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November 3, 2024

AI and Cybersecurity: Predictions for 2025

Discover the role of AI in shaping cybersecurity predictions for 2025 and how organizations can prepare for emerging threats.
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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03
Nov 2024

Introduction: AI cybersecurity predictions for 2025

Each year, Darktrace's AI and cybersecurity experts reflect on the events of the past 12 months and predict the trends we expect to shape the cybersecurity landscape in the year ahead. In 2024, we predicted that the global elections, fast-moving AI innovations, and increasingly cloud-based IT environments would be key factors shaping the cyber threat landscape.

Looking ahead to 2025, we expect the total addressable market of cybercrime to expand as attackers add more tactics to their toolkits. Threat actors will continue to take advantage of the volatile geopolitical environment and cybersecurity challenges will increasingly move to new frontiers like space. When it comes to AI, we anticipate the innovation in AI agents in 2024 to pave the way for the rise of multi-agent systems in 2025, creating new challenges and opportunities for cybersecurity professionals and attackers alike.

Here are ten trends to watch for in 2025:

1. The overall Total Addressable Market (TAM) of cybercrime gets bigger

Cybercrime is a global business, and an increasingly lucrative one, scaling through the adoption of AI and cybercrime-as-a-service. Annual revenue from cybercrime is already estimated to be over $8 trillion, which we’ve found is almost 5x greater than the revenue of the Magnificent Seven stocks. There are a few key factors driving this growth.

The ongoing growth of devices and systems means that existing malware families will continue to be successful. As of October 2024, it’s estimated that more than 5.52 billion people (~67%) have access to the internet and sources estimate 18.8 billion connected devices will be online by the end of 2024. The increasing adoption of AI is poised to drive even more interconnected systems as well as new data centers and infrastructure globally.

At the same time, more sophisticated capabilities are available for low-level attackers – we’ve already seen the trickle-down economic benefits of living off the land, edge infrastructure exploitation, and identity-focused exploitation. The availability of Ransomware-as-a-Service (RaaS) and Malware-as-a-Service (MaaS) make more advanced tactics the norm. The subscription income that these groups can generate enables more adversarial innovation, so attacks are getting faster and more effective with even bigger financial ramifications.

While there has also been an increasing trend in the last year of improved cross-border law enforcement, the efficacy of these efforts remains to be seen as cybercriminal gangs are also getting more resilient and professionalized. They are building better back-up systems and infrastructure as well as more multi-national networks and supply chains.

2. Security teams need to prepare for the rise of AI agents and multi-agent systems

Throughout 2024, we’ve seen major announcements about advancements in AI agents from the likes of OpenAI, Microsoft, Salesforce, and more. In 2025, we’ll see increasing innovation in and adoption of AI agents as well as the emergence of multi-agent systems (or “agent swarms”), where groups of autonomous agents work together to tackle complex tasks.

The rise of AI agents and multi-agent systems will introduce new challenges in cybersecurity, including new attack vectors and vulnerabilities. Security teams need to think about how to protect these systems to prevent data poisoning, prompt injection, or social engineering attacks.

One benefit of multi-agent systems is that agents can autonomously communicate, collaborate, and interact. However without clear and distinct boundaries and explicit permissions, this can also pose a major data privacy risk and avenue for manipulation. These issues cannot be addressed by traditional application testing alone. We must ensure these systems are secure by design, where robust protective mechanisms and data guardrails are built into the foundations.

3. Threat actors will be the earliest adopters of AI agents and multi-agent systems

We’ve already seen how quickly threat actors have been able to adopt generative AI for tasks like email phishing and reconnaissance. The next frontier for threat actors will be AI agents and multi-agent systems that are specialized in autonomous tasks like surveillance, initial access brokering, privilege escalation, vulnerability exploitation, data summarization for smart exfiltration, and more. Because they have no concern for safe, secure, accurate, and responsible use, adversaries will adopt these systems faster than cyber defenders.

We could also start to see use cases emerge for multi-agent systems in cyber defense – with potential for early use cases in incident response, application testing, and vulnerability discovery. On the whole, security teams will be slower to adopt these systems than adversaries because of the need to put in place proper security guardrails and build trust over time.

4. There is heightened supply chain risk for Large Language Models (LLMs)

Training LLMs requires a lot of data, and many experts have warned that world is running out of quality data for that training. As a result, there will be an increasing reliance on synthetic data, which can introduce new issues of accuracy and efficacy. Moreover, data supply chain risks will be an Achilles heel for organizations, with the potential interjection of vulnerabilities through the data and machine learning providers that they rely on. Poisoning one data set could have huge trickle-down impacts across many different systems. Data security will be paramount in 2025.

5. The race to identify software vulnerabilities intensifies

The time it takes for threat actors to exploit newly published CVEs is getting shorter, giving defenders an even smaller window to apply patches and remediations. A 2024 report from Cloudflare found that threat actors quickly weaponized proof of concept exploits in attacks as quickly as 22 minutes after the exploits were made public.

At the same time, 2024 also saw the first reports from researchers across academia and the tech industry using AI for vulnerability discovery in real-world code. With threat actors getting faster at exploiting vulnerabilities, defenders will need to use AI to identify vulnerabilities in their software stack and to help identify and prioritize remediations and patches.

6. Insider threat risks will force organizations to evolve zero trust strategies

In 2025, an increasingly volatile geopolitical situation and the intensity of the AI race will make insider threats an even bigger risk for businesses, forcing organizations to expand zero-trust strategies. The traditional zero-trust model provides protection from external threats to an organization’s network by requiring continuous verification of the devices and users attempting to access critical business systems, services, and information from multiple sources. However, as we have seen in the more recent Jack Teixeira case, malicious insiders can still do significant damage to an organization within their approved and authenticated boundary.

To circumvent the remaining security gaps in a zero-trust architecture and mitigate increasing risk of insider threats, organizations will need to integrate a behavioral understanding dimension to their zero-trust approaches. The zero-trust best practice of “never trust, always verify” needs to evolve to become “never trust, always verify, and continuously monitor.”

7. Identity remains an expensive problem for businesses

2024 saw some of the biggest and costliest attacks – all because the attacker had access to compromised credentials. Essentially, they had the key to the front door. Businesses still struggle with identity and access management (IAM), and it’s getting more complex now that we’re in the middle of a massive Software-as-a-Service (SaaS) migration driven by increasing rates of AI and cloud use across businesses.

This challenge is going to be exacerbated in 2025 by a few global and business factors. First, there is an increasing push for digital identities, such as the rollout of the EU Digital Identity Framework that is underway, which could introduce additional attack vectors. As they scale, businesses are turning more and more to centralized identity and access solutions with decentralized infrastructure and relying on SaaS and application-native security.

8. Increasing vulnerabilities at the edge

During the COVID-19 pandemic, many organizations had to stand-up remote access solutions quickly – in a matter of days or weeks – without the high level of due diligence that they require to be fully secured. In 2025, we expect to see continued fall-out as these quickly spun-up solutions start to present genuine vulnerability to businesses. We’ve already seen this start to play out in 2024 with the mass-exploitation of internet-edge devices like firewalls and VPN gateway products.

By July 2024, Darktrace’s threat research team observed that the most widely exploited edge infrastructure devices were those related to Ivanti Connect Secure, JetBrains TeamCity, FortiClient Enterprise Management Server, and Palo Alto Networks PAN-OS. Across the industry, we’ve already seen many zero days and vulnerabilities exploiting these internet-connected devices, which provide inroads into the network and store/cache credentials and passwords of other users that are highly valuable for threat actors.

9. Hacking Operational Technology (OT) gets easier

Hacking OT is notoriously complex – causing damage requires an intimate knowledge of the specific systems being targeted and historically was the reserve of nation states. But as OT has become more reliant and integrated with IT systems, attackers have stumbled on ways to cause disruption without having to rely on the sophisticated attack-craft normally associated with nation-state groups. That’s why some of the most disruptive attacks of the last year have come from hacktivist and financially-motivated criminal gangs – such as the hijacking of internet-exposed Programmable Logic Controllers (PLCs) by anti-Israel hacking groups and ransomware attacks resulting in the cancellation of hospital operations.  

In 2025, we expect to see an increase in cyber-physical disruption caused by threat groups motivated by political ideology or financial gain, bringing the OT threat landscape closer in complexity and scale to that of the IT landscape. The sectors most at risk are those with a strong reliance on IoT sensors, including healthcare, transportation, and manufacturing sectors.

10. Securing space infrastructure and systems becomes a critical imperative

The global space industry is growing at an incredibly fast pace, and 2025 is on track to be another record-breaking year for spaceflight with major missions and test flights planned by NASA, ESA, CNSA as well as the expected launch of the first commercial space station from Vast and programs from Blue Origin, Amazon and more. Research from Analysis Mason suggests that 38,000 additional satellites will be built and launched by 2033 and the global space industry revenue will reach $1.7 trillion by 2032. Space has also been identified as a focus area for the incoming US administration.

In 2025, we expect to see new levels of tension emerge as private and public infrastructure increasingly intersect in space, shining a light on the lack of agreed upon cyber norms and the increasing challenge of protecting complex and remote space systems against modern cyber threats.  Historically focused on securing earth-bound networks and environments, the space industry will face challenges as post-orbit threats rise, with satellites moving up the target list.

The EU’s NIS2 Directive now recognizes the space sector as an essential entity that is subject to its most strict cybersecurity requirements. Will other jurisdictions follow suit? We expect global debates about cyber vulnerabilities in space to come to the forefront as we become more reliant on space-based technology.

Conclusion: Preparing for the future

Whatever 2025 brings, Darktrace is committed to providing robust cybersecurity leadership and solutions to enterprises around the world. Our team of subject matter experts will continue to monitor emerging threat trends, advising both our customers and our product development teams.

And for day-to-day security, our multi-layered AI cybersecurity platform can protect against all types of threats, whether they are known, unknown, entirely novel, or powered by AI. It accomplishes this by learning what is normal for your unique organization, therefore identifying unusual and suspicious behavior at machine speed, regardless of existing rules and signatures. In this way, organizations with Darktrace can be ready for any developments in the cybersecurity threat landscape that the new year may bring.

Discover more about Darktrace's predictions on the AI and cybersecurity landscape for 2025 by watching the full recorded webinar here.

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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