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November 6, 2022

Behind Yanluowang: Unveiling Cyber Threat Tactics

Discover the latest insights into the Yanluowang leak organization, uncovering its members and tactics.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Taisiia Garkava
Security Analyst
Written by
Dillon Ashmore
Security and Research
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06
Nov 2022

Background of Yanluowang

Yanluowang ransomware, also known as Dryxiphia, was first spotted in October 2021 by Symantec’s Threat Hunter Team. However, it has been operational since August 2021, when a threat actor used it to attack U.S. corporations. Said attack shared similar TTPs with ransomware Thieflock, designed by Fivehands ransomware gangs. This connection alluded to a possible link between the two through the presence or influence of an affiliate. The group has been known for successfully ransoming organisations globally, particularly those in the financial, manufacturing, IT services, consultancy, and engineering sectors.

Yanluowang attacks typically begin with initial reconnaissance, followed by credential harvesting and data exfiltration before finally encrypting the victim’s files. Once deployed on compromised networks, Yanluowang halts hypervisor virtual machines, all running processes and encrypts files using the “.yanluowang” extension. A file with name README.txt, containing a ransom note is also dropped. The note also warns victims against contacting law enforcement, recovery companies or attempting to decrypt the files themselves. Failure to follow this advice would result in distributed denial of service attacks against a victim, its employees and business partners. Followed by another attack, a few weeks later, in which all the victim’s files would be deleted.

The group’s name “Yanluowang” was inspired by the Chinese mythological figure Yanluowang, suggesting the group’s possible Chinese origin. However, the recent leak of chat logs belonging to the group, revealed those involved in the organisation spoke Russian. 

 Leak of Yanluowang’s chat logs

 On the 31st of October, a Twitter user named @yanluowangleaks shared the matrix chat and server leaks of the Yanluowang ransomware gang, alongside the builder and decryption source. In total, six files contained internal conversations between the group’s members. From the analysis of these chats, at least eighteen people have been involved in Yanluowang operations.

Twitter account where the leaks and decryption source were shared
Figure 1: Twitter account where the leaks and decryption source were shared

Potential members: ‘@killanas', '@saint', '@stealer', '@djonny', '@calls', '@felix', '@win32', '@nets', '@seeyousoon', '@shoker', '@ddos', '@gykko', '@loader1', '@guki', '@shiwa', '@zztop', '@al', '@coder1'

Most active members: ‘@saint’, ‘@killanas’, ‘@guki’, ‘@felix’, ‘@stealer’. 

To make the most sense out of the data that we analyzed, we combined the findings into two categories: tactics and organization.

Tactics 

From the leaked chat logs, several insights into the group’s operational security and TTPs were gained. Firstly, members were not aware of each other’s offline identities. Secondly, discussions surrounding security precautions for moving finances were discussed by members @killanas and @felix. The two exchanged recommendations on reliable currency exchange platforms as well as which ones to avoid that were known to leak data to law enforcement. The members also expressed paranoia over being caught with substantial amounts of money and therefore took precautions such as withdrawing smaller amounts of cash or using QR codes for withdrawals.

Additionally, the chat logs exposed the TTPs of Yanluowang. Exchanges between the group’s members @stealer, @calls and @saint, explored the possibilities of conducting attacks against critical infrastructure. One of these members, @call, was also quick to emphasise that Yanluowang would not target the critical infrastructure of former Soviet countries. Beyond targets, the chat logs also highlighted Yanluowang’s use of the ransomware, PayloadBIN but also that attacks that involved it may potentially have been misattributed to another ransomware actor, Evil Corp.

Further insight surrounding Yanluowang’s source code was also gained as it was revealed that it had been previously published on XSS.is as a downloadable file. The conversations surrounding this revealed that two members, @killanas and @saint, suspected @stealer was responsible for the leak. This suspicion was supported by @saint, defending another member whom he had known for eight years. It was later revealed that the code had been shared after a request to purchase it was made by a Chinese national. @saint also used their personal connections to have the download link removed from XSS.is. These connections indicate that some members of Yanluowang are well embedded in the ransomware and wider cybercrime community.

Another insight gained from the leaked chat logs was an expression by @saint in support of Ukraine, stating, “We stand with Ukraine” on the negotiation page of Yanluowang’s website. This action reflects a similar trend observed among threat actors where they have taken sides in the Russia-Ukraine conflict.

Regarding Yanluowang’s engagement with other groups, it was found that a former member of Conti had joined the group. This inference was made by @saint when a conversation regarding the Conti leak revolved around the possible identification of the now Yanluowang member @guki, in the Conti files. It was also commented that Conti was losing a considerable number of its members who were then looking for new work. Conversations about other ransomware groups were had with the mentioning of the REVIL group by @saint, specifically stating that five arrested members of the gang were former classmates. He backed his statement by attaching the article about it, to which @djonny replies that those are indeed REVIL members and that he knows it from his sources.

Organization 

When going through the chat logs, several observations were made that can offer some insights into the group's organizational structure. In one of the leaked files, user @saint was the one to publish the requirements for the group's ".onion" website and was also observed instructing other users on the tasks they had to complete. Based on this, @saint could be considered the leader of the group. Additionally, there was evidence indicating that a few users could be in their 30s or 40s, while most participants are in their 20s.

More details regarding Yanluowang's organizational structure were discussed deeper into the leak. The examples indicate various sub-groups within the Yanlouwang group and that a specific person coordinates each group. From the logs, there is a high probability that @killanas is the leader of the development team and has several people working under him. It is also possible that @stealer is on the same level as @killanas and is potentially the supervisor of another team within the group. This was corroborated when @stealer expressed concerns about the absence of certain group members on several occasions. There is also evidence showing that he was one of three people with access to the source code of the group. 

Role delineation within the group was also quite clear, with each user having specific tasks: DDoS (distributed denial of service) attacks, social engineering, victim negotiations, pentesting or development, to mention a few. When it came to recruiting new members, mostly pentesters, Yanluowang would recruit through XSS.is and Exploit.in forums.

Underground analysis and members’ identification 

From the leaked chat logs, several “.onion” URLs were extracted; however, upon further investigation, each site had been taken offline and removed from the TOR hashring. This suggests that Yanluowang may have halted all operations. One of the users on XSS.is posted a picture showing that the Yanluowang onion website was hacked, stating, “CHECKMATE!! YANLUOWANG CHATS HACKED @YANLUOWANGLEAKS TIME’S UP!!”.

Figure 2: The screenshot of Yanluowang website on Tor (currently offline)

After learning that Yanluowang used Russian Web Forums, we did an additional search to see what we could find about the group and the mentioned nicknames. 

By searching through XSS.Is we managed to identify the user registered as @yanluowang. The date of the registration on the forum dates to 15 March 2022. Curiously, at the time of analysis, we noticed the user was online. There were in total 20 messages posted by @yanluowang, with a few publications indicating the group is looking for new pentesters.

Figure 3: The screenshot of Yanluowang profile on XSS.is 

Figure 4: The screenshot of Yanluowang posts about pentester recruitment on XSS.is 

While going through the messages, it was noticed the reaction posted by another user identified as @Sa1ntJohn, which could be the gang member @saint.

Figure 5: The screenshot of Sa1ntJohn’s profile on XSS.is

Looking further, we identified that user @Ekranoplan published three links to the website doxbin.com containing information about three potential members of the YanLuoWang gang: @killanas/coder, @hardbass and @Joe/Uncle. The profile information was published by the user @Xander2727.

Figure 6: The screenshot of Yanlouwang member-profile leak on XSS.is
Figure 7: The screenshot of @hardbass Yanlouwang member profile leak
Figure 8: The screenshot of @killanas/coder Yanlouwang member profile leak.

If the provided information is correct, two group members are Russian and in their 30s, while another member is Ukrainian and in his 20s. One of the members, @killanas, who was also referenced in chat logs, is identified as the lead developer of the Yanluowang group; giving the interpretation of the chat leaks a high-level of confidence. Another two members, who were not referenced in the logs, took roles as Cracked Software/Malware provider and English translator/Victim Negotiator.

Implications for the wider ransomware landscape

To conclude with the potential implications of this leak, we have corroborated the evidence gathered throughout this investigation and employed contrarian analytical techniques. The ascertained implications that follow our mainline judgement, supporting evidence and our current analytical view on the matter can be categorized into three key components of this leak:

Impact on the ransomware landscape

The leak of Yanluowang’s chat logs has several implications for the broader ransomware landscape. This leak, much like the Conti leak in March, spells the end for Yanluowang operations for the time being, given how much of the group’s inner workings it has exposed. This could have an adverse effect. While Yanluowang did not control as large of a share of the ransomware market as Conti did, their downfall will undoubtedly create a vacuum space for established groups for their market share. The latter being a consequence of the release of their source code and build tools. 

Source code

The release of Yanluowang’s source code has several outcomes. If the recipients have no malintent, it could aid in reverse engineering the ransomware, like how a decryption tool for Yanluowng was released earlier this year. An alternative scenario is that the publication of the source code will increase the reach and deployment of this ransomware in the future, in adapted or modified versions by other threat actors. Reusing leaked material is notorious among ransomware actors, as seen in the past, when Babuk’s source code was leaked and led to the development of several variants based on this leak, including Rook and Pandora. This could also make it harder to attribute attacks to one specific group.

Members

The migration of unexposed Yanluowang members to other ransomware gangs could further add to the proliferation of ransomware groups. Such forms of spreading ransomware have been documented in the past when former Conti members repurposed their tactics to join efforts with an initial access broker, UAC-0098. Yet, the absence of evidence from members expressing and/or acting upon this claim requires further investigation and analysis. However, as there is no evidence of absence – this implication is based on the previously observed behavior from members of other ransomware gangs.

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Taisiia Garkava
Security Analyst
Written by
Dillon Ashmore
Security and Research

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November 20, 2025

Managing OT Remote Access with Zero Trust Control & AI Driven Detection

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The shift toward IT-OT convergence

Recently, industrial environments have become more connected and dependent on external collaboration. As a result, truly air-gapped OT systems have become less of a reality, especially when working with OEM-managed assets, legacy equipment requiring remote diagnostics, or third-party integrators who routinely connect in.

This convergence, whether it’s driven by digital transformation mandates or operational efficiency goals, are making OT environments more connected, more automated, and more intertwined with IT systems. While this convergence opens new possibilities, it also exposes the environment to risks that traditional OT architectures were never designed to withstand.

The modernization gap and why visibility alone isn’t enough

The push toward modernization has introduced new technology into industrial environments, creating convergence between IT and OT environments, and resulting in a lack of visibility. However, regaining that visibility is just a starting point. Visibility only tells you what is connected, not how access should be governed. And this is where the divide between IT and OT becomes unavoidable.

Security strategies that work well in IT often fall short in OT, where even small missteps can lead to environmental risk, safety incidents, or costly disruptions. Add in mounting regulatory pressure to enforce secure access, enforce segmentation, and demonstrate accountability, and it becomes clear: visibility alone is no longer sufficient. What industrial environments need now is precision. They need control. And they need to implement both without interrupting operations. All this requires identity-based access controls, real-time session oversight, and continuous behavioral detection.

The risk of unmonitored remote access

This risk becomes most evident during critical moments, such as when an OEM needs urgent access to troubleshoot a malfunctioning asset.

Under that time pressure, access is often provisioned quickly with minimal verification, bypassing established processes. Once inside, there’s little to no real-time oversight of user actions whether they’re executing commands, changing configurations, or moving laterally across the network. These actions typically go unlogged or unnoticed until something breaks. At that point, teams are stuck piecing together fragmented logs or post-incident forensics, with no clear line of accountability.  

In environments where uptime is critical and safety is non-negotiable, this level of uncertainty simply isn’t sustainable.

The visibility gap: Who’s doing what, and when?

The fundamental issue we encounter is the disconnect between who has access and what they are doing with it.  

Traditional access management tools may validate credentials and restrict entry points, but they rarely provide real-time visibility into in-session activity. Even fewer can distinguish between expected vendor behavior and subtle signs of compromise, misuse or misconfiguration.  

As a result, OT and security teams are often left blind to the most critical part of the puzzle, intent and behavior.

Closing the gaps with zero trust controls and AI‑driven detection

Managing remote access in OT is no longer just about granting a connection, it’s about enforcing strict access parameters while continuously monitoring for abnormal behavior. This requires a two-pronged approach: precision access control, and intelligent, real-time detection.

Zero Trust access controls provide the foundation. By enforcing identity-based, just-in-time permissions, OT environments can ensure that vendors and remote users only access the systems they’re explicitly authorized to interact with, and only for the time they need. These controls should be granular enough to limit access down to specific devices, commands, or functions. By applying these principles consistently across the Purdue Model, organizations can eliminate reliance on catch-all VPN tunnels, jump servers, and brittle firewall exceptions that expose the environment to excess risk.

Access control is only one part of the equation

Darktrace / OT complements zero trust controls with continuous, AI-driven behavioral detection. Rather than relying on static rules or pre-defined signatures, Darktrace uses Self-Learning AI to build a live, evolving understanding of what’s “normal” in the environment, across every device, protocol, and user. This enables real-time detection of subtle misconfigurations, credential misuse, or lateral movement as they happen, not after the fact.

By correlating user identity and session activity with behavioral analytics, Darktrace gives organizations the full picture: who accessed which system, what actions they performed, how those actions compared to historical norms, and whether any deviations occurred. It eliminates guesswork around remote access sessions and replaces it with clear, contextual insight.

Importantly, Darktrace distinguishes between operational noise and true cyber-relevant anomalies. Unlike other tools that lump everything, from CVE alerts to routine activity, into a single stream, Darktrace separates legitimate remote access behavior from potential misuse or abuse. This means organizations can both audit access from a compliance standpoint and be confident that if a session is ever exploited, the misuse will be surfaced as a high-fidelity, cyber-relevant alert. This approach serves as a compensating control, ensuring that even if access is overextended or misused, the behavior is still visible and actionable.

If a session deviates from learned baselines, such as an unusual command sequence, new lateral movement path, or activity outside of scheduled hours, Darktrace can flag it immediately. These insights can be used to trigger manual investigation or automated enforcement actions, such as access revocation or session isolation, depending on policy.

This layered approach enables real-time decision-making, supports uninterrupted operations, and delivers complete accountability for all remote activity, without slowing down critical work or disrupting industrial workflows.

Where Zero Trust Access Meets AI‑Driven Oversight:

  • Granular Access Enforcement: Role-based, just-in-time access that aligns with Zero Trust principles and meets compliance expectations.
  • Context-Enriched Threat Detection: Self-Learning AI detects anomalous OT behavior in real time and ties threats to access events and user activity.
  • Automated Session Oversight: Behavioral anomalies can trigger alerting or automated controls, reducing time-to-contain while preserving uptime.
  • Full Visibility Across Purdue Layers: Correlated data connects remote access events with device-level behavior, spanning IT and OT layers.
  • Scalable, Passive Monitoring: Passive behavioral learning enables coverage across legacy systems and air-gapped environments, no signatures, agents, or intrusive scans required.

Complete security without compromise

We no longer have to choose between operational agility and security control, or between visibility and simplicity. A Zero Trust approach, reinforced by real-time AI detection, enables secure remote access that is both permission-aware and behavior-aware, tailored to the realities of industrial operations and scalable across diverse environments.

Because when it comes to protecting critical infrastructure, access without detection is a risk and detection without access control is incomplete.

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance

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November 21, 2025

Xillen Stealer Updates to Version 5 to Evade AI Detection

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Introduction

Python-based information stealer “Xillen Stealer” has recently released versions 4 and 5, expanding its targeting and functionality. The cross-platform infostealer, originally reported by Cyfirma in September 2025, targets sensitive data including credentials, cryptocurrency wallets, system information, browser data and employs anti-analysis techniques.  

The update to v4/v5 includes significantly more functionality, including:

  • Persistence
  • Ability to steal credentials from password managers, social media accounts, browser data (history, cookies and passwords) from over 100 browsers, cryptocurrency from over 70 wallets
  • Kubernetes configs and secrets
  • Docker scanning
  • Encryption
  • Polymorphism
  • System hooks
  • Peer-to-Peer (P2P) Command-and-Control (C2)
  • Single Sign-On (SSO) collector
  • Time-Based One-Time Passwords (TOTP) and biometric collection
  • EDR bypass
  • AI evasion
  • Interceptor for Two-Factor Authentication (2FA)
  • IoT scanning
  • Data exfiltration via Cloud APIs

Xillen Stealer is marketed on Telegram, with different licenses available for purchase. Users who deploy the malware have access to a professional-looking GUI that enables them to view exfiltrated data, logs, infections, configurations and subscription information.

Screenshot of the Xillen Stealer portal.
Figure 1: Screenshot of the Xillen Stealer portal.

Technical analysis

The following technical analysis examines some of the interesting functions of Xillen Stealer v4 and v5. The main functionality of Xillen Stealer is to steal cryptocurrency, credentials, system information, and account information from a range of stores.

Xillen Stealer specifically targets the following wallets and browsers:

AITargetDectection

Screenshot of Xillen Stealer’s AI Target detection function.
Figure 2: Screenshot of Xillen Stealer’s AI Target detection function.

The ‘AITargetDetection’ class is intended to use AI to detect high-value targets based on weighted indicators and relevant keywords defined in a dictionary. These indicators include “high value targets”, like cryptocurrency wallets, banking data, premium accounts, developer accounts, and business emails. Location indicators include high-value countries such as the United States, United Kingdom, Germany and Japan, along with cryptocurrency-friendly countries and financial hubs. Wealth indicators such as keywords like CEO, trader, investor and VIP have also been defined in a dictionary but are not in use at this time, pointing towards the group’s intent to develop further in the future.

While the class is named ‘AITargetDetection’ and includes placeholder functions for initializing and training a machine learning model, there is no actual implementation of machine learning. Instead, the system relies entirely on rule-based pattern matching for detection and scoring. Even though AI is not actually implemented in this code, it shows how malware developers could use AI in future malicious campaigns.

Screenshot of dead code function.
Figure 3: Screenshot of dead code function.

AI Evasion

Screenshot of AI evasion function to create entropy variance.
Figure 4: Screenshot of AI evasion function to create entropy variance.

‘AIEvasionEngine’ is a module designed to help malware evade AI-based or behavior-based detection systems, such as EDRs and sandboxes. It mimics legitimate user and system behavior, injects statistical noise, randomizes execution patterns, and camouflages resource usage. Its goal is to make the malware appear benign to machine learning detectors. The techniques used to achieve this are:

  • Behavioral Mimicking: Simulates user actions (mouse movement, fake browser use, file/network activity)
  • Noise Injection: Performs random memory, CPU, file, and network operations to confuse behavioral classifiers
  • Timing Randomization: Introduces irregular delays and sleep patterns to avoid timing-based anomaly detection
  • Resource Camouflage: Adjusts CPU and memory usage to imitate normal apps (such as browsers, text editors)
  • API Call Obfuscation: Random system API calls and pattern changes to hide malicious intent
  • Memory Access Obfuscation: Alters access patterns and entropy to bypass ML models monitoring memory behavior

PolymorphicEngine

As part of the “Rust Engine” available in Xillen Stealer is the Polymorphic Engine. The ‘PolymorphicEngine’ struct implements a basic polymorphic transformation system designed for obfuscation and detection evasion. It uses predefined instruction substitutions, control-flow pattern replacements, and dead code injection to produce varied output. The mutate_code() method scans input bytes and replaces recognized instruction patterns with randomized alternatives, then applies control flow obfuscation and inserts non-functional code to increase variability. Additional features include string encryption via XOR and a stub-based packer.

Collectors

DevToolsCollector

Figure 5: Screenshot of Kubernetes data function.

The ‘DevToolsCollector’ is designed to collect sensitive data related to a wide range of developer tools and environments. This includes:

IDE configurations

  • VS Code, VS Code Insiders, Visual Studio
  • JetBrains: Intellij, PyCharm, WebStorm
  • Sublime
  • Atom
  • Notepad++
  • Eclipse

Cloud credentials and configurations

  • AWS
  • GCP
  • Azure
  • Digital Ocean
  • Heroku

SSH keys

Docker & Kubernetes configurations

Git credentials

Database connection information

  • HeidiSQL
  • Navicat
  • DBeaver
  • MySQL Workbench
  • pgAdmin

API keys from .env files

FTP configs

  • FileZilla
  • WinSCP
  • Core FTP

VPN configurations

  • OpenVPN
  • WireGuard
  • NordVPN
  • ExpressVPN
  • CyberGhost

Container persistence

Screenshot of Kubernetes inject function.
Figure 6: Screenshot of Kubernetes inject function.

Biometric Collector

Screenshot of the ‘BiometricCollector’ function.
Figure 7: Screenshot of the ‘BiometricCollector’ function.

The ‘BiometricCollector’ attempts to collect biometric information from Windows systems by scanning the C:\Windows\System32\WinBioDatabase directory, which stores Windows Hello and other biometric configuration data. If accessible, it reads the contents of each file, encodes them in Base64, preparing them for later exfiltration. While the data here is typically encrypted by Windows, its collection indicates an attempt to extract sensitive biometric data.

Password Managers

The ‘PasswordManagerCollector’ function attempts to steal credentials stored in password managers including, OnePass, LastPass, BitWarden, Dashlane, NordPass and KeePass. However, this function is limited to Windows systems only.

SSOCollector

The ‘SSOCollector’ class is designed to collect authentication tokens related to SSO systems. It targets three main sources: Azure Active Directory tokens stored under TokenBroker\Cache, Kerberos tickets obtained through the klist command, and Google Cloud authentication data in user configuration folders. For each source, it checks known directories or commands, reads partial file contents, and stores the results as in a dictionary. Once again, this function is limited to Windows systems.

TOTP Collector

The ‘TOTP Collector’ class attempts to collect TOTPs from:

  • Authy Desktop by locating and reading from Authy.db SQLite databases
  • Microsoft Authenticator by scanning known application data paths for stored binary files
  • TOTP-related Chrome extensions by searching LevelDB files for identifiable keywords like “gauth” or “authenticator”.

Each method attempts to locate relevant files, parse or partially read their contents, and store them in a dictionary under labels like authy, microsoft_auth, or chrome_extension. However, as before, this is limited to Windows, and there is no handling for encrypted tokens.

Enterprise Collector

The ‘EnterpriseCollector’ class is used to extract credentials related to an enterprise Windows system. It targets configuration and credential data from:

  • VPN clients
    • Cisco AnyConnect, OpenVPN, Forticlient, Pulse Secure
  • RDP credentials
  • Corporate certificates
  • Active Directory tokens
  • Kerberos tickets cache

The files and directories are located based on standard environment variables with their contents read in binary mode and then encoded in Base64.

Super Extended Application Collector

The ‘SuperExtendedApplication’ Collector class is designed to scan an environment for 160 different applications on a Windows system. It iterates through the paths of a wide range of software categories including messaging apps, cryptocurrency wallets, password managers, development tools, enterprise tools, gaming clients, and security products. The list includes but is not limited to Teams, Slack, Mattermost, Zoom, Google Meet, MS Office, Defender, Norton, McAfee, Steam, Twitch, VMWare, to name a few.

Bypass

AppBoundBypass

This code outlines a framework for bypassing App Bound protections, Google Chrome' s cookie encryption. The ‘AppBoundBypass’ class attempts several evasion techniques, including memory injection, dynamic-link library (DLL) hijacking, process hollowing, atom bombing, and process doppelgänging to impersonate or hijack browser processes. As of the time of writing, the code contains multiple placeholders, indicating that the code is still in development.

Steganography

The ‘SteganographyModule’ uses steganography (hiding data within an image) to hide the stolen data, staging it for exfiltration. Multiple methods are implemented, including:

  • Image steganography: LSB-based hiding
  • NTFS Alternate Data Streams
  • Windows Registry Keys
  • Slack space: Writing into unallocated disk cluster space
  • Polyglot files: Appending archive data to images
  • Image metadata: Embedding data in EXIF tags
  • Whitespace encoding: Hiding binary in trailing spaces of text files

Exfiltration

CloudProxy

Screenshot of the ‘CloudProxy’ class.
Figure 8: Screenshot of the ‘CloudProxy’ class.

The CloudProxy class is designed for exfiltrating data by routing it through cloud service domains. It encodes the input data using Base64, attaches a timestamp and SHA-256 signature, and attempts to send this payload as a JSON object via HTTP POST requests to cloud URLs including AWS, GCP, and Azure, allowing the traffic to blend in. As of the time of writing, these public facing URLs do not accept POST requests, indicating that they are placeholders meant to be replaced with attacker-controlled cloud endpoints in a finalized build.

P2PEngine

Screenshot of the P2PEngine.
Figure 9: Screenshot of the P2PEngine.

The ‘P2PEngine’ provides multiple methods of C2, including embedding instructions within blockchain transactions (such as Bitcoin OP_RETURN, Ethereum smart contracts), exfiltrating data via anonymizing networks like Tor and I2P, and storing payloads on IPFS (a distributed file system). It also supports domain generation algorithms (DGA) to create dynamic .onion addresses for evading detection.

After a compromise, the stealer creates both HTML and TXT reports containing the stolen data. It then sends these reports to the attacker’s designated Telegram account.

Xillen Killers

 Xillen Killers.
FIgure 10: Xillen Killers.

Xillen Stealer appears to be developed by a self-described 15-year-old “pentest specialist” “Beng/jaminButton” who creates TikTok videos showing basic exploits and open-source intelligence (OSINT) techniques. The group distributing the information stealer, known as “Xillen Killers”, claims to have 3,000 members. Additionally, the group claims to have been involved in:

  • Analysis of Project DDoSia, a tool reportedly used by the NoName057(16) group, revealing that rather functioning as a distributed denial-of-service (DDos) tool, it is actually a remote access trojan (RAT) and stealer, along with the identification of involved individuals.
  • Compromise of doxbin.net in October 2025.
  • Discovery of vulnerabilities on a Russian mods site and a Ukrainian news site

The group, which claims to be part of the Russian IT scene, use Telegram for logging, marketing, and support.

Conclusion

While some components of XillenStealer remain underdeveloped, the range of intended feature set, which includes credential harvesting, cryptocurrency theft, container targeting, and anti-analysis techniques, suggests that once fully developed it could become a sophisticated stealer. The intention to use AI to help improve targeting in malware campaigns, even though not yet implemented, indicates how threat actors are likely to incorporate AI into future campaigns.  

Credit to Tara Gould (Threat Research Lead)
Edited by Ryan Traill (Analyst Content Lead)

Appendicies

Indicators of Compromise (IoCs)

395350d9cfbf32cef74357fd9cb66134 - confid.py

F3ce485b669e7c18b66d09418e979468 - stealer_v5_ultimate.py

3133fe7dc7b690264ee4f0fb6d867946 - xillen_v5.exe

https://github[.]com/BengaminButton/XillenStealer

https://github[.]com/BengaminButton/XillenStealer/commit/9d9f105df4a6b20613e3a7c55379dcbf4d1ef465

MITRE ATT&CK

ID Technique

T1059.006 - Python

T1555 - Credentials from Password Stores

T1555.003 - Credentials from Password Stores: Credentials from Web Browsers

T1555.005 - Credentials from Password Stores: Password Managers

T1649 - Steal or Forge Authentication Certificates

T1558 - Steal or Forge Kerberos Tickets

T1539 - Steal Web Session Cookie

T1552.001 - Unsecured Credentials: Credentials In Files

T1552.004 - Unsecured Credentials: Private Keys

T1552.005 - Unsecured Credentials: Cloud Instance Metadata API

T1217 - Browser Information Discovery

T1622 - Debugger Evasion

T1082 - System Information Discovery

T1497.001 - Virtualization/Sandbox Evasion: System Checks

T1115 - Clipboard Data

T1001.002 - Data Obfuscation: Steganography

T1567 - Exfiltration Over Web Service

T1657 - Financial Theft

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About the author
Tara Gould
Threat Researcher
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