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May 23, 2025

Defending the Frontlines: Proactive Cybersecurity in Local Government

To quickly identify and respond to threats before damage occurs, this local government relies on Darktrace to improve network visibility, stop insider threats, protect its email systems, and accelerate incident investigations.
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.
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23
May 2025

Serving a population of over 165,000 citizens, this county government delivers essential services that enhance the quality of life for all of its residents in Florida, United States. From public safety and works to law enforcement, economic development, health, and community services, the county’s cybersecurity strategy plays a foundational role in protecting its citizens.

From flying blind to seeing the bigger picture

Safeguarding data from multiple systems, service providers, and citizens is a key aspect of the County’s Systems Management remit. Protecting sensitive information while enabling smooth engagement with multiple external partners poses a unique challenge; the types of data and potential threats are continuously evolving, but resources – both human and financial – remain consistently tight.

When the Chief Information Officer took on his role in 2024, building out a responsive defense-in-depth strategy was central to achieving these goals. However, with limited resources and complex needs, his small security team was struggling with high alert volumes, inefficient tools, and time-consuming investigations that frequently led nowhere.

Meanwhile, issues like insider threats, Denial of Service (DoS), and phishing attacks were growing; the inefficiencies were creating serious security vulnerabilities. As the CIO put it, he was flying blind. With so much data coming in, security analysts were in danger of missing the bigger picture.

“We would just see a single portion of data that could send us down a rabbit hole, thinking something’s going on – only to find out after spending days, weeks, or even months that it was nothing. If you’re only seeing one piece of the issue, it’s really difficult to identify whether something is a legitimate threat or a false positive.”

Local government’s unique cybersecurity challenges

According to the CIO, even with a bigger team, aligning and comparing all the data into a comprehensive, bigger picture would be a major challenge. “The thing about local government specifically is that it’s a complex security environment. We bring together a lot of different individuals and organizations, from construction workers to people who bring projects into our community to better the County. What we work with varies from day to day.”

The challenge wasn’t just about identifying threats, but also about doing so quickly enough to respond before damage was done. The CIO said this was particularly concerning when dealing with sophisticated threats: “We’re dealing with nation-state attackers nowadays, as opposed to ‘script kiddies.’ There’s no time to lose. We’ve got to have cybersecurity that can respond as quickly as they can attack.”

To achieve this, among the most critical challenges the CIO and his team needed to address were:

  • Contextual awareness and visibility across the network: The County team lacked the granular visibility needed to identify potentially harmful behaviors. The IT team needed a tool that uncovered hidden activities and provided actionable insights, with minimal manual intervention.
  • Augmenting human expertise and improving response times: Hiring additional analysts to monitor the environment is prohibitively expensive for many local governments. The IT team needed a cybersecurity solution that could augment existing skills while automating day-to-day tasks. More effective resource allocation would drive improved response times.
  • Preventing email-based threats: Phishing and malicious email links present a persistent threat. The County team needed a way to flag, identify, and hold suspicious messages automatically and efficiently. Given the team’s public service remit, contextual awareness is crucial to ensuring that no legitimate communications are accidentally blocked. Accuracy is extremely important.
  • Securing access and managing insider threats: Having already managed insider threats posed by former staff members, the IT team wanted to adopt a more proactive, deterrent-based approach towards employee IT resource use, preventing incidents before they could occur.

Proactive cybersecurity

Recognizing these challenges, the CIO and County sought AI-driven solutions capable of acting autonomously to support a lean IT team and give the big picture view needed, without getting lost in false positive alerts.

Ease of deployment was another key requirement: the CIO wanted to quickly establish a security baseline for County that would not require extensive pre-planning or disrupt existing systems. Having worked with Darktrace in previous roles, he knew the solution had the capacity to make the critical connections he was looking for, while delivering fast response times and reducing the burden on security teams.

When every second counts, we want to be as close to the same resources as our attackers are utilizing. We have got to have something that can respond as quickly as they can attack. For the County, that’s Darktrace.” – CIO, County Systems Management Department.

Closing network visibility gaps with Darktrace / NETWORK

The County chose Darktrace / NETWORK for unparalleled visibility into the County’s network. With the solution in place, the CIO and his team were able to identify and address previously hidden activities, uncovering insider threats in unexpected places. For example, one team member had installed an unauthorized anonymizer plug-in on their browser, posing a potentially serious security risk via traffic being sent out to the internet. “Darktrace immediately alerted on it,” said CIO. “We were able to deal with the threat proactively and quickly.”

Darktrace / NETWORK continuously monitored and updated its understanding of the County environment, intelligently establishing the different behaviors and network activity. The end result was a level of context awareness that enabled the team to focus on the alerts that mattered most, saving time and effort.

“Darktrace brings all the data we need together, into one picture. We’re able to see what’s going on at a glance, as opposed to spending time trying to identify real threats from false positives,” said the CIO. The ability to automate actions freed the team up to focus on more complex tasks, with 66% of network response actions being applied autonomously, taking the right action at the right time to stop the earliest signs of threatening activity. This reduced pressure on the County’s team members, while buying valuable containment time to perform deeper investigations.

The agentless deployment advantage

For the CIO, one of the major benefits of Darktrace / NETWORK is that it’s agentless. “Agents alert attackers to the presence of security in your environment, it helps them to understand that there’s something else they need to bring down your defenses,” he said. Using Darktrace to mirror network traffic, the County can maintain full visibility across all network entities without alerting attackers and respond to threatening activity at machine speed. “It allows me to sleep better at night, knowing that this tool can effectively unplug the network cable from that device and bring it offline,” said CIO.

Streamlining investigations with Darktrace Cyber AI Analyst

For lean security teams, contextual awareness is crucial in reducing the burden of alert fatigue. Using Cyber AI Analyst, the County team is able to take the pressure off, automatically investigating every relevant event, and reducing thousands of individual alerts to only a small number of incidents that require manual review.

For the County team, the benefits are clear: 520 investigation hours saved in one month, with an average of just 11 minutes investigation time per incident. For the CIO, Darktrace goes beyond reducing workloads, it actually drives security: “It identifies threats almost instantly, bringing together logs and behaviors into a single, clear view.”

The efficiency gain has been so significant that the CIO believes Darktrace augments capabilities beyond the size of a team of analysts. “You could have three analysts working around the clock, but it’s hard to bring all those logs and behaviors together in one place and communicate everything in a coordinated way. Nothing does that as quickly as Darktrace can.”

Catching the threats from within: Defense in depth with Darktrace / IDENTITY

One of the key benefits of Darktrace for the County was its breadth of capability and responsiveness. “We’re looking at everything from multi-factor authentication, insider threats, distributed denial of service attacks,” said the CIO. “I’ve worked with other products in the past, but I’ve never found a tool as good as Darktrace.”

Further insider threats uncovered by Darktrace / IDENTITY included insecure access practices. Some users had logins and passwords on shared network resources or in plain-text files. Darktrace alerted the security team and the threats were mitigated before serious damage was done.

Darktrace / IDENTITY gives organizations advanced visibility of application user behavior from unusual authentication, password sprays, account takeover, resource theft, and admin abuse. Security teams can take targeted actions including the forced log-off of a user or temporary disabling of an account to give the team time to verify legitimacy.

First line of defense against the number one attack vector: Enhancing email security with Darktrace / EMAIL

Email-based threats, such as phishing, are among the most common attack vectors in modern cybersecurity, and a key vector for ransomware attacks. Post implementation performance was so strong that the organization now plans to retire other tools, cutting costs without compromising on security.

Darktrace / EMAIL was one of the first tools that I implemented when I started here,” said CIO. “I really recognize the value of it in our environment.” In addition to detecting and flagging potentially malicious email, the CIO said an unexpected benefit has been the reinforcement of more security-aware behaviors among end users. “People are checking their junk folders now, alerting us and checking to see if something is legitimate or not.”

The CIO said that, unlike traditional email security tools that basically perform only one function, Darktrace has multiple additional capabilities that deliver extra layers of protection compared to one-dimensional alternatives. For example, AI-employee feedback loops leverage insights gained from individual users to not only improve detection rates, but also provide end users with contextual security awareness training, to enhance greater understanding of the risks.

Straightforward integration, ease of use

The County wanted a powerful, responsive solution – without demanding pre-installation or integration needs, and with maximum ease of use. “The integration is relatively painless,” said the CIO. “That’s another real benefit, you can bring Darktrace into your environment and have it up and running faster than you could ever hire additional analysts to look at the same data.”

The team found that, compared to competing products, where there was extensive setup, overhead, and resources, “Darktrace is almost plug-and-play.” According to the CIO, the solution started ingesting information and providing notifications immediately: “You can turn on defense or response mechanisms at a granular level, for email or network – or both at the same time.”

The County sees Darktrace as an integral part of its cybersecurity strategy into the future. “Having worked with Darktrace in the past, it was an easy decision for me to agree to a multi-year partnership,” said the CIO “As we continue to build out our defense-in-depth strategy, the ability to use Darktrace to manage other data sources and identify new, additional behavior will be crucial to our proactive, risk-based approach.”

Darktrace has the capacity to meet the organization’s need for exceptional responsiveness, without burning out teams. “If you’re not overburdening the teams that you do have with significant workloads, they have a lot more agility to deal with things on the fly,” said the CIO.

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
The Darktrace Community

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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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