Check Point Research (CPR) believes a new era of AI-generated malware has begun. VoidLink stands as the first evidently documented case of this era, as a truly advanced malware framework authored almost entirely by artificial intelligence, likely under the direction of a single individual.
Until now, solid evidence of AI-generated malware has primarily been linked to inexperienced threat actors, as in the case of FunkSec, or to malware that largely mirrored the functionality of existing open-source malware tools. VoidLink is the first evidence based case that shows how dangerous AI can become in the hands of more capable malware developers.
Operational security (OPSEC) failures by the VoidLink developer exposed development artifacts. These materials provide clear evidence that the malware was produced predominantly through AI-driven development, reaching a first functional implant in under a week.
This case highlights the dangers of how AI can enable a single actor to plan, build, and iterate complex systems at a pace that previously required coordinated teams, ultimately normalizing high-complexity attacks, that previously would only originate from high-resource threat actors.
From a methodology perspective, the actor used the model beyond coding, adopting an approach called Spec Driven Development (SDD), first tasking it to generate a structured, multi-team development plan with sprint schedules, specifications, and deliverables. That documentation was then repurposed as the execution blueprint, which the model likely followed to implement, iterate, and test the malware end-to-end.
Introduction
When we first encountered VoidLink, we were struck by its level of maturity, high functionality, efficient architecture, and flexible, dynamic operating model. Employing technologies like eBPF and LKM rootkits and dedicated modules for cloud enumeration and post-exploitation in container environments, this unusual piece of malware seemed to be a larger development effort by an advanced actor. As we continued tracking it, we watched it evolve in near real time, rapidly transforming from what appeared to be a functional development build into a comprehensive, modular framework. Over time, additional components were introduced, command-and-control infrastructure was established, and the project accelerated toward a full-fledged operational platform.
In parallel, we monitored the actor’s supporting infrastructure and identified multiple operational security (OPSEC) failures. These missteps exposed substantial portions of VoidLink’s internal materials, including documentation, source code, and project components. The leaks also contained detailed planning artifacts: sprints, design ideas, and timelines for three distinct internal “teams,” spanning more than 30 weeks of planned development. At face value, this level of structure suggested a well-resourced organization investing heavily in engineering and operationalization.
However, the sprint timeline did not align with our observations. We had directly witnessed the malware’s capabilities expanding far faster than the documentation implied. Deeper investigation revealed clear artifacts indicating that the development plan itself was generated and orchestrated by an AI model and that it was likely used as the blueprint to build, execute, and test the framework. Because AI-produced documentation is typically thorough, many of these artifacts were timestamped and unusually revealing. They show how, in less than a week, a single individual likely drove VoidLink from concept to a working, evolving reality.
As this narrative comes into focus, it turns long-discussed concerns about AI-enabled malware from theory into practice. VoidLink, implemented to a notably high engineering standard, demonstrates how rapidly sophisticated offensive capability can be produced, and how dangerous AI becomes when placed in the wrong hands.
AI-Crafted Malware: Creation and Methodology
The general approach to developing VoidLink can be described as Spec Driven Development (SDD). In this workflow, a developer begins by specifying what they’re building, then creates a plan, breaks that plan into tasks, and only then allows an agent to implement it.
High-level overview of the VoidLink Project
Artifacts from VoidLink’s development environment suggest that the developer followed a similar pattern: first defining the project based on general guidelines and an existing codebase, then having the AI translate those guidelines into an architecture and build a plan across three separate teams, paired with strict coding guidelines and constraints, and only afterward running the agent to execute the implementation.
Project Initialization
VoidLink’s development likely began in late November 2025, when its developer turned to TRAE SOLO, an AI assistant embedded in TRAE, an AI-centric IDE. While we do not have access to the full conversation history, TRAE automatically produces helper files that preserve key portions of the original guidance provided to the model. Those TRAE-generated files appear to have been copied alongside the source code to the threat actor’s server, and later surfaced due to an exposed open directory. This leakage gave us unusually direct visibility into the project’s earliest directives.
In this case, TRAE generated a Chinese-language instruction document. These directives offer a rare window into VoidLink’s early-stage planning and the baseline requirements that set the project in motion. The document is structured as a series of key points:
Chinese
English
Description
目标
Objective
Explicitly instructs the model not to implement code or provide technical details related to adversarial techniques, likely an attempt to navigate or bypass initial model safety constraints (”jailbreak”).
资料获取
Material acquisition
Directs the model to reference an existing file named c2架构.txt (C2 Architecture), which likely contained the seed architecture and design concepts for the C2 platform.
架构梳理
Architecture breakdown
Takes the initial input and decomposes it into discrete components required to build a functional and robust framework.
风险与合规评估
Risk and compliance
Frames the work in terms of legal boundaries and compliance, likely used as a credibility layer and/or an additional attempt to steer the model toward permissive responses.
代码仓库映射
Code repository mapping
Suggests VoidLink was bootstrapped from an existing minimal codebase provided to the model as a starting point, but subsequently rewritten end-to-end.
交付输出
Deliverables
Requests a consolidated output package: an architecture summary, a risk/compliance overview, and a technical roadmap to convert the concept into an operational framework.
下一步
Next Steps
A confirmation from the agent that, once the TXT file is provided, it will proceed to extract it and deliver the relevant information.
This summary of the developer’s initial exchange with the agent suggests the opening directive was not to build VoidLink directly, but to design it around a thin skeleton and produce a concrete execution plan to turn it into a working platform. It remains unclear whether this approach was purely pragmatic, intended to make the process more efficient, or a deliberate “jailbreak” strategy to navigate guardrails early and enable full end-to-end malware development later.
Project Specifications
Beyond the TRAE-generated prompt document, we also uncovered an unusually extensive body of internal planning material: a comprehensive work plan spanning three development teams. Written in Chinese and saved as Markdown (MD) files, the documentation bears all the hallmarks of a Large Language Model (LLM): highly structured, consistently formatted, and exceptionally detailed. Some appear to have been generated as a direct output of the planning request described above.
These documents are laid out in various folders and include sprint schedules, feature breakdowns, coding guidelines, and others, with clear ownership by teams:
The earliest of these documents, timestamped to November 27th, 2025, describes a 20-week sprint plan across three teams: a Core Team (Zig), an Arsenal Team (C), and a Backend Team (Go). The plan is strikingly specific, referencing additional companion files intended to document each sprint in depth. Notably, the initial roadmap also includes a dedicated set of standardization files, prescribing explicit coding conventions and implementation guidelines, effectively a rulebook for how the codebase should be written and maintained.
Translated development plan for three teams: Core, Arsenal and Backend.
A review of the code standardization instructions against the recovered VoidLink source code shows a striking level of alignment. Conventions, structure, and implementation patterns match so closely that it leaves little room for doubt: the codebase was written to those exact instructions.
Code headers as described in the specifications (Left) compared to actual source code (Right)
The source itself, apparently developed according to the documented sprints and coding guidelines, was presented as a 30-week engineering effort, yet appears to have been executed in a dramatically shorter timeframe. One recovered test artifact, timestamped to December 4, a mere week after the project began, indicates that by that date, VoidLink was already functional and had grown to more than 88,000 lines of code. At this point in time, a compiled version of it was already submitted to VirusTotal, marking the beginning of our research.
VoidLink report showing lines of code (Added translations in parentheses)
Generating VoidLink from Scratch
With access to the documentation and specifications of VoidLink and its various sprints, we replicated the workflow using the same TRAE IDE that the developer used (although any frontend for agentic models would work). While TRAE SOLO is only available as a paid product, the regular IDE is sufficient here, as the documentation and design are already available, and the design step can be skipped.
When given the task of implementing the framework described according to the specification in the markdown documentation files sprint by sprint, the model slowly began to generate code that resembled the actual source code of VoidLink in structure and content.
Source tree after the second sprint
By implementing each sprint according to the specified code guidelines, feature lists, and acceptance criteria, and writing tests to validate those, the model quickly implemented the requested code. While the chosen model still influences code quality and overall coding style, the detailed and precise documentation ensures a comparatively high level of reproducibility, as the model has less room for interpretation and strict testing criteria to validate each feature.
Implementing sprint 1 according to the documentation and requirements
The usage of sprints is a helpful pattern for AI code engineering because at the end of each sprint, the developer has a point where code is working and can be committed to a version control repository, which can then act as the restore point if the AI messes up in a later sprint. The developer can then do additional manual testing, refine the specs and documentation, and plan the next sprint. This emulates a lightning-fast SCRUM software engineering team, where the developer acts as the product owner.
Sprint completion log
While testing, integration, and specification refinements are left to the developer, this workflow can offload almost all coding tasks to the model. This results in the rapid development we observed, resembling the efforts of multiple teams of professionals in the pre-agentic-AI era.
Conclusion
Within the rapid advancement of AI technologies, the security community has long anticipated that AI would be a force multiplier for malicious actors. Until now, however, the clearest evidence of AI-driven activity has largely surfaced in lower-sophistication operations, often tied to less experienced threat actors, and has not meaningfully raised the risk beyond regular attacks. VoidLink shifts that baseline: its level of sophistication shows that when AI is in the hands of capable developers, it can materially amplify both the speed and the scale at which serious offensive capability can be produced.
While not a fully AI-orchestrated attack, VoidLink demonstrates that the long-awaited era of sophisticated AI-generated malware has likely begun. In the hands of individual experienced threat actors or malware developers, AI can build sophisticated, stealthy, and stable malware frameworks that resemble those created by sophisticated and experienced threat groups.
Our investigation into VoidLink leaves many open questions, one of them deeply unsettling. We only uncovered its true development story because we had a rare glimpse into the developer’s environment, a visibility we almost never get. Which begs the question: how many other sophisticated malware frameworks out there were built using AI, but left no artifacts to tell?
Additional Credit
We want to acknowledge @huairenWRLD for collaboration, who, following our initial blog post, also investigated VoidLink.
Sicarii is a newly observed RaaS operation that surfaced in late 2025 and has only published 1 claimed victim.
The group explicitly brands itself as Israeli/Jewish, using Hebrew language, historical symbols, and extremist right-wing ideological references not usually seen in financially-motivated ransomware operations.
Underground online activity associated with Sicarii is primarily conducted in Russian, including RaaS recruitment posts and forum engagement.
Hebrew content used by the group appears to be machine-translated or non-native and contains grammatical and semantic errors.
The group’s behavior and messaging diverge from established ransomware practices and raise the possibility of identity manipulation or influence-oriented signaling, rather than a real and mature criminal operation.
The ransomware performs an active geo-fencing check to prevent execution on Israeli systems, an unusual design choice that weakens plausible deniability.
The ransomware’s technical capabilities include data exfiltration, collecting system credentials and network information, check exploitation for Fortinet devices, and encrypt files using AES-GCM and the .sicarii extension.
Introduction
In December 2025, a previously unknown Ransomware-as-a-Service (RaaS) operation calling itself Sicarii began advertising its services across multiple underground platforms. The group’s name references the Sicarii, a 1st-century Jewish assassins group that opposed Roman rule in Judea. From its initial appearance, the Sicarii ransomware group distinguished itself through unusually explicit and persistent use of Israeli and Jewish symbolism in its branding, communications, and malware logic.
Figure 1 – Sicarii Ransomware logo featuring the phrase “The Sicarii Knife” in Hebrew text with the symbol of the Haganah (predecessor to the Israel Defense Forces).
Unlike most financially-motivated ransomware groups, Sicarii overtly claims Israeli or Jewish affiliation. Its visual branding incorporates Hebrew text and the emblem of the historical Jewish paramilitary organization Haganah, while its ransomware selectively avoids executing on systems identified as Israeli. The group further claims ideological motivation rooted in extremist Jewish groups, while simultaneously marketing the operation as profit-driven and offering financial incentives for attacks against Arab or Muslim states.
In this report, Check Point Research (CPR) examines Sicarii’s background and capabilities, outlines its technical characteristics, and highlights a series of anomalies and inconsistencies that complicate attribution and clear understanding who is behind this group. These indicators raise questions regarding the authenticity of the group’s claimed identity and suggest the possibility of performative or false-flag behavior rather than genuine national or ideological alignment.
Technical analysis
While the exact initial access path is still unclear, communications with the group suggest the operator is likely purchasing access to the targeted organizations and not necessarily exploiting them directly.
The ransomware execution begins with an Anti-VM phase that tries to determine whether the malware is running in a real victim environment or inside a sandbox. It performs several environment checks, including virtualization detection. If it concludes it is executing inside a VM, it stops early and displays a decoy MessageBox error: "DirectX failed to initialize memory during runtime, exiting". Next, it enforces single-instance execution by creating a mutex and exiting if the mutex already exists. The ransomware then copies itself to the Temp directory with a random name in the format svchost_{random}.exe
The ransomware tests for Internet connection by attempting to contact the following url 120 times: google.com/generate_204
Figure 2 – Check for internet connection.
After checking connectivity, the ransomware determines if the victim is Israeli by checking:
Is the time zone set to Israel
Does the keyboard layout include Hebrew
Do any adapter IPs belongs to Israeli subnets
After establishing its execution context, the ransomware disables SafeBoot options and initiates broad collection of high-value data and files with predefined extensions list from Documents\Downloads\Desktop\VIdeos\Pictures\Music. While this activity supports double extortion, the harvested information may also be leveraged for lateral movement or follow-up attacks. The malware collects registry hives, system credentials, browser data, and some application data from platforms including Discord, Slack, Roblox, Telegram, Office, WhatsApp, Atomic Wallet and more. In addition, it attempts to dump LSASS to obtain further credentials. All collected data is packaged into a ZIP archive named collected_data.zip and exfiltrated to an external service via file.io.
Figure 3 – Staging the collected data in a ZIP archive.
Next, the malware performs network reconnaissance to better understand the victim’s environment. The malware enumerates the local network configuration, maps nearby hosts via ARP requests, and actively probes discovered systems. As part of this process, it scans for exposed RDP services and attempts to exploit Fortinet devices using CVE-2025-64446.
Figure 4 – CVE-2025-64446 exploitation code.
To maintain persistence, the malware uses several different mechanisms, favoring redundancy:
Registry Run key
Creating a service named WinDefender
Creating a new user SysAdmin with password Password123!
Creating a new AWS user, without any check if AWS is installed:
Figure 5 – Persistence via AWS.
Next, the malware checks if AV and VPN products are running. If so, it terminates their processes and sends to the C2 server the link to file.io which contains exfiltrated data file and victim information:
Figure 6 – Sending victim data to the attackers’ server.
Finally, after finishing reconnaissance, privilege handling, and data collection stages, the ransomware moves into the main impact phase: encryption. It iterates through common user directories such as Documents, Desktop, Music, Downloads, Pictures and Videos, and encrypts files in place using the BCryptEncrypt API. The .sicarii extension is appended to each encrypted file name:
The algorithm used is AES-GCM (256-bit key) via BCryptOpenAlgorithmProvider("AES", ..., "ChainingModeGCM").
A unique random AES key is used for each file and the encryption parameters (nonce and tag) are stored in an XOR-0xAA-encoded header.
The encrypted file is named <original_name>.sicarii and contains only a custom header plus ciphertext.
The original unencrypted file is deleted.
The ransomware drops its ransom note:
Figure 7 – Ransom Note.
As a final pressure mechanism, the malware deploys a destructive component intended to hinder system recovery and prolong operational downtime. The ransomware drops a destruct.bat script and registers it to execute at system startup. When triggered, the script corrupts critical bootloader files, leverages built-in Windows utilities such as cipher and diskpart to perform disk-wiping operations, and ultimately forces an immediate system shutdown.
Figure 8 – Destructive phase.
Intelligence Findings & Anomalies
Telegram Presence
The primary Sicarii operator uses the Telegram account @Skibcum, operating under the display name “Threat.” According to our analysis, the account was registered in November 2025, shortly before Sicarii’s initial appearance in underground forums and RaaS advertisements. This timing aligns closely with the group’s emergence and suggests the account was created specifically for this operation rather than part of a long-standing criminal persona.
The account’s profile image features a repurposed internet meme containing the phrase “Smile is a mitzvah” (the word “mitzvah” in Hebrew means “good deed”) alongside iconography associated with the banned Israeli extremist Kach organization.
Figure 9 – Threat’s Profile picture.
The account is active in several Telegram group chats associated with underground communities. These include Russian-language informal hacker and meme-oriented channels where the operator participates in casual conversation, exchanges stickers and GIFs, as well as chats unrelated to operational activity. The tone in public group chats is informal and at times impulsive, standing in contrast to the more deliberate and controlled tone adopted in private communications.
In all these communications, the operator demonstrates comfortable fluency in English and Russian, using colloquial phrasing, slang, and emotionally expressive language consistent with native or near-native proficiency. No comparable fluency is observed in the Hebrew language in any setting.
Direct Messaging and Signaling Behavior
In private communications, the operator posed as Sicarii’s communications lead and made several self-reported operational claims:
Victim Activity: Claimed that Sicarii compromised 3–6 victims within approximately one month, all of whom paid the ransom.
Targeting Strategy: Stated that the group focuses on small businesses, intentionally avoiding large enterprises and government entities to reduce scrutiny and pressure.
Negotiation Practices: Acknowledged routine negotiation and cited a single case in which a ransom demand was reduced to approximately USD 10,000 for an incident involving around five endpoints.
Comparative Positioning: Repeatedly compared Sicarii to established Russian ransomware groups such as LockBit and Qilin, while emphasizing that Sicarii is intentionally maintaining a lower profile “for now.”
On January 5, 2026, Sicarii published its first publicly listed victim, a Greece-based manufacturer. Shortly thereafter, Sicarii advertised downloadable exfiltrated data hosted on a public file-sharing service, but the file download links quickly expired. The operator described this victim as “just a test,” despite earlier assertions that multiple successful extortion cases had already occurred. This reframing introduces an internal inconsistency between prior claims of operational success and the treatment of the first disclosed victim.
Ideological Claims vs. Financial Motivation
Sicarii simultaneously frames itself as a profit-driven RaaS platform and an ideologically motivated actor inspired by extremist Jewish figures. Multiple conversations and advertisements emphasize that Sicarii prioritizes attacks against Arab or Muslim targets and explicitly volunteer “insider information” about their intention to next target a Saudi Arabian entity.
Figure 10 – Insider information offer.
This duality is inconsistent with observed ransomware ecosystems, where ideological messaging is typically minimized to avoid limiting affiliate recruitment and operational reach. The selective invocation of ideology, particularly when paired with commercial incentives, appears performative rather than doctrinal.
Figure 11 – Performative claim or ideological statement?
Performative Israeli Identity and Linguistic Inconsistencies
Although Sicarii group members present themselves as Israeli or Jewish, their use of Hebrew strongly suggests non-native language skills. Hebrew content on the group’s shame site contains misspellings, awkward phrasing, and literal translations of English idioms that do not exist in Hebrew. In private communications, the Telegram user claimed to personally handle only “frontend and communications,” while asserting other operators are Israeli and responsible for ransomware development and initial access operations. Using the same Telegram profile, the actor quickly reemerged as “Isaac” while producing Hebrew that appears to be machine-translated English and insisting they are Hebrew speakers even when challenged.
Figure 12 – An excerpt from the chat with the Sicarii operator, allegedly handing over their account to another operator, “Isaac”, who is Israeli.
In contrast, Sicarii’s activity on underground forums and Telegram channels is conducted fluently in Russian and English, including structured RaaS advertisements and informal interactions. This linguistic asymmetry indicates that English or Russian is actually the operator’s primary language.
Behavioral Indicators and OpSec Observations
The operator’s Telegram behavior displays several notable characteristics:
Low operational discipline, such as openly requesting “ransomware APKs” in public group chats rather than sourcing such information privately.
Identity play and inconsistency, including shifting self-descriptions and performative signaling toward ideological alignment without a clear strategic purpose.
This reinforces the impression of a relatively inexperienced actor navigating established underground ecosystems rather than a seasoned participant.
Visual Branding and Subcultural Overlap Image
The Telegram operator’s profile image and shared graphics reuse a modified internet meme featuring the phrase “Smile is a mitzvah” alongside symbols associated with the banned Israeli extremist organization Kach. The only variant of this image was identified within a looksmax forum, an online male-dominated subculture often characterized by extreme racism, misogyny, and anti-Semitic discourse.
The limited circulation of this image suggests it’s not a mainstream ideological representation. The forum user who shared this picture said he was a 15-year-old boy and participated in anti-Semitic forum threads.
VirusTotal Activity – Uploading Your Own Source Code & Terrorist Images
The majority of Sicarii-associated samples were submitted to VirusTotal by a single community account which uploaded approximately 250 files over the past several months. Most submissions correspond to apparent variants or loaders associated with the Sicarii ransomware.
Notably, the ransomware binaries were frequently uploaded under the generic filename Project3.exe, a naming convention consistent with testing, staging, or iterative development rather than finalized deployment artifacts.
In addition to compiled ransomware samples, the same VirusTotal account uploaded a source code file titledransomawre.cson October 25, 2025, predating Sicarii’s public emergence. This source code referenced the same Tor infrastructure later used by the Sicarii ransomware, suggesting early development or experimentation prior to operational deployment.
In addition to malware-related submissions, the same account also uploaded:
Unrelated suspicious files
Malware report-style documents
An image of Meir Kahane, founder of the extremist Kach organization
The convergence of ransomware testing artifacts, early-stage source code, and extremist ideological imagery within a single VirusTotal account is atypical for mature ransomware operations. Instead of reflecting a compartmentalized development pipeline or affiliate-driven ecosystem, this activity suggests personal experimentation or centralized control, reinforcing the impression of limited operational experience and informal tradecraft.
Explicit National Signaling and Deviation from Ransomware Norms
Established ransomware groups, particularly those operating from Russia or Eastern Europe, typically avoid overt national or ideological signaling to preserve plausible deniability and reduce geopolitical risk. Even well-documented Russian-linked groups such as Qilin or Cl0p refrain from explicit self-identification, despite consistently avoiding domestic targets.
Notably, Sicarii’s operators referenced Qilin and Cl0p in private communications, explicitly describing them as Russian groups that do not attack within Russia and stating that Sicarii follows the “same logic.” This comparison was used by the operator to justify both excluding Israeli victims and the group’s broader targeting posture.
Despite invoking this model, Sicarii diverges sharply from established ransomware norms by:
Advertising preferential rates for attacks against Arab or Muslim states.
Embedding Israeli geo-exclusion logic directly into its ransomware.
Publicly associating itself with extremist Jewish figures and symbols.
Whereas Eastern European ransomware groups rely on implicit understandings and silent geographic avoidance, Sicarii’s approach is unusually explicit and performative. Such behavior is not only unnecessary for a financially motivated RaaS but also invites avoidable exposure. All of this suggests either limited operational maturity or deliberate signaling beyond purely criminal objectives.
Historical Precedent for False-Flag Use of Jewish Identity
Previous campaigns attributed to Iranian-aligned or anti-Israeli actors, including Moses Staff and Abraham’s Ax, leveraged Jewish historical references and fabricated Israeli insider personas to conduct false-flag operations or influence campaigns.
While no direct technical linkage exists between Sicarii and these actors, the use of Jewish extremist symbolism, overt Israeli identity claims, and ideologically charged rhetoric mirrors known deception techniques employed in prior operations by anti-Israeli Middle Eastern actors.
Leak site
The Sicarii leak site is notably rudimentary, offering display options in both Hebrew and English. The Hebrew version is characterized by awkward phrasing and frequent misspellings, further indicating non-native authorship. In private communications, the operator stated that AI tools were used in the site’s development. Notably, the leak site was active for approximately one month before the first victim was published, a delay that is atypical for RaaS operations seeking rapid visibility and credibility.
Figure 13 -Sicarii onion website.
Conclusion & Assessment
Sicarii is a newly observed ransomware operation that combines a functional extortion capability with unusually explicit Israeli and Jewish branding. While the malware itself demonstrates credible ransomware functionality, the group’s behavior and presentation deviate from established ransomware norms.
On Telegram communications, underground forum activity, and public-facing infrastructure, Sicarii repeatedly asserts national and ideological identity in ways that provide no clear operational benefit. Although the operators compare themselves to Russian ransomware groups such as Qilin and Cl0p (arguing that those groups also avoid domestic targets), Sicarii departs from this model by making its alignment explicit and performative, weakening plausible deniability.
Linguistic analysis further undermines the group’s claims. Hebrew usage across the leak site and private communications is inconsistent and indicative of non-native authorship, while English and Russian are used fluently. Operationally, the group appears centralized and informal, with early-stage tooling, inconsistent victim narratives, and limited compartmentalization, suggesting experimentation rather than a mature RaaS ecosystem.
Taken together, these indicators suggest that Sicarii’s claimed Israeli or Jewish identity doesn’t necessarily reflect genuine ideological motives. Instead, the operation appears to leverage performative identity signaling layered onto an immature ransomware capability. Attribution remains inconclusive, but Sicarii’s self-description should not necessarily be taken at face value.
The YouTube Ghost Network is a malware distribution network that uses compromised accounts to promote malicious videos and spread malware, such as infostealers.
One of the observed campaigns uses a new, heavily obfuscated loader malware written in Node.js, which we call GachiLoader.
To make it easier to analyze obfuscated Node.js malware, Check Point Research developed an open-source Node.js tracer, which significantly reduces the effort needed to analyze this type of malware and extract configurations.
One variant of GachiLoader deploys a second stage malware, Kidkadi, that implements a novel technique for Portable Executable (PE) injection. This technique loads a legitimate DLL and abuses Vectored Exception Handling to replace it on-the-fly with a malicious payload.
Introduction
In a previous publication, we examined the YouTube Ghost Network, a coordinated collection of compromised accounts that abuse the platform to promote malware. In our current research, we analyze one specific campaign of this network, which stood out as the deployed malware implements a previously undocumented PE injection method which abuses Vectored Exception Handling to load its malicious payload.
Campaign Overview
Similar to campaigns we previously documented, the infection chain begins with compromised accounts that host videos designed to lure viewers into downloading malware from an external file hosting platform. The theme of this campaign are game cheats and various cracked software:
Figure 1 — Example Compromised Account starts sharing malicious game cheat advertisements
The video’s descriptions then provide a password for the archive containing the malware, as well as instructions that usually include disabling Windows Defender.
We identified more than a hundred videos belonging to this campaign, which collected approximately 220.000 views. The videos were spread across 39 compromised accounts, with the first video uploaded on December 22, 2024. This means that this campaign has been running for more than 9 months. After we reported these videos to YouTube, most have been taken offline, although new videos will continue to appear on newly compromised accounts.
Since we started monitoring this specific campaign, it deployed the Rhadamanthys infostealer as a final payload, which is distributed through a custom loader which we call GachiLoader.
GachiLoader
GachiLoader is a heavily obfuscated Node.js JavaScript malware used to deploy additional payloads to an infected machine. Node.js is one of a long line of threat actors always adapt their arsenal using non-traditional programming languages and platforms adapted by threat actors in their quest to spread malware.
As obfuscated JavaScript requires a lot of time and effort to manually deobfuscate, we developed a tracer for Node.js scripts to dynamically analyze this type of malware, defeat common anti-analysis tricks and significantly reduce the manual analysis effort. This tool is not only useful for GachiLoader, but is useful for anyone analyzing heavily obfuscated Node.js malware. Therefore, we decided to share it with the research community here:
Some of the analyzed GachiLoader samples drop a second-stage loader, which we call Kidkadi. This loader is particularly interesting, as it implements a novel technique for PE injection, which tricks the Windows loader into loading a malicious PE from memory instead of a legitimate DLL. We analyzed this technique, which we call Vectored Overloading and reimplemented it in a Proof-of-Concept (PoC) shared below.
Technical Analysis
GachiLoader’s JavaScript module is bundled into a self-contained executable, using the nexe packer, with sizes roughly between 60 and 90 MB. nexe is an open-source project, that compiles a Node.js application into a single executable file, bundled with a Node.js runtime, so that the file can run on a host without Node.js installed. While the size of the executable is quite big, it isn’t suspicious as the victim expects to receive a software package. The tool nexe_unpacker can be used to extract the obfuscated JavaScript source code from the PE.
To avoid analysis by a security researcher or an automated sandbox, the GachiLoader JavaScript module employs several anti-VM and anti-analysis checks:
Checks if the total amount of RAM is at least 4GB
Checks if at least 2 CPU cores are available
Compares the username against a list of usernames, that can be associated with various sandboxes or analysis systems (see Appendix A for a list of all names).
Checks the hostname against a similar list of hostnames (see Appendix B for a list of all hostnames).
Probes the running programs and compares against a list of programs, such as analysis tools, sandbox indicators or common programs running on VMs (see Appendix C for a list of all process names).
The malware then proceeds to run several PowerShell commands to enumerate the system resources and capabilities over WMI .
Check if at least one port connector object exists: (Get-WmiObject Win32_PortConnector).Count
Get drive manufacturers and compare against a blacklist: Get-WmiObject Win32_DiskDrive | Select-Object -ExpandProperty Model (See Appendix D for a list of all drive manufacturers).
Resolve video controllers via Get-WmiObject Win32_VideoController | Select-Object -ExpandProperty Name, and check the names against a blacklist associated with VM environments (See Appendix E for a list of all video controller names).
If any of these checks indicate a virtual machine, sandbox or analysis environment the malware enters a loop of sending HTTP GET requests to benign websites such as linkedin.com, grok.com, whatsapp.com or twitter.com :
Figure 3 — Endless loop of GET requests when a lab environment is detected
Finally, to avoid running multiple times in a short period of time, a mutex file with a random-per-sample name and the .lock extension is created in the %TEMP% directory on running for the first time. If this file already exists or was modified within the last 5 minutes, the program terminates.
We were able to easily bypass all of these anti-analysis with Node.js Tracer: the tool hooks into the respective methods and spoofs the results to the caller, in this case the malware, allowing the script to run and expose its malicious actions:
Figure 4 — Anti-Analysis Checks bypassed with Node.js Tracer
Privilege Elevation via UAC Prompt
If the malware decides that the environment is not that a sandbox, it then checks if it is running in an elevated context by running net session , a command that is expected to fail if run by a non-administrative user. If the command fails, the malware tries to restart itself in an elevated context using the following PowerShell command:
While this triggers a UAC prompt, that prompt is likely to be accepted by the victim, as they expect to run an installer for some sort of software, which usually requires administrative privileges.
Defense Evasion
To avoid detections of subsequent payloads, the malware attempts to kill Windows Defender’s SecHealthUI.exe process by running taskkill /F /IM SecHealthUI.exe and adds Defender exclusions via Add-MpPreference -ExclusionPath for the following paths:
C:\Users\
C:\ProgramData\
C:\Windows\
For all other existing drives, at the root (e.g. D:\ )
In addition, an exclusion for *.sys files is added via Add-MpPreference -ExclusionExtension '.sys', although we have not observed any *.sys files being dropped by the analyzed samples.
Payload Delivery and Execution
To retrieve the next stage’s payload, the malware comes in two variants.
One variant gets the payload from a remote URL
The other variant drops another loader, kidkadi.node, which loads the final payload using the Vectored Overloading method. This payload is embedded in the loader’s JavaScript source.
First Variant – Remote Payload
Figure 5 — First GachiLoader Variant loading a Remote Payload
GachiLoader first obtains information about the host it is running on, such as antivirus products and the OS version, and sends them via a POST request to the /log endpoint of its C2 (Command and Control) addresses. The samples all have multiple C2 addresses embedded for redundancy and try out each one in succession, as we saw when tracing the calls through our tracer:
Figure 6 — C2 Communication Traced via Node.js Tracer
Next, a GET request to the /richfamily/<key> endpoint (where <key> is a value unique to each sample) with the X-Secret: gachifamily header gets the URL of the final payload to download, encoded in Base64. This final payload can only be retrieved if using the correct X-Secret header again – this time using a unique key embedded in the binary, e.g. X-Secret: 5FZQY1gYj0UKw4ZC99d1oNYR8LvTPtrfN357Eh5gmRvsMaPYgXtMxRXpMb2bTFOb2h2HqMnvUKT9CUpj9864gckmPUzf9uLIIU9. Otherwise, the web server returns a Forbidden error.
The final payload is then downloaded to the %TEMP% directory and saved with a random name, mimicking legitimate software such as KeePass.exe, GoogleDrive.exe , UnrealEngine.exe or others which contain the Rhadamanthys infostealer, packed and protected with VMProtect or Themida.
Second Variant – Kidkadi
The second variant we observed in the wild did not reach out to a C2 server to get the second payload, but instead had an embedded payload which is executed through another loader that is dropped to disk under %TEMP% as kidkadi.node:
Figure 7 — Second Variant of GachiLoader dropping Kidkadi
.node files are native addons for Node.js, which are essentially just DLLs that can be called from Node.js code via dlopen. Therefore, they can be used by developers whenever the Node-API does not expose sufficient functionality.
The malware exposes a function for Node.js to call, where the name of the method differs across samples. In some cases, the name as well as the error messages in some samples are of Russian origin:
Figure 8 — Exposing a function to the JavaScript code
The loader passes the payload PE as a binary buffer to Kidkadi through this exposed function, which then runs this payload via reflective PE loading. We found that this loader uses a novel spin on Module Overloading, abusing Vectored Exception Handlers (VEHs) to trick the Windows operating system to run the final payload, when invoking LoadLibrary to load an arbitrary DLL. This technique, not yet documented, shows that the author has a decent understanding of Windows internals. We named this method Vectored Overloading.
PE Loading via Vectored Overloading
The malware first creates a new section with SEC_IMAGE from the legitimate wmp.dll, a DLL used by Windows Media Player. It then overwrites this section with the content of the payload (the PE to be loaded) and maps a view of that section into the process via NtMapViewOfSection. The PE’s sections are then copied into memory one by one and relocations as well as the correct protections are applied:
Figure 9 — PE mapper
This results in a view of the malicious PE, mapped to the process, which is backed by the legitimate DLL wmp.dll. This section view is what the Windows loader (meaning ntdll!Ldr*) will be tricked into loading later on.
Since the Windows loader, called via LoadLibrary, does not load arbitrary PEs, but only those that have DLL characteristics, the Characteristics of the FileHeader are set to IMAGE_FILE_DLL , if the payload is not a DLL. Additionally, the entry point is zeroed out, likely to avoid the loader calling an entry point that is not that of a DLL. If the payload is a DLL, the header is not changed.
Figure 10 — Check and update FileHeader Characteristics
Afterwards, the malware registers a Vectored Exception Handler (VEH).
VEHs are user-mode callbacks that are invoked by the OS when an exception occurs. A common malware technique abusing VEHs is to register a hardware breakpoint on a specific instruction, which triggers an exception whenever this instruction is reached. This exception is then handled by the VEH, which can intercept the call and, for example, change the parameters. This essentially allows hooking functions without patching memory, such as when using classic trampoline hooks.
In this case, the hardware breakpoint (HWBP) is set on NtOpenSection :
Figure 11 — Setting a hardware breakpoint on NtOpenSection
The malware then loads amsi.dll via LoadLibrary , which kicks off the injection:
Figure 12 — Loading the target library and removing the exception handler
A call to LoadLibrary internally ends up in the Windows loader creating a section object of the target DLL to load, which is opened through a call to NtOpenSection . This triggers the hardware breakpoint, and subsequently the VEH, which were registered earlier. This is where the main injection logic is implemented.
To make the loader map the malicious PE instead of the actual amsi.dll section, the section object pointing to amsi.dll is swapped with the malicious payload section from earlier. The VEH simply places the section handle created earlier on the stack position that corresponds to the [out] PHANDLE SectionHandle argument of NtOpenSection. The VEH then advances the instruction pointer eip to the ret instruction and resumes execution. This skips the actual call to the kernel while still giving back a valid handle, essentially emulatingNtOpenSection:
Figure 13 — Skipping the call to NtOpenSection, replacing the expected output parameter with the SectionHandle pointing to the malicious payload
Before stepping out of the VEH, the hardware breakpoint is re-set to NtMapViewOfSection.
Figure 14 — Setting a hardware breakpoint on NtMapViewOfSection
NtMapViewOfSection is then used by the Windows loader to map the section into the process, which again triggers the hardware breakpoint. To make sure the malicious payload is mapped, the syscall is again emulated by advancing the instruction pointer and replacing the [out] arguments with the relevant values, such as the section base address or the section size. This is possible, because the section view was mapped by the malware earlier, when the malicious payload was written into the view of wmp.dll:
Figure 15 — Skipping the call to NtMapViewOfSection, replacing the expected output parameter with the pointer to the malicious payload
A final hardware breakpoint is then set on NtClose , where the malware simply verifies that the correct section handle is closed.
Figure 16 — Setting the hardware breakpoint on NtClose
Back in the regular flow of the program, outside the VEH, the entry point will be invoked if the payload is a regular PE. If it is a DLL, the loader expects it to be another .node module and resolves the correct exports to invoke:
Figure 17 — EXE and DLL invocation
Completely unrelated to this campaign, we found a file with an original filename of HookPE.exe, which is a 64-bit PoC version of the technique with debug prints that uses the technique to load calc.exe into memory. Error strings in this binary indicate that the loader uses code from libpeconv for PE manipulation.
Figure 18 — HookPE PoC project, using the same technique
This injection technique has multiple advantages over “classic” RunPE-style reflective loading:
Just like when using the Module Overloading technique, the injected DLL will show up as backed by a legitimate image (such as wmp.dll), since the section was originally created for this DLL. However, since the code in memory will differ from the code on disk, tools such as Moneta are able to detect it:
Figure 19 — While Moneta detects the mismatching module, most analysis tools display the original DLL name
Some loader work is offloaded to the Windows loader. This significantly reduces complexity for the malware author as they do not have to implement e.g. resolving imports or TLS callbacks, which in turn increases payload compatibility. For example, many publicly available PE loaders do not properly handle TLS callbacks.
By emulating syscalls, the respective kernel side callbacks such as ETWti are not invoked, as the call to the kernel is skipped entirely. This may fool security solutions that rely only on these section ETWti events. Of course, the earlier calls before the injection (when mapping the image) still trigger those events, but not in the order usually expected.
We published a reimplementation of the 64bit variant of this injection method as a tool for security researchers to analyze the technique and test detections:
As deobfuscation of the JavaScript source is a tedious and partially manual process, we decided to run all available samples of GachiLoader through Node.js Tracer to bypass the anti-analysis checks and receive the final payloads. By hooking filesystem-related Node APIs, the downloaded files are saved for the analyst before they can be deleted by the malware trying to remove its traces.
Figure 20 — Tracer showing GachiLoader dropping Kidkadi to disk
The final payloads of both variants of GachiLoader were all packed and protected by Themida or VMProtect. Dumping the unprotected configuration from memory when running them in an automated sandbox then allowed us to extract the C2 servers of the final payloads.
Figure 20 — Detect-it-Easy Output for the Final Payload
All the analyzed samples that were part of this campaign dropped Rhadamanthys as the final malware. The extracted C2 servers can be found in the IoC section below.
Conclusion
Malware written for the Node.js platform has become increasingly common and is mostly found in obfuscated form, which is tedious to statically deobfuscate and analyze. By enabling analysts to trace and hook Node-API execution dynamically with our open source Node.js Tracer, the time that has to be spent on triage and analysis is significantly reduced, and common anti-analysis checks can easily be defeated.
The threat actor behind GachiLoader demonstrated proficiency with Windows internals, coming up with a new variation of a known technique. This highlights the need for security researchers to stay up-to-date with malware techniques such as PE injections and to proactively look for new ways in which malware authors try to evade detections.
The threat actors behind the YouTube Ghost Network exploit the trust in the YouTube platform to trick victims into downloading malware. Users should be particularly cautious of offers for cracked software, cracks, trainers, or cheats, as these files are frequently laced with malware designed to steal data and/or compromise a device. While both the security community and YouTube actively work to identify and remove such content, these attacks remain persistent.
Protections
Check Point Threat Emulation and Harmony Endpoint provide comprehensive coverage of attack tactics, filetypes, and operating systems, and protect against the attacks and threats described in this report.
In recent months, Check Point Research has identified a new wave of attacks attributed to the Chinese threat actor Ink Dragon. Ink Dragon overlaps with threat clusters publicly reported as Earth Alux, Jewelbug, REF7707, CL-STA-0049, among others.
Ink Dragon has expanded its operational focus to new regions – In the last few months, the threat actor’s activities show increased focus on government targets in Europe in addition to continued activities in Southeast Asia and South America.
Ink Dragon builds a victim-based relay network – Ink Dragon leverages a custom ShadowPad IIS Listener module to turn compromised servers into active nodes within a distributed mesh, allowing each victim to forward commands and traffic, effectively transforming targets into part of their C2 infrastructure.
Ink Dragon continues to exploit long-known IIS misconfigurations for initial access – Despite years of public reporting and awareness within the security community, Ink Dragon still relies on predictable or mismanaged ASP.NET machineKey values to perform ViewState deserialization attacks against vulnerable IIS and SharePoint servers.
Ink Dragon is evolving its operations with new TTPs and tools – The cluster has introduced a new variant of FinalDraft malware with enhanced stealth and higher exfiltration throughput, along with advanced evasion techniques that enable stealthy lateral movement and multi-stage malware deployment across compromised networks.
Introduction
Check Point Research tracks a sustained, highly capable espionage cluster, which we refer to as Ink Dragon, and is referenced in other reports as CL-STA-0049, Earth Alux, or REF7707. This cluster is assessed by several vendors to be PRC-aligned. Since at least early 2023, Ink Dragon has repeatedly targeted government, telecom, and public-sector infrastructure, initially concentrating on Southeast Asia and South America, but with an increasing footprint in Europe and other regions. The actor’s campaigns combine solid software engineering, disciplined operational playbooks, and a willingness to reuse platform-native tools to blend into normal enterprise telemetry. This mix makes their intrusions both effective and stealthy.
A notable characteristic of Ink Dragon’s operations is their tendency to convert compromised environments into part of a larger, distributed relay network. By deploying a ShadowPad IIS Listener Module across multiple victims, the group effectively turns each breached server into a communication node capable of receiving, forwarding, and proxying commands. This design allows attackers to route traffic not only deeper inside a single organization’s network, but also across different victim networks entirely. As a result, one compromise can quietly become another hop in a global, multi-layered infrastructure supporting ongoing campaigns elsewhere, blending operational control with strategic reuse of previously breached assets.
This blog also presents the forensic story of a high‑stakes compromise of a European government office, highlighting recurring methods observed across different victims. We walk through the entire kill chain observed in the field, including web-centric initial access, hands-on-keyboard activity, staged loaders, privilege escalation, and credential-harvesting components, as well as aggressive lateral movement that culminated in domain dominance. We also document multiple delivery and persistence patterns that Ink Dragon favors, and unpack a new variant of the FinalDraft backdoor, which is used as a resilient, cloud-native command-and-control platform.
Beyond the technical details, this article shows how Ink Dragon’s tooling and repeatable TTPs reflect a mature, modular development model that steadily expands in capability while maintaining a consistent operational philosophy.
Attack Chain
Attackers begin by gaining initial access through ViewState deserialization or ToolShell-based exploits, then deploy ShadowPad on the compromised server. They harvest IIS worker credentials and establish an RDP proxy to move laterally, using RDP and ShadowPad’s built-in capabilities along with reused credentials. After obtaining access to a domain admin account, they achieve domain dominance. From there, they deploy FinalDraft on strategic machines and install a ShadowPad IIS listener on public-facing servers, enabling new victims to connect to the attackers’ infrastructure as the campaign continues.
Initial Access
In the environments we investigated, the common initial access vector is exploitation of ASP.NET ViewState deserialization via publicly disclosed machine keys. In this scenario, the __VIEWSTATE parameter, normally protected using the application’s machineKey, can be forged if the key is copied from public sources. Once the attacker can generate a valid signature, they can inject a crafted ViewState payload that the server deserializes, leading to remote code execution.
In other cases, we’ve also observed the actor abusing the ToolShell SharePoint vulnerability. ToolShell is an exploit chain targeting on-premises Microsoft SharePoint that combines authentication bypass and unsafe deserialization (CVE-2025-49706 / CVE-2025-53771 and CVE-2025-49704 / CVE-2025-53770, among others) to enable unauthenticated remote code execution and web shell deployment on vulnerable servers. In July 2025, we observed the actor conducting mass scanning for the ToolShell vulnerability during the initial waves of exploitation, indicating the actor was among a limited set of actors with early access to the exploit.
This demonstrates that the attackers have multiple web-facing options for initial compromise. The practical workflow is straightforward: enumerate internet-facing IIS/SharePoint servers, test for predictable machine keys or vulnerable SharePoint endpoints (often using publicly available fuzzing lists), and submit crafted POSTs or payloads that trigger deserialization/RCE. These attacks are stealthy and scale well against large organizations with inconsistent web configurations.
Internal Operation & Kill Chain Overview
Lateral Movement
In these campaigns, the adversary leverages two complementary strengths of a web compromise after gaining an initial foothold: privileged local artifacts (the IIS application/service credentials and configuration) and visibility into active administrative sessions.
By obtaining the IIS machineKey/DecryptionKey or otherwise recovering the site’s cryptographic secrets, the attacker can decrypt locally stored configuration blobs and credentials that the site or its worker processes store. In practice, this frequently yields the IIS worker/app-pool account password or other local secrets that carry elevated rights on the host and often across other IIS servers that reuse the same service account or credential material.
With a local administrative credential in hand, Ink Dragon escalates from code execution in w3wp.exe to full system control, and then leverages that control to create a persistent remote access channel (commonly an RDP tunnel or scheduled task that launches an administrative payload). Because many organizations reuse service credentials across web farms for management convenience, obtaining a single IIS credential can allow the actor to authenticate to sibling IIS hosts and pivot laterally with minimal network noise. Ink Dragon frequently tunnels RDP traffic to reach internal hosts from a remote workstation, exposing their machine names and enabling direct, interactive sessions that appear superficially legitimate.
From there, the attacker’s lateral playbook becomes straightforward: stage a resilient implant (ShadowPad/FinalDraft variants are common) and propagate it using native protocols such as SMB. The operator copies the triad (EXE + side-loaded DLL + encrypted blob) into writable shares, creating a service or scheduled task to run the payload, and attempting to disguise the service/process under a plausible name.
Persistence
The most common patterns we observed:
Scheduled tasks – the actor created tasks with benign-looking names (notably SYSCHECK) set to run under SYSTEM and pointing to staged loader hosts such as conhost.exe. Tasks were often created to run once at a chosen time, allowing the operator to bootstrap wide-scale re-execution while minimizing noisy periodic callbacks.
Service installation – on several machines, Ink Dragon installed services to launch their loaders as persistent system services, with service names disguised as Windows updates or temporary maintenance (e.g., WindowsTempUpdate). These services were used to run side-loaded triads (EXE + malicious DLL + encrypted blob), guaranteeing automatic restart and SYSTEM execution after reboots.
It’s essential to note that many of the staged executables (e.g., conhost.exe) were renamed to resemble native Windows binaries, yet they were digitally signed by legitimate vendors such as Advanced Micro Devices, Realtek Semiconductor Corp, and NVIDIA. Their OriginalFileName metadata differed from the on-disk names, indicating deliberate masquerading and abuse of trusted signatures to blend with the operating system.
Privilege Escalation
Ink Dragon combines targeted exploitation with heavy credential harvesting to quickly escalate local control into domain-level dominance.
Local escalation via exploitation: In multiple incidents, the initial __VIEWSTATE RCE was followed by local escalation tooling such as PrintNotifyPotato to obtain SYSTEM from a web server context. This allowed full control over the host and the rights to create persistent services and change firewall settings.
Credential harvesting and LSASS dumping: the operators deployed custom tools (we observed variants of LalsDumper) to create LSASS dumps and extract registry hives (SAM, SYSTEM) into ProgramData or user-profile directories for offline cracking. Dump files and registry hive exports were then used to recover NTLM hashes and Kerberos material.
Leveraging idle sessions: Ink Dragon actively enumerated Remote Desktop sessions and administrative consoles on key servers. In at least one instance, the actor located an idle RDP session belonging to a Domain Administrator that had authenticated via Network Level Authentication (CredSSP) using NTLMv2 fallback. Since the session remained disconnected but not logged off, it is highly likely that LSASS retained the associated logon token and NTLM verifier in memory. Ink Dragon obtained SYSTEM-level access to the host, extracted the token (and possibly the NTLM key material), and reused it to perform authenticated SMB operations. Through these actions, they were able to write to administrative shares and exfiltrate NTDS.dit and registry hives, marking the point at which they achieved domain-wide privilege escalation and control.
Enabling Egress
Ink Dragon modified host firewall rules to permit broad outbound traffic and effectively turned compromised hosts into unconstrained exfiltration/proxy nodes.
The group created a permissive outbound rule labeled to resemble legitimate software (we observed a rule named Microsoft MsMpEng, associated with MsMpEng.exe) that allowed Any → Any outbound traffic across all profiles. The rule was created locally (not via GPO), enabled, and applied to the Defender process in SYSTEM context. This bypasses upstream egress controls that would otherwise block custom ports or tunneling traffic.
Building the Relay Network
During the investigation, we discovered that Ink Dragon is actively converting compromised organizations into functional communication nodes within a distributed ShadowPad relay network. This capability is powered by an IIS Listener Module. Instead of serving as a traditional backdoor, the module enables the malware to register new URL listeners directly through the HttpAddUrl API, which lets processes bind HTTP(S) endpoints dynamically, including wildcard patterns that match all sub-paths or hostnames. This means the malicious listener seamlessly coexists with legitimate IIS behavior, silently intercepting any incoming HTTP requests whose URL matches its configuration. When a request arrives, the module decrypts the payload and evaluates whether the structure fits its proprietary protocol. If not, it falls back to genuine IIS logic, serving real web content or returning legitimate error codes. The result is a hard-to-detect implant that blends into the server’s normal traffic patterns while retaining full control over its hidden C2 channel.
Communication with a regular client via the IIS module
Where this becomes significantly more strategic is in how the module manages remote peers. The listener can categorize external IP addresses into two functional roles: servers and clients. It automatically pairs nodes from each list to relay traffic between them. Once two peers are matched, the compromised host becomes a live forwarding point: data arriving from the “server” side is streamed directly to the “client,” and responses are returned in the same manner, effectively turning the victim into a transparent communication bridge. We observed multiple instances where government entities were inadvertently serving as C2 relays. In some instances, the victim machine is leveraged as a hop, serving as an access node for ShadowPad clients active in other, unrelated target environments. This chaining effect forms a multi-layered mesh of compromised infrastructure, allowing the operator to issue commands to downstream implants without direct communication with them.
Beyond its role in constructing a large-scale external relay network, the ShadowPad IIS module also exposes a conventional internal proxy capability, designed to route commands toward ShadowPad nodes located deeper within the same network. When the attackers need to deliver instructions to implants that do not have direct external reachability, the IIS module simply relays the traffic internally, behaving much like a pivot point inside the compromised environment. This capability is not new in concept, but its integration into a legitimate IIS worker process makes it extremely difficult to distinguish malicious lateral communication from normal internal service traffic.
Beyond the relay logic itself, the module leaves behind an unusually rich forensic artifact: debug strings that document the number of bytes it forwards between external and internal IP addresses. These strings include source, destination, and payload size. During our investigation, this telemetry proved essential in reconstructing the attackers’ communication graph, enabling us to map exactly how commands entered the victim, how they were relayed, and which internal hosts were drawn into the chain. The presence of such granular logging underscores just how central the relay mechanism is to the operator’s workflow: the victim is not merely compromised, but actively repurposed as a communication bridge that keeps the broader ShadowPad infrastructure stitched together.
Debug log strings dumped from the IIS Listener
This architecture enhances stealth and survivability. By routing commands through unrelated victims, the true controlling IP is never exposed, and network defenders have difficulty distinguishing malicious cross-organization traffic from legitimate inter-government or inter-infrastructure communication. Most significantly, every newly compromised perimeter system can be repurposed immediately as another hop, allowing Ink Dragon to expand its operational reach while obscuring both the origin and the direction of command flow.
Tools & Post‑Exploitation Components
As we expanded our analysis beyond the initial access vector, a broader toolset began to emerge. Ink Dragon does not operate with a single backdoor or a monolithic framework; instead, the intrusions feature a sequence of purpose-built components that activate at different stages of the operation. What follows is a breakdown of the post-exploitation tooling we recovered, from IIS-embedded ShadowPad modules to debugger-based loaders, credential-harvesting implants, and long-term command-and-control platforms.
Summary of Observed Components
Category
Name
Function
Brief Description
IIS Backdoors & C2
ShadowPad IIS Listener Module
Core C2 & relay node
Intercepts selected IIS traffic, decrypts commands, builds a distributed relay network, and exposes a full ShadowPad backdoor on IIS servers.
Loader
ShadowPad Loader
Payload delivery
Triad structure (EXE + DLL + TMP) that decrypts and runs the ShadowPad core in memory while deleting artifacts.
Loader
CDBLoader
Memory-resident payload execution
Uses cdb.exe scripts to patch memory, run shellcode, and load AES-encrypted payloads via a debugger session.
Credential Access
LalsDumper
LSASS dump extraction
Registers a malicious SSP DLL in LSASS, extracts a compressed LSASS memory dump via custom direct-syscall logic.
Loader
032Loader
Host-dependent payload loader
RC4-like decryption using the system’s InstallDate as entropy, delivering payloads via shared memory mapping.
Modular RAT
FinalDraft
Long-term espionage & cloud C2
Modular RAT using Microsoft Graph API; supports exfiltration, RDP history harvesting, tunneling, scheduling, and mailbox-based command exchange.
ShadowPad IIS Listener Module
Module Initialization
The ShadowPad IIS Listener Module deployed in this intrusion is a fully integrated component designed to masquerade as part of the legitimate IIS stack while quietly providing command-and-control and relay capabilities. Its configuration block defines several operational parameters that determine both how it responds to benign web traffic and how it handles attacker-controlled messages. These include a server type string (used in HTTP response headers), a document root path, and a fallback error page path. Even when configuration fields are absent, the module falls back to realistic defaults — "C:\\inetpub\\wwwroot", "C:\\inetpub\\custerr\\en-US\\404.htm", and "Microsoft-IIS/10.0". Those values allow the module to blend seamlessly with a standard Windows Server installation.
The most important part of the configuration is the list of URL patterns the module will intercept. These patterns are stored as wildcard-enabled strings, separated by semicolons, and are used to register listeners via the Windows HttpAddUrl API. This API allows the module to attach itself to arbitrary HTTP URL prefixes, including those containing wildcards for hostnames or paths, making the listener hard to detect unless the exact bindings are enumerated at the OS level. Any inbound request matching one of these patterns is captured and passed through the module’s internal decryption and message-parsing routine.
def decrypt_first_packet(buf: bytearray, seed: int, length: int):
"""
buf = bytearray starting at a1->type (first byte = LOBYTE(seed), second = HIBYTE(seed))
length = total length of buffer (rdx)
"""
count = length - 2
seed_lo = buf[0]
seed_hi = buf[1]
num = (seed_hi << 8) | seed_lo
num &= 0xFFFFFFFF
pos = 2
for _ in range(count):
hi = (num >> 16) & 0xFFFF
num = (hi * 0x7093915D - num * 0x6EA30000 + 0x06B0F0E3) & 0xFFFFFFFF
buf[pos] ^= num & 0xFF
pos += 1
return buf
Requests that do not conform to the attacker’s expected encrypted structure are quietly handled as normal IIS traffic: static files are served from the configured webroot folder when available, and legitimate error pages are returned otherwise. This fallback behavior ensures the listener remains operationally stealthy, presenting a legitimate façade while still acting as a covert interception point.
Split Command Architecture
Once a request passes the module’s URL‑matching, decryption, and structural checks, the ShadowPad IIS Listener evaluates the embedded command ID. These commands fall into two broad categories, both of which are handled by the same component.
The first category contains the instructions responsible for building and maintaining the distributed relay network. These include commands that register an endpoint as a “server,” commands that register an endpoint as a “client,” and the logic that pairs nodes, forwards traffic, and maintains the two queues that drive the hop‑to‑hop communication model.
The second category is entirely separate in purpose: a full backdoor command set enabling the operator to interact directly with the local system. These commands cover host reconnaissance, file operations, data collection, payload staging, configuration updates, process control, and network‑level actions. In other words, the same module responsible for linking compromised machines into a cross‑victim relay chain is also fully capable of operating as a traditional ShadowPad backdoor on that same host.
Distributed Relay Network Construction
The most strategically significant capability lies in the module’s role in building and maintaining the operator’s distributed relay network. The IIS Listener maintains two concurrent registries of peers: a server list and a client list. Nodes can add themselves to either list via dedicated command IDs. Entries in the server list are periodically revalidated every 30 seconds, and the module issues acknowledgments to confirm ongoing availability. Nodes placed into the client list behave differently; if a client remains unpaired for 30 seconds, it is automatically pruned to prevent stale links.
Whenever a new node is inserted into one of the lists and the Listener detects that both lists contain at least one live entry, it pairs the first server node with the first client node. At that moment, it sends the server the victim’s IP address. After this handshake, the module establishes bidirectional relaying between the two sockets, shuttling each packet from server → client and client → server with no further processing. The result is a fully transparent hop that allows an upstream operator to deliver commands to a downstream client without ever interacting with that client directly.
Relay network logic flow
Backdoor Features
The IIS Listener Module of ShadowPad also includes features of the ShadowPad client, letting the attackers run different commands on the IIS machine. This embedded command set provides the operator with extensive control over the compromised host, enabling everything from reconnaissance to interactive access to payload staging.
The breadth of these command IDs illustrates how ShadowPad’s IIS Listener is more than a simple traffic forwarder; it is a self-contained control surface capable of both maintaining the distributed relay network and exerting fine-grained control over any machine running it. This duality is central to Ink Dragon’s operational philosophy: relay-capable nodes double as fully functional access points, allowing the operator to maintain stealthy persistence, collect intelligence, deploy additional tooling, and issue high-privilege instructions without ever exposing their true command infrastructure.
ShadowPad’s backdoor exposes a broad command surface designed to give operators full, hands-on control of a compromised system. It begins with basic orchestration capabilities, retrieving the full command map, gathering a detailed system snapshot, launching an interactive reverse shell, or cleanly shutting down the implant when needed. From there, the malware offers an extensive file-management layer that can enumerate drives, walk directories, manipulate timestamps, create or remove files and folders, and read, write, move, or download data on demand. This is complemented by rich process and service control, allowing operators to list and kill processes, inspect loaded modules, and start, stop, or delete Windows services with the same precision seen in legitimate administration tools.
Main commands method flow
ShadowPad also supports multiple execution models, including running commands with output capture, spawning interactive console processes with full screen-buffer access, and maintaining long-lived execution channels through pipes. The networking side is equally capable: the implant can inspect local TCP/UDP tables, alter network entries, and even serve as a pivot point by proxying or tunneling traffic through the infected host. In practice, these capabilities turn the backdoor into a complete remote operations platform that blends system administration, espionage tooling, and covert tunneling into a single, tightly integrated module.
ShadowPad Loaders
ShadowPad is a modular, multi-layered backdoor framework widely attributed to Chinese state-sponsored threat groups and frequently used in long-term espionage campaigns. ShadowPad allows operators to deploy customized modules for data exfiltration, credential harvesting, and lateral movement.
In Ink Dragon’s operations, we observed numerous ShadowPad deployments following a recurring triad sideloading structure: an executable, a malicious DLL, and an encrypted TMP payload. The legitimate or masqueraded executable loads the malicious DLL, which in turn decrypts and executes the ShadowPad core from the TMP file directly in memory, erasing the payload afterward to minimize forensic artifacts.
A notable detail in Ink Dragon’s ShadowPad loader is that many of the malicious DLLs across different incidents shared the same generic name, DLL.dll. The DLL loaders were MFC-based binaries, where the malicious code was heavily obfuscated using ScatterBrain.
Search for ShadowPad obfuscated code entry point
Once executed, these loaders perform on-the-fly decryption of configuration data and payloads stored in the same directory.
Path
Executable
DLL
C:\Users\Public
vncutil64.exe
vncutil64loc.dll
C:\Program Files\Microsoft\Edge
ApplicationLogs.exe
atiadlxy.dll
C:\Program Files\Microsoft\Edge\Application
msedge_proxyLog.exe
msedge_proxyLogLOC.dll
CDBLoader
One of Ink Dragon’s most distinctive TTPs is leveraging the Microsoft debugger (cdb.exe) as an execution host.
Rather than launching a typical EXE, the operator runs a WinDbg/CDB scripted session, such as: c:\users\public\cdb.exe -cf C:\Users\Public\config.ini -o C:\Users\Public\cdb.exe.
The shipped config.ini is not an innocuous INI file. It contains a sequence of memory-edit / write-bytes commands followed by a change to the instruction pointer (RIP) so execution continues at the attacker-controlled shellcode. The shellcode, in turn, reads an auxiliary file (wmsetup.log) from disk, decrypts it with an AES key hard-coded into the shellcode, and loads the real payload into memory for execution. After the payload runs, the debugger instance and helper files are often removed, leaving only transient memory-resident code.
The shellcode script in config.ini
LalsDumper
LalsDumper is Ink Dragon’s in-house LSASS extraction chain observed during multiple intrusions. The sequence starts with a small loader (lals.exe) that manipulates a companion DLL (fdp.dll) in the same folder. The loader locates a placeholder (32 ‘d’ characters) inside fdp.dll, overwrites that placeholder with the path to an auxiliary file (rtu.txt), and produces a patched DLL named nfdp.dll.
Crucially, the loader calls AddSecurityPackageA to register the DLL as a Security Support Provider (SSP) so that lsass.exe will load it, a technique that forces LSASS itself to load attacker code in-process.
The replacement of the placeholder by a real name added as SSP
The registered DLL reads rtu.txt, applies a simple XOR with 0x20 to recover a secondary payload, and then maps that payload into memory. The payload implements a custom MiniDumpWriteDump-like routine (not using the Windows API directly) to create a compressed dump of lsass.exe, writing a ZIP-like dump file named <%TEMP%>\<pid>.ddt.
The chain uses direct syscalls (hashed at runtime) to evade API-based hooking and EDR detection.
def hash_syscall(name: str) -> int:
h = 0xCD7815D6 # constant seed
for (*WORD) ch in name: # iterate Unicode code-points
h ^= (ord(ch) & 0xFFFF) + ror32(h, 8)
return h & 0xFFFFFFFF
The DLL exposes a function called Tom, which serves as the payload’s runtime entry point and orchestrates the loader’s core logic: when invoked, Tom creates a file in the system TEMP folder named using the victim process ID (for example %TEMP%\<pid>.ddt), captures a memory dump of lsass.exe and writes that dump into the .ddt file using a ZIP-style archive format.
LalsDumper’s main function
032Loader
032Loader is a sideload-based loader that uses host-specific entropy to decrypt and execute payloads. After being sideloaded by a legitimate executable, the loader patches the host EXE’s code flow so that control transfers to its own logic immediately after load.
The patching of the executable by 032Loader
The loader then queries the system’s InstallDate from the registry and derives a decryption key from that value. It uses the InstallDate as the seed for RC4 (or RC4-like) decryption of a third-file blob (the encrypted payload), often accessed via the SystemFunction032 API to perform the decryption step. Once decrypted, the loader maps the payload via CreateFileMappingW / MapViewOfFile and adjusts memory protections with VirtualProtect, then transfers execution to the in-memory payload.
FinalDraft: New Version
FinalDraft is a well-engineered, full-featured remote administration tool with the ability to accept add-on modules that extend functionality and proxy network traffic internally, mostly by abusing Outlook mail service via the Microsoft Graph API. The samples and behavior we analyzed are consistent with previous public write-ups, but Ink Dragon’s deployments show continued feature expansion and careful operational tuning to reduce telemetry and maximize resilience.
FinalDraft begins by locating and decrypting its configuration blob. The configuration is XORed with either a hash derived from the host ProductId (making the config per-machine) or the hash of a hardcoded string.
The Decryption of FinalDraft’s configuration
Once decrypted, the config exposes a unique GUID for the implant, the preferred communication transport, an AES key for message encryption, C2 endpoints and refresh tokens, and other operational metadata.
The most commonly used transport we observed is COutlookTrans, which leverages the Microsoft Graph API (the token endpoint at https://login.microsoftonline.com/common/oauth2/token) to hide command-and-control traffic inside legitimate cloud mail flows. FinalDraft uses a refresh token embedded in its configuration to acquire OAuth access tokens, which it stores locally under HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\UUID\ for later use. Commands and responses are exchanged as email drafts (messages whose subject starts with r_<session_id> and p_<session_id>). The payloads inside those messages are base64-encoded, AES-encrypted (with the config key), and compressed, which helps the operator hide instructions inside otherwise normal mailbox data and bypass network filtering that whitelists Microsoft cloud endpoints.
The message FinalDraft sends to get commands
FinalDraft implements a modular command framework in which operators push encoded command documents to the victim’s mailbox, and the implant pulls, decrypts, and executes them. Beyond the standard toolkit (process/service control, file/registry access, tunneling, and file transfer), the Ink Dragon variants we analyzed introduce several extensions that increase stealth, operational control, and data-theft efficiency.
Flexible Beacon Scheduling
The malware supports highly granular callback controls, allowing operators to define daily beaconing windows or specify an explicit list of times when the implant will attempt to reach its C2. This schedule-based design helps blend command traffic into predictable operational patterns.
RDP Activity Harvesting
DumpRDPHistory extracts evidence of both outbound and inbound RDP usage.
RDP OUT (Outbound RDP targets) – MRU-like entries in the user registry hives that record the servers the user has connected to (the built-in RDP client’s Recent Server list). For each entry, the tool reads UserNameHint and the registry key’s last write time to produce lines like: <ServerName>:<UserNameHint>:<Timestamp>. This data is queried from HKCU\SOFTWARE\Microsoft\Terminal Server Client\Servers\<ServerName>\
RDP IN (Inbound RDP sessions) – Event log entries from the Terminal Services Local Session Manager operational channel that record session connect/disconnect events. The tool queries Windows Event Log (via EvtQuery / Get-WinEvent) for Event ID 21 (Remote Desktop Services: Session logon succeeded) and 25 (disconnect/other session events depending on Windows version). From these, it extracts records like: <UserName>:<IP>:<TimeStamp>.
Security Control Downgrades
Several commands intentionally weaken Windows security posture to assist persistence and lateral movement:
DisableRestrictedAdmin – makes RDP connections authenticate with reusable credentials or delegated tokens, enabling credential hopping instead of “restricted” mode authentication. It makes RDP more attractive for lateral movement because an attacker who already controls an endpoint can initiate Restricted Admin connections using local credentials or stolen hashes to hop to other systems without needing cleartext passwords.
Enabling Restricted Admin
DisableTokenFiltering – disables remote token filtering so local admin accounts receive full, not restricted, admin tokens over the network. This allows attackers to use reused or harvested local credentials to perform truly administrative remote actions without requiring domain credentials.
EnableDSRMAdmin – allows the powerful DSRM recovery account to log in even when the DC is not in Directory Services Restore Mode. It gives an attacker a powerful, low-audited way to gain full local control of a DC using an otherwise seldom-monitored credential or hash.
Setting the DSRMAdminLogonBehavior registry key to 2
DisableRunAsPPL – removes Process Protection Level safeguards, enabling injection and tampering with protected processes such as AV/EDR components. Adversaries do this to defeat endpoint defenses (allowing in-memory loaders, hooking, or live patching of AV and EDR components), to persist by replacing signed binaries, or to extract secrets from processes that would normally be protected.
High-Throughput Exfiltration
BackgroundFileTransfer introduces a dedicated asynchronous worker for large-scale exfiltration. It streams data in sizeable chunks, reports progress in the background, and minimizes the number of required outbound connections. This is ideal for moving large archives over cloud-proxied channels without attracting attention.
Host Profiling & Inventory
Ink Dragon consolidates multiple reconnaissance routines such as system fingerprinting, adapter enumeration, and installed software collection into a unified host profile. This captures IP configuration details (similar to ipconfig /all), network adapter metadata, installed applications (from HKLM and HKCU uninstall keys), system identifiers (MachineGuid, ProductId, InstallDate, and System uptime), and other indicators useful for victim classification and targeting.
IP Configuration data strings in FinalDraft
Victimology
Ink Dragon’s targeting patterns show a consistent emphasis on government organizations, but beyond that shared characteristic, there are no clear indicators of how the actor selects its victims. Most affected organizations appear to serve a specific operational purpose for the actor rather than reflecting broad, industry-wide targeting.
Geographically, Ink Dragon has focused heavily on government entities in Southeast Asia and Africa, and in recent months has expanded its activity into Europe. Since the ToolShell exploitation wave in July, the actor has steadily increased its operations in the region, with a growing concentration on European government-sector targets. A key aspect of Ink Dragon’s tradecraft is its use of compromised organizations as C2 relay nodes. As a result, we have observed European victims being leveraged to launch activity not only against additional European institutions, but also against targets in Africa and Southeast Asia.
Overlap With RudePanda / REF3927 Activity
During our investigation, we also observed evidence of a second intrusion set commonly tracked as RudePanda / REF3927 on several of the same victim environments compromised by Ink Dragon. While we do not assess these activity clusters to be operationally linked, the victimology overlap is notable: both actors exploited the same internet-facing server vulnerability to gain footholds in identical organizations and systems.
Upon gaining access, RudePanda relied on its customary toolset, which included:
Godzilla-derived webshells used to execute in-memory .NET payloads and deploy secondary components.
Malicious IIS modules belonging to the TOLLBOOTH family embed command-and-control logic directly inside the web server’s processing pipeline. Several modules contained PDB references to dongtai, consistent with tooling previously documented in public reporting.
Configuration tampering of IIS’s applicationHost.config, where a simple diff against previous snapshots exposes the unauthorized module insertions.
In addition, the actor deployed a kernel-mode rootkit driver, wingtb.sys (2965ddbcd11a08a3ca159af187ef754c), a modified and signed variant of Winkbj.sys. Its installation uses an INF file (Hidden.inf) naming the service “Wingtb.” The driver is derived from the open-source “Hidden” rootkit project, used to conceal files, processes, and registry entries after being activated on the system.
Finally, several .lnk artifacts referencing the operator’s backdoor kit that is consistent with the GoToHTTP tooling previously documented by both HarfangLab and Elastic were recovered on disk. Although the original payloads were deleted, these link files served as additional indicators of RudePanda’s presence.
Conclusion
Ink Dragon’s operations illustrate a shift in how modern espionage clusters weaponize compromised infrastructure. Rather than treating each victim as an endpoint to be monitored or harvested, the group systematically folds every breached perimeter server into a distributed ShadowPad relay fabric. The IIS Listener Module sits at the center of this strategy: a stealthy, low-friction component that binds hidden URL prefixes, intercepts traffic without disrupting legitimate services, and quietly links victims together into a multi-hop communication grid.
This architecture transforms the threat landscape in several ways. First, it grants the operators resilient command paths that do not rely on fixed infrastructure. Even if a downstream implant has no internet reachability or if upstream servers are blocked, the attacker can simply reroute through other victims. Second, it provides natural operational camouflage. Traffic tunneled between unrelated government or enterprise networks appears outwardly benign and often blends into the expected profile of inter-organizational HTTP flow. Third, it enables strategic re-use of compromised assets: once a host is enrolled as a ShadowPad relay, it becomes an evergreen pivot point that continues to serve campaigns long after the initial intrusion.
The rest of the kill chain reinforces this design philosophy. Mature sideloading patterns, modular loader triads, memory-resident payloads, compartmentalized privilege escalation, and disciplined credential harvesting all serve one purpose: establish durable, high-privilege access long enough to turn the victim into reliable infrastructure. Whether deploying FinalDraft, staging ShadowPad loaders, or extracting domain-wide secrets, each action feeds back into the broader communication mesh that Ink Dragon relies on to maintain persistence across environments.
Taken together, Ink Dragon presents a threat model in which the boundary between “compromised host” and “command infrastructure” no longer exists. Each foothold becomes a node in a larger, operator-controlled network – a living mesh that grows stronger with every additional victim. Defenders must therefore view intrusions not only as local breaches but as potential links in an external, attacker-managed ecosystem, where shutting down a single node is insufficient unless the entire relay chain is identified and dismantled. Ink Dragon’s relay-centric architecture is among the more mature uses of ShadowPad observed to date. A blueprint for long-term, multi-organizational access built on the victims themselves.
On November 30, 2025, Check Point Research detected a critical exploit targeting Yearn Finance’s yETH pool on Ethereum. Within hours, approximately $9 million was stolen from the protocol. The attacker achieved this by minting an astronomical number of tokens—235 septillion yETH (a 41-digit number)—while depositing only 16 wei, worth approximately $0.000000000000000045. This represents one of the most capital-efficient exploits in DeFi history.
The Vulnerability: Cached Storage Flaw
The attack exploited a critical flaw in how the protocol manages its internal accounting. The yETH pool caches calculated values in storage variables called packed_vbs[] to save on gas costs. These variables store virtual balance information that tells the protocol how much value exists in the pool. The vulnerability emerged when the pool was completely emptied—while the main supply counter correctly reset to zero, the cached packed_vbs[] values were never cleared.
How the Attack Was Executed
The attacker executed the exploit in three stages: First, they performed over ten deposit-and-withdrawal cycles using flash-loaned funds, deliberately leaving small residual values in the packed_vbs[] storage with each iteration. Second, they withdrew all remaining liquidity, bringing the supply to zero while the cached values remained populated with accumulated phantom balances. Finally, they deposited just 16 wei across eight tokens. The protocol detected that supply was zero and triggered its “first-ever deposit” logic, which read the cached values from storage. Instead of minting tokens based on the 16 wei actually deposited, the protocol read the accumulated phantom values and minted trillions upon trillions of LP tokens, giving the attacker control over the entire pool.
Background: The yETH Ecosystem
Protocol Architecture
Yearn Finance’s yETH is a liquid staking token representing a basket of Ethereum-based liquid staking derivatives (LSDs). The protocol consists of three main components:
yETH Token – A standard ERC20 token with minter privileges
yETH Pool – A weighted stableswap AMM (Automated Market Maker) pool
Rate Providers – Oracle contracts that provide exchange rates for various LSDs
The pool contract implements a complex mathematical invariant based on weighted pool mechanics (similar to Balancer), adapted with Curve-style virtual balances for gas optimization.
The Pool’s Core Mechanism
Unlike simple constant-product AMMs (x × y = k), the yETH pool uses a sophisticated invariant that accounts for:
Multiple assets (up to 32)
Weighted ratios for each asset
Exchange rates for LSDs (wstETH, rETH, cbETH, etc.)
The pool stores these virtual balances in state variables to avoid recalculating them on every operation—a gas optimization that became the source of the vulnerability.
The Vulnerability: Incomplete State Cleanup
The Core Bug
The vulnerability exists in the interaction between two functions: remove_liquidity() and add_liquidity().
In remove_liquidity() (lines 590-654):
The Problem: When ALL LP tokens are burned (supply == 0), the virtual balances are decremented proportionally but never explicitly reset to zero. Due to rounding, tiny amounts remain in self.packed_vbs[].S
In add_liquidity() (lines 523-528):
In _calc_vb_prod_sum() (lines 729-744):
The Fatal Flaw: This function reads self.packed_vbs[asset] from storage, expecting them to be zero for a “first deposit” scenario. However, after multiple deposit/withdrawal cycles, these storage slots contain accumulated residual values that were never reset.
The Exploit Transaction: A Technical Walkthrough
Phase 1: Capital Acquisition
The attacker borrowed assets via flash loans from Balancer and Aave, obtaining wstETH, rETH, WETH, ETHx, and cbETH without upfront capital.
Phase 2: State Poisoning
The attacker executed multiple deposit-withdrawal cycles to accumulate residual values in packed_vbs[] storage. Each cycle deposited assets into vaults and the yETH pool, then withdrew portions. The virtual balances decremented but never fully reset.
Phase 3: Pool Drain
The attacker burned all remaining LP tokens, setting self.supply = 0 while self.packed_vbs[] retained accumulated values and was NOT reset.
Phase 4: Exploit
The attacker deposited minimal wei amounts across all supported tokens. The protocol treated this as an initial deposit and read stale storage values, minting septillions of yETH tokens instead of calculating from the actual dust deposit.
Phase 5: Fund Extraction
The attacker swapped the minted yETH tokens for WETH on Balancer pools and withdrew the underlying assets (sfrxETH, wstETH, ETHx, cbETH, rETH, apxETH, wOETH, mETH) from the pool.
Phase 6: Cleanup
The attacker converted all stolen assets to ETH via Uniswap V3 and other DEXs, repaid all flash loans with fees, and sent a portion to Tornado Cash for laundering while retaining the remainder as profit.
The Design Bug
The yETH pool holds multiple LSDs (liquid staking derivatives) with different values for example:
1 wstETH ≈ 1.15 ETH
1 rETH ≈ 1.08 ETH
1 cbETH ≈ 1.00 ETH
To calculate how many LP tokens to give you, the pool needs to:
Doing this EVERY time is expensive gas-wise, so instead of recalculating every time, the pool:
Calculates once when you deposit/withdraw
Stores the result in packed_vbs[]
Reuses this cached value in future calculations
Expensive (done every operation without caching):
Cheap (with caching):
What Happens When It’s Not Zero When It Should Be?
Normal Flow (Working Correctly), scenario: Pool has 100 ETH worth of assets
Bug Scenario (When Not Reset) What the code ASSUMES when supply == 0:
What ACTUALLY happens after full withdrawal:
The pool was designed to store virtual balances in state to save gas on recalculations. This is a common optimization pattern in DeFi:
The Missing Edge Case
The developers correctly handled the normal flow:
Adding liquidity updates virtual balances ✓
Removing liquidity decrements virtual balances ✓
Swapping updates virtual balances ✓
But they missed the edge case:
Removing ALL liquidity should RESET virtual balances to zero ✗
The Implicit Assumption
The code assumed that when prev_supply == 0, this meant a “first-ever deposit” to a pristine pool. But after a full withdrawal, prev_supply == 0 while packed_vbs[] contained residual state from previous operations.
Conclusion
The yETH exploit stands as a masterclass in finding and exploiting subtle state management bugs. The attacker demonstrated deep understanding of:
The protocol’s mathematical invariants
Storage layout and state persistence
How to manipulate state across multiple transactions
How to maximize impact with minimal capital
For defenders, this exploit reinforces that correctness in complex systems requires explicit handling of ALL state transitions, not just the happy path. A missing state reset—a single oversight in 1000+ lines of code—enabled the theft of $9 million.
As DeFi protocols grow more complex, incorporating novel AMM designs and mathematical optimizations, the attack surface for such subtle bugs expands. The only defense is rigorous engineering discipline: explicit state management, comprehensive testing, and the humility to assume that if something CAN go wrong, eventually someone will find a way to exploit it.
How this could have been prevented
Onchain security must evolve from post-incident forensics to real-time prevention:
→ Simulate transactions before execution to catch abnormal token minting ratios (16 wei in → septillions out is not normal)
→ Track state across transaction sequences — this attack required 10+ deposit/withdrawal cycles to poison packed_vbs[]. Single-transaction monitoring would miss it
→ Block execution automatically when drain patterns emerge, not just alert after the fact
The difference:
Seeing the exploit after $9M is gone vs.
Stopping the malicious add_liquidity() before it executes
The Lesson: A single missing state reset — packed_vbs[] not clearing when supply hit zero — enabled this entire attack. Complex DeFi systems need runtime protection that understands protocol logic, not just signature-based detection.
Learn more about Check Point’s Blockchain Security solution here.
OpenAI Codex CLI is OpenAI’s command-line tool that brings AI model-backed reasoning into developer workflows. It can read, edit, and run code directly from the terminal, making it possible to interact with projects using natural language commands, automate tasks, and streamline day-to-day development One of its key features is MCP (Model Context Protocol) – a standardized way to integrate external tools and services into the Codex environment, allowing developers to extend the CLI’s capabilities with custom functionality and automated workflows.
Research Motivation
We tested whether Codex safely handles project-supplied configuration and environment overrides automatically loaded at runtime, and whether implicit trust in those project files, which the CLI may read and execute without explicit user consent or provenance checks, can be abused in collaborative workflows.
Our Research Findings
During testing, we found that Codex CLI will automatically load and execute MCP server entries from a project-local configuration whenever codex is run inside that repository., Concretely, if a repository contains a .env that sets CODEX_HOME=./.codex and an accompanying ./.codex/config.toml with mcp_servers entries, Codex CLI resolves its config to that local folder, parses the MCP definitions, and invokes the declared command/args immediately at startup. There is no interactive approval, no secondary validation of the command or arguments, and no re-check when those values change — the CLI treats the project-local MCP configuration as trusted execution material.
This sequence turns ordinary repository files into an execution vector: an attacker who can commit or merge a .env and a ./.codex/config.toml can cause arbitrary commands to run on any developer who clones the repo and runs codex. In practice, we demonstrated this with deterministic payloads (file-creation) and by replacing benign commands with reverse-shell payloads; both executed without user prompts. Because the behavior binds trust to the presence of the MCP entry under the resolved CODEX_HOME rather than to the contents of the entry, an initially innocuous config can be swapped for a malicious one post-approval or post-merge, creating a stealthy, reproducible supply-chain backdoor that triggers on normal developer workflows.
Technical Deep Dive
Codex resolves its configuration path at startup, then parses and materializes any MCP server entries it finds so they’re available to the runtime. When the effective CODEX_HOME points at a repository folder, Codex treats that repo-level config as the authoritative source and will invoke the command + args listed under mcp_servers as part of expected startup/automation flows. In the vulnerable behavior, there is no secondary validation, no interactive approval, and no re-check when the command/args change. The CLI simply runs what the project config declares.
This means an attacker can perform the following steps:
Prepare a repository with a benign-looking project structure.
2. Add a .env that redirects configuration to the repo:
3. Commit a ./.codex/config.toml containing an mcp_servers entry that declares command + args. In this example, we used a harmless file-creation payload, but the same chain can be swapped for a reverse shell.
4. When a developer clones or updates the project and runs codex, the repo .env setting CODEX_HOME=./.codex causes Codex to load ./.codex/config.toml and execute its mcp_servers.*.command immediately, without prompting. The command runs in the user’s context; an attacker can silently swap in a reverse shell, exfiltrate data, or harvest credentials. In the image example below, we demonstrate this by opening Calculator on the victim machine.
Real World Consequences
This vulnerability enables silent, repeatable remote code execution in any environment where developers run codex against a repository. By abusing project-local config loading, an attacker who can land a commit or PR can turn an otherwise innocent repo into a persistent backdoor that triggers whenever a developer runs codex, with no additional prompts or approvals.
An attacker with write or PR access can:
Achieve persistent remote access: Embed a reverse shell or persistent payload in ./.codex/config.toml (delivered alongside a .env that redirects CODEX_HOME) and regain access each time a developer runs codex.
Execute arbitrary commands silently: Any shell command defined in an MCP entry runs immediately in the user’s context whenever codex loads the project config.
Escalate and exfiltrate: Developer machines frequently hold cloud tokens, SSH keys, and source; attackers can harvest credentials, exfiltrate secrets, or push further exploits.
Persist and swap payloads post-merge: Because trust is tied to the resolved config location rather than the contents, an initially harmless entry can be replaced later with malicious commands without triggering re-approval.
Propagate via supply-chain artifacts: Compromised templates, starter repos, or popular open-source projects can weaponize many downstream consumers with a single commit.
Contaminate CI and build pipelines: If CI, automation, or build agents run codex on checked-out code, the compromise can move from workstations into build artifacts and downstream deployments.
Enable lateral movement and privilege escalation: With harvested credentials and local access, an attacker can pivot to cloud resources, repositories, or internal networks.
This breaks the CLI’s expected security boundary: project-supplied files become trusted execution material, and that implicit trust can be exploited with minimal effort and no user interaction beyond standard development workflow.
Responsible disclosure timeline:
Check Point Research responsibly disclosed the issue to the OpenAI Codex CLI team on August 7, 2025.
OpenAI issued a fix on August 20, 2025, in Codex CLI version 0.23.0. The patch prevents .env files from silently redirecting CODEX_HOME into project directories, closing the automatic execution path we demonstrated.
Our testing confirmed the fix is effective. Codex CLI now blocks project-local redirection of CODEX_HOME, requiring safer defaults and stopping immediate execution of attacker-supplied project files.
To ensure protection, we strongly recommend all users update to Codex CLI version 0.23.0 or later.
Record fragmentation and decentralization: The number of active extortion groups in Q3 2025 rose to a record of 85 groups, the highest number observed to date. The top 10 groups accounted only for 56% of all published victims, down from 71% in Q1.
Stable high activity: Ransomware victim postings stabilized at an average of 535 victims per month, up from 420 year-over-year (YoY) in Q2–Q3 2024, but below the Q1 2025 peak.
LockBit’s return: Lockbit’s reappearance with the release of LockBit 5.0 in September 2025 potentially signals affiliate re-centralization under a major brand
Emerging dominance: Qilin became the most active group, averaging 75 victims per month, followed by Akira, INC Ransom, and Play.
Regional concentration shifts: South Korea entered the top 10 list of most attacked countries following a focused campaign by Qilin targeting its financial sector.
Sector impact: Manufacturing and business services remained the most affected sectors. Healthcare held steady at 8% of total victims, despite selective avoidance by major actors like Play.
Ransomware in Q3 2025: RaaS fragmentation increases and Lockbit is back
During the third quarter of 2025, we monitored more than 85 active data leak sites (DLS) that collectively listed 1,592 new victims. Compared to the 1,607 victims reported in Q2 2025, the publication rate remained stable though it is still notably higher than the 1,270 victims recorded in Q3 2024 (a 25% increase YoY). Overall, there are approximately 520 to 540 new victims per month, indicating that ransomware activity has plateaued albeit at historically high levels.
Figure 1 – Total Number of Reported Ransomware Victims in DLS, per month.
Despite the termination of several prominent ransomware groups, the overall number of active threat actors continues to grow, with new ones appearing every month.
During Q3 2025, we observed a steady expansion of double-extortion activity, driven mostly by small and emerging operators. Of the 85 data leak sites tracked this quarter, 47 groups published fewer than ten victims, suggesting a growing number of affiliates moving beyond established ransomware-as-a-service (RaaS) programs to conduct attacks independently.
This fragmentation follows the closure or dormancy of several major RaaS brands during the year, including RansomHub, 8Base, BianLian, Cactus, and others. In Q3 alone, 14 new groups began publishing victims, bringing the total number of newly observed actors in 2025 to 45. In Q1, the ten most active groups accounted for 71% of all DLS postings. In Q2, their share fell to 63%, and by Q3, to just 56%.
These findings illustrate the limited long-term impact of law-enforcement operations on the overall number of ransomware victims. Despite several high-profile takedowns during the past year—most of them directed at large RaaS operations such as LockBit, 8Base, and Blacksuit—the total volume of attacks did not significantly decline. Instead, the attacks continue a gradual upward trend, from an average of approximately 420 victims per month in Q2–Q3 2024 to about 535 per month in the same period of 2025.
This limited effect appears to stem from the focus of enforcement efforts: takedown operations primarily target RaaS infrastructure and administrators, which does not affect the affiliate operators who conduct the intrusions and drive the operational execution. When a major RaaS platform is disrupted, these affiliates typically migrate to alternative programs or establish their own data-leak sites, resulting in only short-term interruptions to overall activity levels.
The effects of affiliate mobility are evident both in the proliferation of new leak sites and in the rising activity of existing groups. Qilin, the most active actor in Q3 2025—and one of the most aggressive in recruiting former RansomHub affiliates—averaged around 75 victims per month, up from 36 in Q1 prior to RansomHub’s closure in April. INC Ransomware increased its monthly total from 23 to 39 victims, and Play went from 28 to 33 during the same period.
This ongoing fragmentation of the ecosystem may further erode ransomware operators’ reliability. Victims traditionally rely on attackers reputation to supply decryption keys after payment. Large RaaS brands have a commercial incentive to maintain credibility and provide the keys, but smaller, short-lived groups do not, leading to reduced payment rates which are currently estimated at 25–40% of total attacks.
In this context, the re-centralization of affiliates around major, recognizable brands remains strategically significant. Large RaaS programs preserve their market advantage through stability, reputation, and structured affiliate infrastructure. The re-emergence of LockBit, discussed in the following section, may represent precisely such a re-consolidation of affiliates under a stable and trusted brand identity.
Figure 2 – Ransomware Groups by Publicly Claimed Victims – Q3 2025.
Qilin, Akira, INC Ransom, Play, and Safepay maintained their positions among the most active groups in Q3. Warlock and The Gentlemen emerged more recently, both demonstrating rapid early activity: Warlock began posting victims in June 2025 and reached 43 total listings in Q3, while The Gentlemen claimed 38 victims during a single month of operation in September 2025.
Lockbit 5.0 is here – is LockBit truly back?
Until its disruption during Operation Cronos in early 2024, LockBit dominated the RaaS ecosystem, accounting for 20–30% of all published victims. Following the takedown, several arrests were announced, and successor groups — first RansomHub, then Qilin — attempted to inherit its affiliate base. However, the group’s core administrator, known as LockBitSupp, was never apprehended and in underground forums continued to hint at an eventual comeback.
The release of LockBit 5.0 in September 2025 marks the group’s return to active operations, reigniting questions about whether the RaaS landscape may again consolidate around this long-standing brand.
Figure 3 – LockBit’s share of all DLS-published victims.
LockBit had long promised a comeback. In May 2025, following the latest in a series of public setbacks, the group’s administrator, LockBitSupp, responded on the RAMP underground forum, declaring that they would “always rise up after being hacked.”
Figure 4 – LockBit administrator vowing to return on the RAMP forum.
Figure 5 – LockBit administrator announcing the group’s return on RAMP chat.
The XSS Russian-language cybercriminal forum previously banned LockBitSupp following a dispute with another user and reaffirmed that “explicit advertising of RaaS will remain prohibited.” RaaS programs depend on affiliate recruitment for their business model and maintaining visibility on prominent criminal forums such as XSS or RAMP is essential to their work.
By early September, the XSS administrator reported that LockBitSupp requested to be reinstated and opened the decision to a community vote.
Figure 6 – XSS forum administrator polling members on whether to reinstate LockBitSupp.
Despite this effort, the vote ultimately failed, and LockBit remains banned from XSS.
Figure 7 – Voting results on LockBitSupp’s proposed return to XSS.
In early September 2025, LockBit announced on RAMP the official launch of LockBit 5.0, coinciding with the sixth anniversary of the operation. New affiliates were asked to provide a Bitcoin deposit of roughly US $500 for access to a new encryptor and an updated control panel.
Since that announcement, we identified more than 15 distinct victims affected by LockBit 5.0, which replaced the earlier 4.0 builds that were still active until April 2025. LockBit continues to enforce strict operational security: all affiliate interfaces require individualized credentials, and no victims have been publicly listed on the group’s data-leak site.
Figure 9 – LockBit 5.0 ransom note from an attack in mid-September 2025
Updated ransom notes now explicitly identify themselves as “LockBit 5.0” and include a unique personal identifier that allows each victim to access a private negotiation portal. Victims are typically granted a 30-day grace period before the stolen data is published.
Figure 10 – Screenshot of LockBit 5.0 negotiations with a victim, mid-September 2025.
Analysis of the initial campaign shows that approximately 65 percent of identified attacks targeted organizations in the United States, with the remainder affecting Mexico, Indonesia, and several European countries.
LockBit 5.0 represents an upgraded evolution of the previous 4.0 version, incorporating Windows, Linux, and ESXi variants. The new build introduces enhanced evasion and anti-analysis mechanisms, faster encryption routines, and the use of a randomized 16-character file extension to disrupt signature-based detection. Most confirmed infections were deployed on Windows systems, while roughly 20 percent targeted ESXi virtual infrastructure.
Historically, LockBit has been among the most active and disruptive RaaS programs. With its extensive experience and the lower entry barrier for new affiliates, the re-emergence of the group poses a renewed risk to organizations across many sectors. The actions observed in September likely represent only the leading edge of a larger campaign, and the October victim postings on LockBit’s data-leak site are expected to confirm its full operational return.
DragonForce’s marketing efforts
DragonForce distinguishes itself among emerging ransomware groups through its heavy emphasis on public relations and coalition branding, frequently issuing high-profile statements and partnership claims on criminal forums. In September 2025, it announced on RAMP a supposed “coalition” with Qilin and LockBit, presented as a unified affiliate initiative.
However, these declarations appear largely symbolic, with no verified evidence of shared infrastructure or joint operations. The announcements likely serve to attract affiliates and project influence within a fragmented RaaS market. This reflects DragonForce’s broader strategy to maintain visibility and credibility in an increasingly competitive underground ecosystem.
Figure 11 – DragonForce announcing updates and coalition with LockBit and Qilin.
DragonForce roughly tripled its monthly victim count since the shutdown of RansomHub and claimed 56 victims in Q3 2025. This is still fewer than Qilin and Akira but shows steady growth. DragonForce continues to actively recruit affiliates and promote new features in its RaaS program and recently announced on RAMP a data-driven extortion service that offers affiliates tailored analysis of stolen data to maximize ransom leverage.
Figure 12 – DragonForce’s “data audit” services.
Under this model, an affiliate that accessed a large dataset (typically over 300 GB) from a company with annual revenues above US $15 million can submit it for analysis and maximize the extortion impact. In a recent showcased example, DragonForce reviewed stolen files from a gold mining company and highlighted the most valuable commercial and financial information, accompanied by a customized extortion letter.
Figure 13 – DragonForce Audit example.
Qilin – The Dominant RaaS Operation of 2025
Qilin remains the most active ransomware group in 2025, increasing its monthly victim rate to an average of 75 victims in Q3, up from 36 in Q1.
Although the group presents itself as ideologically motivated, its operations appear entirely financially driven. In a June 2024 interview published on its WikiLeaksV2 blog, Qilin’s operators described themselves as “idealists who love our country.” This statement, however, contrasts sharply with the group’s broad and opportunistic targeting across sectors and geographies.
Figure 14 – Qilin interview from June 2024 on their official blog.
In a separate interview on SuspectFile, a Qilin affiliate characterized the program as profit-focused and flexible, with affiliates responsible for intrusion and exfiltration while Qilin manages infrastructure, leak-site operations, and negotiations. Reported affiliate share ranges between 80% and 85%, which is among the highest in the market. This has attracted numerous operators previously active under RansomHub and BianLian.
Qilin’s open affiliate framework accommodates actors with diverse motivations and capabilities. In one recent case, the group’s data-leak site briefly listed Israel’s Shamir Medical Center among its victims.
Figure 15 – Shamir Medical Center announcement on Qilin’s DLS.
According to Israeli researcher Erez Dassa, the responsible affiliate was likely an Iranian-linked threat actor. Dassa reported that following direct communication, Qilin’s administrators agreed that public association with terrorism or politically motivated activity could expose the group to additional pressure and subsequently removed the listing.
Figure 16- Erez Dassa’s Telegram post explaining the Shamir incident.
This incident illustrates the range of motivations operating within large RaaS ecosystems. While affiliates enjoy broad autonomy, Qilin demonstrates a degree of central oversight and reputational management, removing politically sensitive cases that may jeopardize its long-term operations. The group continues to balance open recruitment and strategic control, maintaining its position as a leading and resilient RaaS brand.
Geographic Distribution of Victims – Q3 2025
The geographic distribution of ransomware victims in Q3 2025 continues to follow established trends in the global ransomware ecosystem. The United States accounted for roughly half of all reported cases, reaffirming its position as the primary target for financially motivated threat actors. Most publicly listed victims remain concentrated in Western, developed economies, where organizations are perceived as having greater financial resources and a higher likelihood of paying ransom.
Figure 14- Publicly Claimed Victims by Countries, Q3 2025.
An unusual concentration of attacks in South Korea elevated the country to seventh place on the list of most affected nations this quarter. This spike is linked almost entirely to Qilin, which listed 30 South Korean victims, 28 of them between August and September 2025. Of these, 23 belonged to the financial services sector. While the cause of this focused campaign remains uncertain, the Korea Herald newspaper attributed it to a compromised cloud server operated by an IT contractor serving multiple mid-sized private equity funds.
Safepay maintained a strong focus on Germany and the United Kingdom, with each accounting for approximately 10% of its total victims in Q3. Together with INC Ransom, which also recorded 11 British victims, these two groups were the most active in the UK, followed closely by Qilin with 10.
Twenty percent of DragonForce’s victims were based in Germany, making it the most active ransomware group in the country.
INC Ransom reported a notably high proportion of Canadian victims (8%), the largest share of any group targeting Canadian organizations this quarter.
Figure 15- Canadian Victims by Actor, Q3 2025.
Ransomware Attacks by Industry – Q3 2025 Analysis
The industry distribution of ransomware victims in Q3 2025 shows a consistent cross-sector impact, largely unchanged from previous quarters. Profit-oriented organizations with high downtime costs, such as industrial manufacturing, and those holding sensitive or regulated data, such as business services, remained the most targeted sectors, each representing approximately 10% of all extortion attempts.
Healthcare and medical organizations continue to face steady targeting due to the critical nature of their operations and the sensitivity of stored information. Interestingly, threat actors view these victims as high-risk targets because of the attention such incidents attract from law enforcement. In Q2 2025, healthcare accounted for approximately 8% of all publicly listed victims, a figure that remained stable through Q3.
While some groups, such as Play Ransomware, appear to enforce an internal policy against attacking healthcare institutions, others such as Qilin, INC Ransomware, and KillSec continue to list healthcare providers among their victims, disregarding the informal ethical boundaries observed by larger RaaS brands.
Figure 16 – Ransomware Victims by Industry, Q3 2025.
Conclusion
The ransomware ecosystem in Q3 2025 remains highly active and structurally fragmented, while at the same time increasingly adaptive. The dissolution of major RaaS programs did not reduce the overall volume of attacks but instead redistributed the load among numerous smaller and more agile actors. Groups such as Qilin and DragonForce have capitalized on this trend, expanding through aggressive affiliate recruitment and service innovation. LockBit’s re-emergence signals a potential trend toward re-centralization around trusted brands. Despite periodic law-enforcement disruptions, the fundamental dynamics of the ransomware market, using affiliate-driven operations, data extortion models, and cross-platform tooling, remain intact. Continued monitoring of affiliate migration patterns and emerging extortion techniques are essential to understanding how this decentralized ecosystem evolves through the remainder of 2025.
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