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December 6, 2023

How Darktrace Triumphed Over MyKings Botnet

Darktrace has provided full visibility over the MyKings botnet kill chain from the beginning of its infections to the eventual cryptocurrency mining activity.
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
Oluwatosin Aturaka
Analyst Team Lead, Cambridge
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06
Dec 2023

Botnets: A persistent cyber threat

Since their appearance in the wild over three decades ago, botnets have consistently been the attack vector of choice for many threat actors. The most prevalent of these attack vectors are distributed denial of service (DDoS) and phishing campaigns. Their persistent nature means that even if a compromised device in identified, attackers can continue to operate by using the additional compromised devices they will likely have on the target network. Similarly, command and control (C2) infrastructure can easily be restructured between infected systems, making it increasingly difficult to remove the infection.  

MyKings Botnet

One of the most prevalent and sophisticated examples in recent years is the MyKings botnet, also known as Smominru or DarkCloud. Darktrace has observed numerous cases of MyKings botnet compromises across multiple customer environments in several different industries as far back as August 2022. The diverse tactics, techniques, and procedures (TTPs) and sophisticated kill chains employed by MyKings botnet may prove a challenge to traditional rule and signature-based detections.

However, Darktrace’s anomaly-centric approach enabled it to successfully detect a wide-range of indicators of compromise (IoCs) related to the MyKings botnet and bring immediate awareness to customer security teams, as it demonstrated on the network of multiple customers between March and August 2023.

Background on MyKings Botnet

MyKings has been active and spreading steadily since 2016 resulting in over 520,000 infections worldwide.[1] Although verified attribution of the botnet remains elusive, the variety of targets and prevalence of crypto-mining software on affected devices suggests the threat group behind the malware is financially motivated. The operators behind MyKings appear to be highly opportunistic, with attacks lacking an obvious specific target industry. Across Darktrace’s customer base, the organizations affected were representative of multiple industries such as entertainment, mining, education, information technology, health, and transportation.

Given its longevity, the MyKings botnet has unsurprisingly evolved since its first appearance years ago. Initial analyses of the botnet showed that the primary crypto-related activity on infected devices was the installation of Monero-mining software. However, in 2019 researchers discovered a new module within the MyKings malware that enabled clipboard-jacking, whereby the malware replaces a user's copied cryptowallet address with the operator's own wallet address in order to siphon funds.[2]

Similar to other botnets such as the Outlaw crypto-miner, the MyKings botnet can also kill running processes of unrelated malware on the compromised hosts that may have resulted from prior infection.[3] MyKings has also developed a comprehensive set of persistence techniques, including: the deployment of bootkits, initiating the botnet immediately after a system reboot, configuring Registry run keys, and generating multiple Scheduled Tasks and WMI listeners.[4] MyKings have also been observed rotating tools and payloads over time to propagate the botnet. For example, some operators have been observed utilizing PCShare, an open-source remote access trojan (RAT) customized to conduct C2 services, execute commands, and download mining software[5].

Darktrace Coverage

Across observed customer networks between March and August 2023, Darktrace identified the MyKings botnet primarily targeting Windows-based servers that supports services like MySQL, MS-SQL, Telnet, SSH, IPC, WMI, and Remote Desktop (RDP).  In the initial phase of the attack, the botnet would initiate a variety of attacks against a target including brute-forcing and exploitation of unpatched vulnerabilities on exposed servers. The botnet delivers a variety of payloads to the compromised systems including worm downloaders, trojans, executable files and scripts.

This pattern of activity was detected across the network of one particular Darktrace customer in the education sector in early March 2023. Unfortunately, this customer did not have Darktrace RESPOND™ deployed on their network at the time of the attack, meaning the MyKings botnet was able to move through the cyber kill chain ultimately achieving its goal, which in this case was mining cryptocurrency.

Initial Access

On March 6, Darktrace observed an internet-facing SQL server receiving an unusually large number of incoming MySQL connections from the rare external endpoint 171.91.76[.]31 via port 1433. While it is not possible to confirm whether these suspicious connections represented the exact starting point of the infection, such a sudden influx of SQL connection from a rare external endpoint could be indicative of a malicious attempt to exploit vulnerabilities in the server's SQL database or perform password brute-forcing to gain unauthorized access. Given that MyKings typically spreads primarily through such targeting of internet-exposed devices, the pattern of activity is consistent with potential initial access by MyKings.[6]

Initial Command and Control

The device then proceeded to initiate a series of repeated HTTP connections between March 6 and March 10, to the domain www[.]back0314[.]ru (107.148.239[.]111). These connections included HTTP GET requests featuring URIs such as ‘/back.txt',  suggesting potential beaconing and C2 communication. The device continued this connectivity to the external host over the course of four days, primarily utilizing destination ports 80, and 6666. While port 80 is commonly utilized for HTTP connections, port 6666 is a non-standard port for the protocol. Such connectivity over non-standard ports can indicate potential detection evasion and obfuscation tactics by the threat actors.  During this time, the device also initiated repeated connections to additional malicious external endpoints with seemingly algorithmically generated hostnames such as pc.pc0416[.]xyz.

Darktrace UI image
Figure 1: Model breach showing details of the malicious domain generation algorithm (DGA) connections.

Tool Transfer

While this beaconing activity was taking place, the affected device also began to receive potential payloads from unusual external endpoints. On April 29, the device made an HTTP GET request for “/power.txt” to the endpoint 192.236.160[.]237, which was later discovered to have multiple open-source intelligence (OSINT) links to malware. Power.txt is a shellcode written in PowerShell which is downloaded and executed with the purpose of disabling Windows Defenders related functions.[7] After the initial script was downloaded (and likely executed), Darktrace went on to detect the device making a series of additional GET requests for several varying compressed and executable files. For example, the device made HTTP requests for '/pld/cmd.txt' to the external endpoint 104.233.224[.]173. In response the external server provided numerous files, including ‘u.exe’, and ‘upsup4.exe’ for download, both of which share file names with previously identified MyKings payloads.

MyKings deploys a diverse array of payloads to expand the botnet and secure a firm position within a compromised system. This multi-faceted approach may render conventional security measures less effective due to the intricacies of and variety of payloads involved in compromises. Darktrace, however, does not rely on static or outdated lists of IoCs in order to detect malicious activity. Instead, DETECT’s Self-Learning AI allows it to identify emerging compromise activity by recognizing the subtle deviations in an affected device’s behavior that could indicate it has fallen into the hands of malicious actors.

Figure 2: External site summary of the endpoint 103.145.106[.]242 showing the rarity of connectivity to the external host.

Achieving Objectives – Crypto-Mining

Several weeks after the initial payloads were delivered and beaconing commenced, Darktrace finally detected the initiation of crypto-mining operations. On May 27, the originally compromised server connected to the rare domain other.xmrpool[.]ru over port 1081. As seen in the domain name, this endpoint appears to be affiliated with pool mining activity and the domain has various OSINT affiliations with the cryptocurrency Monero coin. During this connection, the host was observed passing Monero credentials, activity which parallels similar mining operations observed on other customer networks that had been compromised by the MyKings botnet.

Although mining activity may not pose an immediate or urgent concern for security unauthorized cryptomining on devices can result in detrimental consequences, such as compromised hardware integrity, elevated energy costs, and reduced productivity, and even potential involvement in money laundering.

Figure 3: Event breach log showing details of the connection to the other.xmrpool[.]ru endpoint associated with cryptocurrency mining activity.

Conclusion

Detecting future iterations of the MyKings botnet will likely demand a shift away from an overreliance on traditional rules and signatures and lists of “known bads”, instead requiring organizations to employ AI-driven technology that can identify suspicious activity that represents a deviation from previously established patterns of life.

Despite the diverse range of payloads, malicious endpoints, and intricate activities that constitute a typical MyKing botnet compromise, Darktrace was able successfully detect multiple critical phases within the MyKings kill chain. Given the evolving nature of the MyKings botnet, it is highly probable the botnet will continue to expand and adapt, leveraging new tactics and technologies. By adopting Darktrace’s product of suites, including Darktrace DETECT, organizations are well-positioned to identify these evolving threats as soon as they emerge and, when coupled with the autonomous response technology of Darktrace RESPOND, threats like the MyKings botnet can be stopped in their tracks before they can achieve their ultimate goals.

Credit to: Oluwatosin Aturaka, Analyst Team Lead, Cambridge, Adam Potter, Cyber Analyst

Appendix

IoC Table

IoC - Type - Description + Confidence

162.216.150[.]108- IP - C2 Infrastructure

103.145.106[.]242 - IP - C2 Infrastructure

137.175.56[.]104 - IP - C2 Infrastructure

138.197.152[.]201 - IP - C2 Infrastructure

139.59.74[.]135 - IP - C2 Infrastructure

pc.pc0416[.]xyz - Domain - C2 Infrastructure (DGA)

other.xmrpool[.]ru - Domain - Cryptomining Endpoint

xmrpool[.]ru - Domain - Cryptomining Endpoint

103.145.106[.]55 - IP - Cryptomining Endpoint

ntuser[.]rar - Zipped File - Payload

/xmr1025[.]rar - Zipped File - Payload

/20201117[.]rar - Zipped File - Payload

wmi[.]txt - File - Payload

u[.]exe - Executable File - Payload

back[.]txt - File - Payload

upsupx2[.]exe - Executable File - Payload

cmd[.]txt - File - Payload

power[.]txt - File - Payload

ups[.]html - File - Payload

xmr1025.rar - Zipped File - Payload

171.91.76[.]31- IP - Possible Initial Compromise Endpoint

www[.]back0314[.]ru - Domain - Probable C2 Infrastructure

107.148.239[.]111 - IP - Probable C2 Infrastructure

194.67.71[.]99 - IP- Probable C2 Infrastructure

Darktrace DETECT Model Breaches

  • Device / Initial Breach Chain Compromise
  • Anomalous File / Masqueraded File Transfer (x37)
  • Compromise / Large DNS Volume for Suspicious Domain
  • Compromise / Fast Beaconing to DGA
  • Device / Large Number of Model Breaches
  • Anomalous File / Multiple EXE from Rare External Locations (x30)
  • Compromise / Beacon for 4 Days (x2)
  • Anomalous Server Activity / New User Agent from Internet Facing System
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous Server Activity / New Internet Facing System
  • Anomalous File / EXE from Rare External Location (x37)
  • Device / Large Number of Connections to New Endpoints
  • Anomalous Server Activity / Server Activity on New Non-Standard Port (x3)
  • Device / Threat Indicator (x3)
  • Unusual Activity / Unusual External Activity
  • Compromise / Crypto Currency Mining Activity (x37)
  • Compliance / Internet Facing SQL Server
  • Device / Anomalous Scripts Download Followed By Additional Packages
  • Device / New User Agent

MITRE ATT&CK Mapping

ATT&CK Technique - Technique ID

Reconnaissance – T1595.002 Vulnerability Scanning

Resource Development – T1608 Stage Capabilities

Resource Development – T1588.001 Malware

Initial Access – T1190 Exploit Public-Facing Application

Command and Control – T15568.002 Domain Generated Algorithms

Command and Control – T1571 Non-Standard Port

Execution – T1047 Windows Management Instrumentation

Execution – T1059.001 Command and Scripting Interpreter

Persistence – T1542.003 Pre-OS Boot

Impact – T1496 Resource Hijacking

References

[1] https://www.binarydefense.com/resources/threat-watch/mykings-botnet-is-growing-and-remains-under-the-radar/

[2] https://therecord.media/a-malware-botnet-has-made-more-than-24-7-million-since-2019

[3] https://www.darktrace.com/blog/outlaw-returns-uncovering-returning-features-and-new-tactics

[4] https://www.sophos.com/en-us/medialibrary/pdfs/technical-papers/sophoslabs-uncut-mykings-report.pdf

[5] https://www.antiy.com/response/20190822.html

[6] https://ethicaldebuggers.com/mykings-botnet/

[7] https://ethicaldebuggers.com/mykings-botnet/

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
Oluwatosin Aturaka
Analyst Team Lead, Cambridge

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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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