx

27716205_attachments_Attachment 16: Graduate Registration Form.docx

695452_attachments_Zou Yuting_University of Chinese Academy of Sciences Graduate Thesis Midterm Report.docx

105873423_attachments_Crash Information.docx

27716205_attachments_Attachment 1: Defense Application.doc

695452_attachments_Zou Yuting_University of Chinese Academy of Sciences Graduate Thesis Proposal.docx

105873423_attachments_Document Notes.docx

27716205_attachments_Attachment 21: Scientific Research Achievement Certification Template.docx

695452_attachments_Zou Yuting's Graduation Remarks.docx

105873423_attachments_Source Code Notes.docx

27716205_attachments_Attachment 2: Revision Instructions for Dissertation Revisions after the Pre-Defense.docx

695502_attachments_Regulations on the Management of Mid-term Assessments for Dissertations of the Institute of Information Engineering, Chinese Academy of Sciences (Interim).doc

105873437_attachments_20240423 Meeting Minutes - Feedback Version.docx

27716205_attachments_Attachment 4: Pre-Defense Committee Member Review Form.docx

695502_attachments_Institute of Information Engineering Master's Degree - Class of 2017 - Mid-term Assessment Registration Form - Shang Jingjing.doc

105873437_attachments_clearn.docx

27716205_attachments_Attachment 5: Doctoral Dissertation Pre-Defense Review Form.doc

695502_attachments_Institute of Information Engineering Master's Degree - Class of 2017 - Thesis Proposal - Shang Jingjing_1.doc

105873437_attachments_MAAT Regular Expression Test.docx

27720755_attachments_Attachment 17: Guidance on Writing Standards for Graduate Degree Dissertations at University of Chinese Academy of Sciences.doc

695502_attachments_Institute of Information Engineering Master's Degree - Class of 2017 - Thesis Proposal Registration Form - Shang Jingjing.doc

105873437_attachments_MAAT Test String Regular Expression Test Report.docx

27721697_attachments_Work Introduction.docx

695502_attachments_Government, Enterprise, and University Email System Security Measurement Report.docx

105873437_attachments_MAAT Network Flow Processing Configuration Unified Description Framework - Learning Annotation Version.docx

39127869_attachments_Offline Deployment SENTRY.docx

695502_attachments_Shucun Government Email System Security Measurement Report.docx

105873437_attachments_Memory Growth Troubleshooting Report 20240326.docx

39129077_attachments_OLP-BP User Manual (Dual-Fiber Bidirectional).doc

695502_attachments_Graduation Reflections.docx

105873437_attachments_Blocking Status Query Program Abnormal Troubleshooting Process.docx

39129077_attachments_Optical Protection Diversion Interoperability Instructions - Communication Instructions.docx

695502_attachments_Email Security Extension Protocol Application Analysis.docx

105873437_attachments_Crash Information.docx

39129436_attachments_Compilation Environment.docx

695502_attachments_Network Mail Service Detection System Manual.docx

105873437_attachments_Documentation Notes.docx

39129436_attachments_Video Recording.doc

695502_attachments_Design Documents.docx

105873437_attachments_Source Code Notes.docx

3.NMS Administrator Manual_V1.0_CH.docx

695502_attachments_Mail Repository Table.docx

106104952_attachments_Psiphon Phenomenon Analysis.docx

40.166 Crash Investigation Document.docx

695502_attachments_Mail Service Provider Reputation Assessment System Manual.docx

106105540_attachments_IP Traceability Report.docx

44990672_attachments_2022.04 Daily Communication Minutes.docx

695502_attachments_Email Service Provider Reputation Assessment System Source Code.docx

106105561_attachments_DPI Benchmark Test Plan.docx

44990672_attachments_20220507-Zhang Qingfeng-Daily Communication Record.docx

695502_attachments_Email System Advanced Features Description 20191022.docx

106106530_attachments_Lesson Plan and Answer Sheet.docx

44990672_attachments_20220520-Wang Meiqi-Daily Communication Minutes.docx

695678_attachments_BillGates, Mayday, and XorDDos Family Traffic Characteristics.docx

106106532_attachments_Fang Ban - Lesson Plan and Answer Sheet - Zhang Linkang.docx

44990672_attachments_20220520 - Gao Yue - Daily Communication Minutes.docx

695678_attachments_Kafka Setup Process.docx

106106535_attachments_Zhang Linkang - Lesson Plan and Answer Sheet.docx

44990672_attachments_April 2022 Meeting Minutes.docx

695678_attachments_Institute of Information Engineering Master's Degree - Class of 2017 - Thesis Proposal Registration Form - Wang Yu.doc

106107220_attachments_Defense Record.docx

44990894_attachments_(Reference Template) Appendix 9: Revision Notes for the Dissertation after Review.docx

695678_attachments_Notice on Voluntary Waiver of Remuneration for Work-Related Inventions and Creations.docx

106107951_attachments_Defense Record.docx

44990894_attachments_Attachment 9: Revision Notes for the Dissertation After Review.docx

695678_attachments_Malicious Service IPv6 Address Discovery and Assessment System Manual.docx

106109482_attachments_Li Zhuo - Defense Opinion.docx

44992427_attachments_Resolution of the Second Research Laboratory Dissertation Defense - Reference Template (including PhD and Master's) 2022.doc

695678_attachments_Malicious Service IPv6 Address Discovery and Assessment System Source Code.docx

106109964_attachments_Advantages of Upgrading_v2.doc

44992427_attachments_Attachment 14: Defense Committee Resolution and Voting Results (Reference Template).docx

695678_attachments_Graduation Reflections - Wang Yu.docx

106109964_attachments_Advantages of Text Upgrade.doc

44992427_attachments_Attachment 2: Defense Committee Resolution (Blank).docx

695678_attachments_Attachment 1: Midterm Report of Graduate Dissertation from University of Chinese Academy of Sciences - Wang Yu.docx

106109974_attachments_Kafka Component Parameters and Frequently Asked Questions.docx

47251516_attachments_2022.05 Daily Communication Minutes.docx

695678_attachments_Attachment 2: Midterm Assessment Registration Form for Graduate Dissertation from University of Chinese Academy of Sciences - Wang Yu.doc

106110644_attachments_Reflections on the Encrypted Video Content Recognition Test Invitational Competition - TikTok - Yang Chen.docx

47251516_attachments_20220601 - Zhang Qingfeng - Daily Communication Records.docx

695874_attachments_CMAF Research and Analysis.docx

106110644_attachments_Competition Exchange - Zhang Xiyuan.docx

47251516_attachments_20220620-Wang Meiqi-Daily Communication Minutes.docx

695874_attachments_Regulations on the Management of Mid-term Assessments of Degree Thesis of the Institute of Information Engineering, Chinese Academy of Sciences (Interim).doc

106110644_attachments_Competition Replay_Tang Weitao_1.docx

47253181_attachments_2022-06-01 Pre-Application Kick-off Meeting and Technical Exchange Meeting.docx

695874_attachments_Institute of Information Engineering Master's Degree - Class of 2017 - Mid-term Assessment Registration Form - Shang Jingjing.doc

106110644_attachments_Shenzhen Competition Replay_Cui Chenyang_1.docx

48042345_attachments_Spring Boot HTTPS.docx

695874_attachments_Institute of Information Engineering Master's Class of 2017 - Project Proposal - Shang Jingjing.doc

106111885_attachments_User Manual.docx

49185937_attachments_20220620 - Wang Meiqi - Daily Communication Minutes.docx

695874_attachments_Institute of Information Engineering Master's Class of 2017 - Project Proposal - Liu Youting.doc

106112252_attachments_CRDT Research.docx

49185937_attachments_2022.06 Daily Communication Minutes.docx

695874_attachments_Institute of Information Engineering Master's Class of 2017 - Project Proposal Registration Form - Shang Jingjing.doc

106113119_attachments_qps Test bind9 vscoredns.docx

49185937_attachments_20220701-Zhang Qingfeng-Daily Communication Record.docx

695874_attachments_Institute of Information Engineering Master's Degree-2017-Project Proposal Registration Form-Liu Youting.doc

106113320_attachments_Flink Troubleshooting Manual.doc

49185937_attachments_20220701-Wang Meiqi-Daily Communication Record.docx

695874_attachments_Government, Enterprise, and University Email System Security Measurement Report.docx

106113349_attachments_Apache Doris Configuration Items

106113349_attachments_Application Notes.docx

49185937_attachments_April 2022 Meeting Minutes.docx

695874_attachments_Shucun Government Email System Security Measurement Report.docx

106113365_attachments_Offline Deployment SENTRY.docx

49186474_attachments_20220715 - Gao Yue - Daily Communication Minutes.docx

695874_attachments_Graduation Reflections.docx

106113392_attachments_Optical Protection Diversion Interoperability Instructions - Communication Instructions.docx

49186474_attachments_20220715 - Zhang Qingfeng - Daily Communication Record.docx

695874_attachments_Email Security Extension Protocol Application Analysis.docx

106113394_attachments_Compilation Environment.docx

49186474_attachments_20220715 - Wang Meiqi - Daily Communication Record.docx

695874_attachments_Network Mail Service Detection System Manual.docx

106113399_attachments_Video Recording.doc

49186474_attachments_April 2022 Meeting Minutes.docx

695874_attachments_Design Documents.docx

106113405_attachments_NMS Administrator Manual_V1.0_CH.docx

49187953_attachments_20220801 - Gao Yue - Daily Communication Minutes.docx

695874_attachments_Mail Repository Table.docx

106113406_attachments_Psiphon Phenomenon Analysis.docx

49187953_attachments_20220801 - Zhang Qingfeng - Daily Communication Record.docx

695874_attachments_Mail Service Provider Reputation Assessment System Manual.docx

106113411_attachments_IP Traceability Report.docx

49187953_attachments_20220801 - Wang Meiqi - Daily Communication Record.docx

695874_attachments_Email Service Provider Reputation Assessment System Source Code.docx

106113416_attachments_DPI Benchmark Test Plan.docx

49187953_attachments_April 2022 Meeting Minutes.docx

695874_attachments_Email System Advanced Features Description 20191022.docx

106113421_attachments_Lesson Plan and Answer Sheet.docx

49189242_attachments_20220815 - Gao Yue - Daily Communication Minutes.docx

695874_attachments_BillGates, Mayday, and XorDDos Family Traffic Characteristics.docx

106113422_attachments_Fang Ban - Lesson Plan and Answer Sheet - Zhang Linkang.docx

49189242_attachments_20220815 - Zhang Qingfeng - Daily Communication Record.docx

695874_attachments_Kafka Setup Process.docx

106113423_attachments_Zhang Linkang - Lesson Plan and Answer Sheet.docx

49189242_attachments_20220815 - Wang Meiqi - Daily Communication Record.docx

695874_attachments_Institute of Information Engineering Master's Degree - Class of 2017 - Thesis Proposal Registration Form - Wang Yu.doc

106113424_attachments_Defense Record.docx

49189242_attachments_April 2022 Meeting Minutes.docx

695874_attachments_Notice on Voluntary Waiver of Remuneration for Work-Related Inventions and Creations.docx

106113425_attachments_Defense Record.docx

49190679_attachments_20220901 - Gao Yue - Daily Communication Minutes.docx

695874_attachments_Malicious Service IPv6 Address Discovery and Assessment System Manual.docx

106113426_attachments_Li Zhuo - Defense Opinion.docx

49190679_attachments_20220901 - Zhang Qingfeng - Daily Communication Record.docx

695874_attachments_Malicious Service IPv6 Address Discovery and Assessment System Source Code.docx

106113427_attachments_Advantages of Upgrading_v2.doc

49190679_attachments_20220901 - Wang Meiqi - Daily Communication Record.docx

695874_attachments_Graduation Reflections - Wang Yu.docx

106113428_attachments_Advantages of Text Upgrade.doc

49190679_attachments_April 2022 Meeting Minutes.docx

695874_attachments_Attachment 1: Midterm Report of Graduate Dissertation from University of Chinese Academy of Sciences - Wang Yu.docx

106113429_attachments_Kafka Component Parameters and Frequently Asked Questions.docx

49192059_attachments_20220915 - Gao Yue - Daily Communication Minutes.docx

695874_attachments_Attachment 2: Midterm Assessment Registration Form for Graduate Dissertation from University of Chinese Academy of Sciences - Wang Yu.doc

106113430_attachments_Reflections on the Encrypted Video Content Recognition Test Invitational Competition - TikTok - Yang Chen.docx

49192059_attachments_20220915 - Zhang Qingfeng - Daily Communication Record.docx

695874_attachments_CMAF Research and Analysis.docx

106113431_attachments_Competition Exchange - Zhang Xiyuan.docx

49192059_attachments_20220915 - Wang Meiqi - Daily Communication Record.docx

695874_attachments_Regulations on the Management of Mid-term Assessments of Degree Thesis of the Institute of Information Engineering, Chinese Academy of Sciences (Interim).doc

106113432_attachments_Competition Replay_Tang Weitao_1.docx

49192059_attachments_April 2022 Meeting Minutes.docx

695874_attachments_Institute of Information Engineering Master's Degree - Class of 2017 - Mid-term Assessment Registration Form - Shang Jingjing.doc

106113433_attachments_Shenzhen Competition Replay_Cui Chenyang_1.docx

49193421_attachments_20221001 - Gao Yue - Daily Communication Minutes.docx

695874_attachments_Institute of Information Engineering Master's Class of 2017 - Project Proposal - Shang Jingjing.doc

106113434_attachments_User Manual.docx

49193421_attachments_20221001 - Zhang Qingfeng - Daily Communication Record.docx

695874_attachments_Institute of Information Engineering Master's Class of 2017 - Project Proposal - Liu Youting.doc

106113435_attachments_CRDT Research.docx

49193421_attachments_20221001 - Wang Meiqi - Daily Communication Record.docx

695874_attachments_Institute of Information Engineering Master's Class of 2017 - Project Proposal Registration Form - Shang Jingjing.doc

106113436_attachments_qps Test bind9 vscoredns.docx

49193421_attachments_April 2022 Meeting Minutes.docx

695874_attachments_Institute of Information Engineering Master's Degree-2017-Project Proposal Registration Form-Liu Youting.doc

106113437_attachments_Flink Troubleshooting Manual.doc

49194719_attachments_20221015 - Gao Yue - Daily Communication Minutes.docx

695874_attachments_Government, Enterprise, and University Email System Security Measurement Report.docx

106113438_attachments_Application Notes.docx

49194719_attachments_20221015 - Zhang Qingfeng - Daily Communication Record.docx

695874_attachments_Shucun Government Email System Security Measurement Report.docx

Learn More