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October 13, 2023

Protecting Brazilian Organizations from Malware

Discover how Darktrace DETECT thwarted a banking trojan targeting Brazilian organizations, preventing data theft and informing the customer.
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
Roberto Romeu
Senior SOC Analyst
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13
Oct 2023

Nationally Targeted Cyber Attacks

As the digital world becomes more and more interconnected, the threat of cyber-attacks transcends borders and presents a significant concern to security teams worldwide. Yet despite this, some malicious actors have shown a tendency to focus their attacks on specific countries. By employing highly tailored tactics, techniques, and procedures (TTPs) to target users and organizations from one nation, rather than launching more widespread campaigns, threat actors are able to maximize the efficiency and efficacy of their attacks.

What is Guildma and how does it work?

One example can be seen in the remote access trojan (RAT) and information stealer, Guildma. Guildma, also known by the demonic moniker, Astaroth, first appeared in the wild in 2017 and is a Latin America-based banking trojan known to primarily target organizations in Brazil, although has more recently been observed in North America and Europe too [1].

By concentrating their efforts on Brazil, Guildma is able to launch attacks with a high degree of specificity, focussing their language on Brazilian norms, referencing Brazilian institutions, and tailoring their social engineering accordingly. Moreover, considering that Brazilian customers likely represent a relatively small portion of security vendors’ clientele, there may be a limited pool of available indicators of compromise (IoCs). This limitation could significantly impact the efficacy of traditional security measures that rely on signature-based detection methods in identifying emerging threats.

Darktrace vs. Guildma

In June 2023, Darktrace observed a Guildma compromise on the network of a Brazilian customer in the manufacturing sector. The anomaly-based detection capabilities of Darktrace DETECT™ allowed it to identify suspicious activity surrounding the compromise, agnostic of any IoCs or specific signatures of a threat actor. Following the successful detection of the malware, the Darktrace Security Operations Center (SOC) carried out a thorough investigation into the compromise and brought it to the attention of the customer’s security team, allowing them to quickly react and prevent any further escalation.

This early detection by Darktrace effectively shut down Guildma operations on the network before any sensitive data could be gathered and stolen by malicious actors.

Attack Overview

In the case of the Guildma RAT detected by Darktrace, the affected system was a desktop device, ostensibly used by one employee. The desktop was first observed on the customer’s network in April 2023; however, it is possible that the initial compromise took place before Darktrace had visibility over the network. Guildma compromises typically start with phishing campaigns, indicating that the initial intrusion in this case likely occurred beyond the scope of Darktrace’s monitoring [2].

Early indicators

On June 23, 2023, Darktrace DETECT observed the first instance of unusual activity being performed by the affected desktop device, namely regular HTTP POST requests to a suspicious domain, indicative of command-and-control (C2) beaconing activity. The domain used an unusual Top-Level Domain (TLD), with a plausibly meaningful (in Portuguese) second-level domain and a seemingly random 11-character third-level domain, “dn00x1o0f0h.puxaofolesanfoneiro[.]quest”.

Throughout the course of this attack, Darktrace observed additional connections like this, representing something of a signature of the attack. The suspicious domains were typically registered within six months of observation, featured an uncommon TLD, and included a seemingly randomized third-level domain of 6-11 characters, followed by a plausibly legitimate second-level domain with a minimum of 15 characters. The connections to these unusual endpoints all followed a similar two-hour beaconing period, suggesting that Guildma may rotate its C2 infrastructure, using the Multi-Stage Channels TTP (MITRE ID T1104) to evade restrictions by firewalls or other signature-based security tools that rely on static lists of IoCs and “known bads”.

Figure 1: Model Breach Event Log for the “Compromise / Agent Beacon (Long Period)”. The connections at two-hour intervals, including at unreasonably late hours, is consistent with beaconing for C2.

Living-off-the-land with BITS abuse

A week later, on June 30, 2023, the affected device was observed making an unusual Microsoft BITS connection. BitsAdmin is a deprecated administrative tool available on most Windows devices and can be leveraged by attackers to transfer malicious obfuscated payloads into and around an organization’s network. The domain observed during this connection, "cwiufv.pratkabelhaemelentmarta[.]shop”, follows the previously outlined domain naming convention. Multiple open-source intelligence (OSINT) sources indicated that the endpoint had links to malware and, when visited, redirected users to the Brazilian versions of WhatsApp and Zoom. This is likely a tactic employed by threat actors to ensure users are unaware of suspicious domains, and subsequent malware downloads, by redirected them to a trusted source.

Figure 2: A screenshot of the Model Breach log summary of the “Unusual BITS Activity” model breach. The breach log contains key details such as the ASN, hostname, and user agent used in the breaching connection.

Obfuscated Tooling Downloads

Within one minute of the suspicious BITS activity, Darktrace detected the device downloading a suspicious file from the aforementioned endpoint, (cwiufv.pratkabelhaemelentmarta[.]shop). The file in question appeared to be a ZIP file with the 17-digit numeric name query, namely “?37627343830628786”, with the filename “zodzXLWwaV.zip”.

However, Darktrace DETECT recognized that the file extension did not match its true file type and identified that it was, in fact, an executable (.exe) file masquerading as a ZIP file. By masquerading files downloads, threat actors are able to make their malicious files seem legitimate and benign to security teams and traditional security tools, thereby evading detection. In this case, the suspicious file in question was indeed identified as malicious by multiple OSINT sources.

Following the initial download of this masqueraded file, Darktrace also detected subsequent downloads of additional executable files from the same endpoint.  It is possible that these downloads represented Guildma actors attempting to download additional tooling, including the information-stealer widely known as Astaroth, in order to begin its data collection and exfiltration operations.

Figure 3: A screenshot of a graph produced by the Threat Visualizer of the affected device's external connections. The visual aid marks breaches with red and orange dots, creating a more intuitive explanation of observed behavior.

Darktrace SOC

The successful detection of the masqueraded file transfer triggered an Enhanced Monitoring model breach, a high-fidelity model designed to detect activity that is more likely indicative of an ongoing compromise.  

This breach was immediately escalated to the Darktrace SOC for analysis by Darktrace’s team of expert analysts who were able to complete a thorough investigation and notify the customer’s security team of the compromise in just over half an hour. The investigation carried out by Darktrace’s analysts confirmed that the activity was, indeed, malicious, and provided the customer’s security team with details around the extent of the compromise, the specific IoCs, and risks this compromise posed to their digital environment. This information empowered the customer’s security team to promptly address the issue, having a significant portion of the investigative burden reduced and resolved by the round-the-clock Darktrace analyst team.

In addition to this, Cyber AI Analyst™ launched an investigation into the ongoing compromise and was able to connect the anomalous HTTP connections to the subsequent suspicious file downloads, viewing them as one incident rather than two isolated events. AI Analyst completed its investigation in just three minutes, upon which it provided a detailed summary of events of the activity, further aiding the customer’s remediation process.

Figure 4: CyberAI Analyst summary of the suspicious activity. A prose summary of the breach activity and the meaning of the technical details is included to maintain an easily digestible stream of information.

Conclusion

While the combination of TTPs observed in this Guildma RAT compromise is not uncommon globally, the specificity to targeting organizations in Brazil allows it to be incredibly effective. By focussing on just one country, malicious actors are able to launch highly specialized attacks, adapting the language used and tailoring the social engineering effectively to achieve maximum success. Moreover, as Brazil likely represents a smaller segment of security vendors’ customers, therefore leading to a limited pool of IoCs, attackers are often able to evade traditional signature-based detections.

Darktrace DETECT’s anomaly-based approach to threat detection allows for effective detection, mitigation, and response to emerging threats, regardless of the specifics of the attack and without relying on threat intelligence or previous IoCs. Ultimately in this case, Darktrace was able to identify the suspicious activity surrounding the Guildma compromise and swiftly bring it to the attention of the customer’s security team, before any data gathering, or exfiltration activity took place.

Darktrace’s threat detection capabilities coupled with its expert analyst team and round-the-clock SOC response is a highly effective addition to an organization’s defense-in-depth, whether in Brazil or anywhere else around the world.

Credit to Roberto Romeu, Senior SOC Analyst, Taylor Breland, Analyst Team Lead, San Francisco

References

https://malpedia.caad.fkie.fraunhofer.de/details/win.astaroth

https://www.welivesecurity.com/2020/03/05/guildma-devil-drives-electric/  

Appendices

Darktrace DETECT Model Breaches

  • Compromise / Agent Beacon (Long Period)
  • Device / Unusual BITS Activity
  • Anomalous File / Anomalous Octet Stream (No User Agent)
  • Anomalous File / Masqueraded File Transfer (Enhanced Monitoring Model)
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Multiple EXE from Rare External Locations

List of IoCs

IoC Type - Description + Confidence

5q710e1srxk.broilhasoruikaliventiladorrta[.]shop - Domain - Likely C2 server

m2pkdlse8md.roilhasohlcortinartai[.]hair - Domain - Likely C2 server

cwiufv.pratkabelhaemelentmarta[.]shop - Domain - C2 server

482w5pct234.jaroilcasacorkalilc[.]ru[.]com - Domain - C2 server

dn00x1o0f0h.puxaofolesanfoneiro[.]quest - Domain - Likely C2 server

10v7mybga55.futurefrontier[.]cyou - Domain - Likely C2 server

f788gbgdclp.growthgenerator[.]cyou - Domain - Likely C2 server

6nieek.satqabelhaeiloumelsmarta[.]shop - Domain - Likely C2 server

zodzXLWwaV.zip (SHA1 Hash: 2a4062e10a5de813f5688221dbeb3f3ff33eb417 ) - File hash - Malware

IZJQCAOXQb.zip (SHA1 Hash: eaec1754a69c50eac99e774b07ef156a1ca6de06 ) - File hash - Likely malware

MITRE ATT&CK Mapping

ATT&CK Technique - Technique ID

Multi-Stage Channels - T1104

BITS Jobs - T1197

Application Layer Protocol: Web Protocols - T1071.001

Acquire Infrastructure: Web Services - T1583.006

Obtain Capabilities: Malware - T1588.001

Masquerading - T1036

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
Roberto Romeu
Senior SOC Analyst

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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 20, 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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