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August 18, 2020

Evil Corp's WastedLocker Ransomware Attacks Observation

Darktrace detects Evil Corp intrusions with WastedLocker ransomware. Learn how AI spotted malicious activity, from initial intrusion to data exfiltration.
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
Max Heinemeyer
Global Field CISO
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18
Aug 2020

Darktrace has recently observed several targeted intrusions associated with Evil Corp, an advanced cyber-criminal group recently in the headlines after a surge in WastedLocker ransomware cases. The group is believed to have targeted hundreds of organizations in over 40 countries, demanding ransoms of $500,000 to $1m to unlock computer files it seizes. US authorities are now offering a $5m reward for information leading to the arrest of the group’s leaders — understood to be the largest sum of money ever offered for a cyber-criminal.

Thanks to its self-learning nature, Darktrace's AI detected these intrusions without the use of any threat intelligence or static Indicators of Compromise (IoCs). This blog describes the techniques, tools and procedures used in multiple intrusions by Evil Corp – also known as TA505 or SectorJ04.

Key takeaways

  • The threat actor was reusing TTPs as well as infrastructure across multiple intrusions
  • Some infrastructure was only observed in individual intrusions
  • While most WastedLocker reports focus on the ransomware, Darktrace has observed Evil Corp conducting data exfiltration
  • The attacker used various ‘Living off the Land’ techniques for lateral movement
  • Data exfiltration and ransomware activity took place on weekends, likely to reduce response capabilities of IT teams
  • Although clearly an advanced actor, Evil Corp can be detected and stopped before encryption ensues

Evil Corp ransomware attack

Figure 1: The standard attack lifecycle observed in Evil Corp campaigns

Initial intrusion

While Evil Corp is technically sophisticated enough to choose from an array of initial intrusion methods, fake browser updates were the weapon of choice in the observed campaign. These were delivered from legitimate websites and used social engineering to convince users to download these malicious ‘updates’. Evil Corp has actually built a framework around this capability, referred to as SocGholish.

Establishing foothold / Command & Control Traffic

Darktrace detected different C2 domains being contacted after the initial infection. These domains overlap across various victims, showing that the attacker is reusing infrastructure within the same campaign. The C2 communication – comprised of thousands of connections over several days – took place over encrypted channels with valid SSL certificates. No single infected device ever beaconed to more than one C2 domain at a time.

Two example C2 domains are listed below with more details:

techgreeninc[.]com

SSL beacon details:

  • Median beacon period: 3 seconds
  • Range of periods: 1 seconds - 2.58 minutes
  • Data volume sent per connection on average: 921 Bytes

investimentosefinancas[.]com

SSL beacon details:

  • Median beacon period: 1.7 minutes
  • Range of periods: 1 seconds - 6.68 minutes
  • Data volume sent per connection on average: 935 Bytes

Certificate information:

  • Subject: CN=investimentosefinancas.com
  • Issuer: CN=Thawte RSA CA 2018,OU=www.digicert.com,O=DigiCert Inc,C=US
  • Validation status: OK

Note in particular the median beacon period, which indicates that some C2 channels were much more hands-on, whilst others possibly acted as backup channels in case the main C2 was burned or detected. It’s also interesting to see the low amount of data being transferred to the hands-on C2 domains. The actual data exfiltration took place to yet another C2 destination, intentionally separated from the hands-on intrusion C2s. All observed C2 websites were recently registered with Russian providers and are not responsive (see below).

Figure 2: The unresponsive C2 domain

Registrar: reg.ru

Created: 2020-06-29 (6 weeks ago) | Updated: 2020-07-07 (5 weeks ago)

Figure 3: Some key information relating to the C2 domain

Darktrace’s Cyber AI Platform detected this Command & Control activity via various behavioral indicators, including unusual beaconing and unusual usage of TLS (JA3).

Internal reconnaissance

In some cases, Darktrace witnessed several days of inactivity between establishing C2 and internal reconnaissance. The attackers used Advanced Port Scanner, a common IT tool, in a clear attempt to blend in with regular network activity. Several hundred IPs and dozens of popular ports were scanned at once, with tens of thousands of connections made in a short period of time.

Some key ports scanned were: 21, 22, 23, 80, 135, 139, 389, 443, 445, 1433, 3128, 3306, 3389, 4444, 4899, 5985, 5986, 8080. Darktrace detected this anomalous behavior easily as the infected devices don’t usually scan the network.

Lateral movement

Different methods of lateral movement were observed across intrusions, but also within the same intrusion, with WMI used to move between devices. Darktrace detected this by identifying when WMI usage was unusual or new for a device. An example of the lateral movement is shown below, with Darktrace detecting this as ‘New Activity’.

Figure 4: The model breach event log

PsExec was used where it already existed in the environment and Darktrace also witnessed SMB drive writes to hidden shares to copy malware, e.g.

C$ file=Programdata\[REDACTED]4rgsfdbf[REDACTED]

A malicious Powershell file was downloaded – partly shown in the screenshot below.

Figure 5: The malicious Powershell file

Accomplish mission – Data exfiltration or ransomware deployment

Evil Corp is currently best known for its WastedLocker ransomware. Whilst some of its recent intrusions have seen ransomware deployments, others have been classic cases of data exfiltration. Darktrace has not yet observed a double-threat – a case of exfiltration followed by ransomware.

The data exfiltration took place over HTTP to generic .php endpoints under the attacker’s control.

How Cyber AI Analyst reported on WastedLocker

When the first signs of anomalous activity were picked up by Darktrace’s Enterprise Immune System, Cyber AI Analyst automatically launched a full investigation and quickly provided a full overview of the overall incident. The AI Analyst continued to add more details to the ongoing incident as it evolved. There were a total of six AI Analyst incidents for the week spanning an example Evil Corp intrusion – and two of them directly covered the Evil Corp attack. In stitching together disparate security events and presenting a single narrative, Cyber AI Analyst did all the heavy lifting for human security staff, who could look at just a handful of fully-investigated incidents, instead of having to triage countless individual model breaches.

Figure 6: Cyber AI Analyst’s overview of the incident

Note how AI Analyst covers five phases of the attack lifecycle in a single incident report:

  1. Unusual Repeated Connections – Initial C2
  2. Possible HTTP Command & Control Traffic – Further C2
  3. Possible SSL Command & Control Traffic – Further C2
  4. Scanning of Multiple Devices – Internal reconnaissance with Advanced IP Scanner
  5. SMB Writes of Suspicious Files – Lateral Movement

Evil Corp rising

Every indicator suggests that this was not a case of indiscriminate ransomware, but rather highly sophisticated and targeted attacks by an advanced threat actor. With the ultimate goal of ransoming operations, the attacker moved towards the crown jewels of the organization: file servers and databases.

The organizations involved in the above analysis did not have Darktrace Antigena – Darktrace’s Autonomous Response technology – in active mode, and the threat was therefore allowed to escalate beyond its initial stages. With Antigena in full operation, the activity would have been contained at its early stages with a precise and surgical response which would have stopped the malicious behavior whilst allowing the business to operate as normal.

Despite the targeted and advanced nature of the threat, security teams are perfectly capable of detecting, investigating, and stopping the threat with Cyber AI. Darktrace was able to not only detect WastedLocker ransomware based on a series of anomalies in network traffic, but also stitch together those anomalies and investigate the incident in real time, presenting an actionable summary of the different attack stages without flooding the security team with meaningless alerts.

Learn more about Autonomous Response

Network IoCs:

IoCCommenttechgreeninc[.]comC2 domaininvestimentosefinancas[.]comC2 domain

Selected associated Darktrace model breaches:

  • Compromise / Beaconing Activity To External Rare
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / Suspicious Beaconing Behaviour
  • Device / New or Unusual Remote Command Execution
  • Compromise / Beaconing Activity To External Rare
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Slow Beaconing Activity To External Rare
  • Device / New User Agent
  • Unusual Activity / Unusual Internal Connections
  • Device / Suspicious Network Scan Activity
  • Device / Network Scan
  • Device / Network Scan - Low Anomaly Score
  • Device / ICMP Address Scan
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Compromise / SSL Beaconing to Rare Destination
  • Anomalous Connection / SMB Enumeration
  • Compliance / SMB Drive Write
  • Anomalous File / Internal / Unusual SMB Script Write

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
Max Heinemeyer
Global Field CISO

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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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