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November 20, 2025

Xillen Stealer Updates to Version 5 to Evade AI Detection

Xillen Stealer v4/v5 introduces advanced features to evade AI detection, steal credentials, cryptocurrency, and sensitive data across browsers, password managers, and cloud environments. With polymorphic engines, container persistence, and behavioral mimicking, this Python-based malware highlights evolving threats and future AI integration in cybercrime campaigns.
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
Tara Gould
Malware Research Lead
xillen stealer updates to version 5 to evade ai detectionDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
20
Nov 2025

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

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
Tara Gould
Malware Research Lead

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March 20, 2026

Darktrace Recognized as the Only Visionary in the 2026 Gartner® Magic Quadrant™ for CPS Protection Platforms

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The Gartner® Magic Quadrant™ for CPS Protection Platforms provides an independent view of the vendors shaping this rapidly evolving market, evaluating how providers are helping organizations address the cybersecurity risks associated with increasingly connected operational technology and cyber-physical environments. Security and risk leaders use this research to better understand vendor positioning and to inform decisions as they modernize their Cyber Physical System (CPS) security strategies. We encourage organizations evaluating CPS security platforms to review the full report to gain a comprehensive view of the market.

Darktrace’s position as the only Visionary for the second consecutive year reflects the strength of our innovation, product execution, and long-term strategy for CPS security.  

Darktrace / OT is built to address the realities of modern industrial defense, securing converged IT, OT, and IoT environments, applying Self-Learning AI to detect known, unknown, and novel threats, accelerating investigations and prioritizing risk based on operational impact. This unique approach supports the flexible deployment models required by complex critical infrastructure organizations.

gartner 2026 CPS magic quadrant

Why Darktrace / OT stands apart in CPS security

As industrial environments continue to converge with enterprise infrastructure, security leaders are being asked to reduce cyber risk in systems where uptime, safety, and regulatory requirements limit traditional security approaches. Teams need to understand how risk develops across the environment, investigate threats with greater speed and clarity, and prioritize mitigation based on operational impact.

Darktrace / OT is built for that challenge. It combines cross-domain visibility, detection and investigation with Self-Learning AI, bespoke risk management beyond CVEs, and OT-relevant workflows that support both security outcomes and operational resilience.

Unified visibility across converged CPS environments

As critical infrastructure expands beyond traditional OT networks to include engineering workstations, HMIs, remote access pathways, enterprise systems, and cloud-linked infrastructure, teams need to understand how assets relate, where dependencies exist, and how exposure develops across domains.  

Darktrace / OT provides unified visibility across OT, IT, IoT, and IoMT to help organizations understand cyber risk across connected industrial environments. By bringing telemetry together through capabilities such as Operational Overview, OT workflows, and deep protocol inspection, Darktrace helps engineers and security teams work from shared context and a more operationally relevant understanding of the environment they are defending.

Enhanced threat detection, investigation, and response powered by Self-Learning AI

While signatures can still provide value for known threats, they fail against insider misuse, zero-day exploits, and custom-built malware designed for targeted operations. Darktrace / OT uses Self-Learning AI to detect subtle deviations from normal behavior across industrial environments where threats often appear through abnormal communications, misuse of legitimate access, or suspicious device behavior rather than known malware. To improve incident investigation, Darktrace’s Cyber AI Analyst automatically correlates activity and produces contextual summaries to reduce manual triage effort and help teams move faster from alert to understanding.  

Darktrace / OT further strengthens investigation and response through Expanded telemetry through NEXT for OT, extending visibility into operational endpoints such as engineering workstations and HMIs to support deeper root-cause analysis. Leveraging Self-Learning AI, Darktrace also enables autonomous response that can surgically contain anomalous activity while allowing industrial processes to continue operating normally. Organizations can customize response actions by device, device type, or network segment, with options ranging from fully autonomous enforcement to human confirmation workflows, helping security teams reduce operational disruption while maintaining control over response decisions.

Contextual risk prioritization based on operational relevance

Teams need the right tools to shift from reactive defense to proactively thinking about their security posture. However, most OT teams are stuck using IT-centric tools that don’t speak the language of industrial systems, are consistently overwhelmed with static CVE lists, and these tools offer no understanding of OT-specific protocols.  

Darktrace / OT helps organizations move beyond static vulnerability lists by prioritizing cyber risk based on operational context. By incorporating asset criticality, network relationships, exploitability intelligence, behavioral telemetry, and attack path analysis, the platform helps teams understand which exposures could realistically impact operations. By correlating CVE severity, KEV data, MITRE techniques, and business impact, Darktrace enables more focused remediation decisions that support operational resilience, governance, and compliance initiatives such as IEC-62443.

Built for real-world deployment and enterprise alignment

Darktrace / OT is designed for the realities of industrial environments where flexible deployment across on-premises, hybrid, distributed, air-gapped and operationally sensitive networks is essential. The platform integrates with enterprise security ecosystems including SIEM, SOAR, CMDB, firewall, and governance tools to support broader security workflows. This enables OT security to align with enterprise programs while respecting operational constraints and improving collaboration between security and engineering teams.

Customer validation and platform recognition  

In the last 12 months, Darktrace / OT has received a 4.8/5 rating on Gartner Peer Insights*. which we believe reflects strong customer confidence in the platform across critical infrastructure and industrial environments.  

With this recognition, Darktrace is now positioned across multiple Gartner Magic Quadrants, including Leader positions in Network Detection and Response (NDR) and Email Security Platforms, reflecting the breadth of Darktrace’s ActiveAI Security Platform.

Darktrace / OT customer review

Continuing to advance the future of CPS security

We believe Darktrace’s recognition as the only Visionary for the second consecutive year reflects a clear direction: CPS security platforms need to help customers connect visibility to investigation, investigation to prioritization, and prioritization to real operational outcomes.

That remains our focus for Darktrace / OT.

As industrial environments continue to grow more connected, more complex, and more business-critical, we will continue to invest in the capabilities that help customers reduce uncertainty, strengthen resilience, and secure the systems that keep their operations running.

  • Download the full Gartner Magic Quadrant for CPS Protection Platforms

Gartner, Magic Quadrant for CPS Protection Platforms, Katell Thielemann, Ruggero Contu, Wam Voster, Sumit Rajput, 3 March 2026

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant and Peer Insights are a registered trademark, of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.

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Pallavi Singh
Product Marketing Manager, OT Security & Compliance

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March 18, 2026

When Reality Diverges from the Playbook: Darktrace Identifies Encryption in a World Leaks Ransomware Attack

EaaS, World Leaks, a rebrand of Hunters International, are known for their extortion-only attack model, abandoning the tactic of file encryption. However, contrary to these claims, Darktrace detected a World Leaks compromise where a ransomware payload was deployed, and customer data was encrypted.Default blog imageDefault blog image

As-a-Service Cybercrime Models

As-a-Service cybercrime models reduce the barrier to entry for cyber criminals as they no longer need expertise in every domain. Threat actors can increasingly outsource or supplement missing skills through the broader cybercrime-as-a-service ecosystem, and thus these models continue to grow in popularity within the cybercriminal underground. This has led to multiple templates in this sphere, such as Phishing-as-a-Service, Botnet-as-a-Service, DDoS-as-a-Service, and notably Ransomware-as-a-Service (RaaS) [1].

What is Extortion-as-a-Service?

Extortion-as-a-Service (EaaS) businesses function as a formalized way for cyber threat actors to offer extortion services to others for a fee or profit share and represents an evolution of extortion operations from the double-extortion ransomware model. Advancing from the RaaS model, extortion has become a distinct profit stream, separate from the encryption payload. This separation of functions, data theft, negotiation, and publicity, sets the stage for EaaS [1].

The EaaS model reflects a broader trend in cybercriminal activity, in which threat actors increasingly prioritize data theft and public exposure over traditional ransomware encryption. This shift reduces operational complexity while increasing pressure on victims through reputational damage. This approach has become increasingly popular among threat actors as, unlike encryption-based attacks, these operations are more difficult to detect and remediate [2]. It reflects a trend of ‘hack-and-leak’ operations that prioritize stealth, speed, and reputational damage over traditional encryption-based ransomware attacks [3].

World Leaks Overview

World Leaks emerged in early 2024 as a direct rebrand of the Hunters International ransomware group, which was notorious for encrypting victims’ data and demanding payment for decryption keys. In mid-2025, Hunters International shifted to an extortion-only model due to law enforcement scrutiny and reduced profitability, rebranding itself as World Leaks.

World Leaks functions as an affiliate-based EaaS operation which provides proprietary Storage Software exfiltration tooling to affiliates while maintaining a four-platform infrastructure consisting of a main data leak site hosted on the Dark Web where victim data is published, a victim negotiation portal with live chat, an affiliate management panel, and an insider journalist platform granting media outlets 24-hour advance access to stolen data before public release [4]. Since its emergence, World Leaks has published data stolen from dozens of organizations globally on its data leak site, serving both as a pressure tactic and a means for building reputation among cyber criminals.

World Leaks (known associations include Hive Ransomware, Secp0 Ransomware, and UNC6148) have been known to target the industrial (manufacturing) sector, along with healthcare organizations, technology firms and more generally, industries with valuable intellectual property [4]. Victims targeted have spanned multiple countries, with most located in the US, as well as Canada and several countries across Europe [5].

World Leaks’ Tactics, Techniques, and Procedures (TTPs) [3][4]

World Leaks’ typical attack pattern involves the exploitation of credentials with inadequate access controls, e.g. lacking multi-factor authentication (MFA), moving through reconnaissance, lateral movement and data exfiltration, notably without an encryption element.

Initial Access:

Initial access is typically gained through the exploitation of compromised virtual private network (VPN) credentials lacking MFA through valid accounts, as well as phishing campaigns. The targeting of internet-facing VPN infrastructure, RDP, and public-facing applications also represent common attack vectors in World Leaks incidents.

Lateral Movement:

SMB, RDP, and SSH are used for lateral movement via remote services. Notably, the group is also known to use PsExec and Rclone as part of their lateral movement activities.

Persistence:

Registry key modifications, scheduled tasks creation, account manipulation.

Exfiltration:

Data exfiltration is carried out through custom storage software tooling via TOR connections. Cloud storage services used for exfiltration particularly include MEGA. World Leaks also carry out direct data transfer through established command-and-control (C2) infrastructure.

Unlike Hunters International, which combined encryption with extortion, World Leaks claims to have abandoned the use of encryption. Some reports note that operations since January 2025 represent a pivot toward eliminating encryption entirely, instead relying on custom exfiltration tooling with SOCKSv5 proxy and TOR-based communications [4]. However, in early 2026, Darktrace detected an incident that directly contradicted this claim: World Leaks carried out an attack that involved both the exfiltration and encryption of customer data.

Darktrace’s Coverage of World Leaks Ransomware

Organizations today face a growing challenge: keeping pace with increasingly fast-moving threats. This incident highlights a common problem, when time-limited mitigations expire or human security teams cannot respond quickly enough, attackers are often able to regain the upper hand. A recent Darktrace detection of World Leaks ransomware provides a clear example of this challenge in practice.

In January 2026, Darktrace identified the presence of ransomware and data encryption linked to World Leaks within the network of an organization within the healthcare sector. Although Darktrace’s Autonomous Response capability was active in the customer’s environment and initially blocking suspicious connectivity, buying time for the customer to remediate, the attack continued once these mitigative actions expired. Darktrace continued to apply Autonomous Response actions as the attack progressed, working to inhibit the attackers at each stage of the intrusion.

Investigations carried out by Darktrace revealed that threat actors likely gained initial access via a Fortigate appliance in mid-October, indicating a three-month dwell time, before employing living-off-the-land (LOTL) techniques for lateral movement. C2 communications were established using Cloudflare Tunnel (formerly Argo Tunnel). As part of the Actions on Objectives attack phase, a significant volume of data was exfiltrated to the MEGA cloud storage platform, followed by the encryption of customer data.

Initial access/ Lateral movement

Darktrace analysts identified the likely patient-zero device within the network as a Fortigate appliance. In October 2025, this device was seen conducting brute-force activity using the compromised ‘administrator’ credential to gain a foothold deeper within the customer’s environment. Masquerading as a privileged user, the threat actor then went on to launch activity on remote devices via PsExec, a common administrative tool that allows users to execute processes on remote systems without manually installing client software, providing significant power to attackers when abused. Around the time, Darktrace detected an unknown device on the network attempting to authenticate via NTLM. As this device had not previously been seen on the network, it likely belonged to the attacker.

Reconnaissance

As part of the reconnaissance phase of the attack, port and network scanning was carried out in an attempt to identify open UDP and TCP ports within the network.

Lateral movement & C2

Around one month after entering the customer’s network, the World Leaks threat actors began tunnelling activity using Cloudflare Tunnel. Darktrace detected connections to several hostnames including: region2.v2.argotunnel[.]com; h2.cftunnel[.]com; region1.v2.argotunnel[.]com. This tunnelling activity continued until January of 2026, when encryption occurred. Cloudflare tunnels are known to be abused by attackers as they enable the use of temporary infrastructure to scale operations, allowing rapid deployment and teardown. Furthermore, leveraging of Cloudflare’s infrastructure to create these rate-limited tunnels (used to relay traffic from an attacker-controlled server to a local machine) makes such malicious activity harder to detect by both defenders and traditional security measures, particularly those that rely on static blocklists [6].

Further lateral movement was carried out using common remote management tools such as Windows Remote Management (WinRM) RDP, allowing the World Leaks threat actors to access local devices within the victim organization’s network.

As this attack progressed, Darktrace detected multiple files being written over SMB. These files included Windows\Temp\chromeremotedesktophost.msi, which was written from the patient-zero device to another internal device as part of lateral movement efforts. Following this transfer, and prior to subsequent data exfiltration activity, a network server was observed connecting to the hostname remotedesktop-pa[.]googleapis[.]com, an API endpoint required for Chrome Remote Desktop, indicating that Chrome RDP was used by the threat actor in this stage of the attack.

Other files written over SMB included the script programdata\syc\OpenSSHUtils.psm1 (which can be used legitimately to configure OpenSSH) and the executable programdata\syc\ssh‑sk‑helper.exe (a legitimate OpenSSH component used to support security keys). These files were written from the suspected patient‑zero device to an internal domain controller using the ‘administrator’ credential.

Thereafter, SSH connections to external IP address 51.15.109[.]222 were observed, providing another channel between the malicious actors and victim machines. Darktrace recognized that the use of SSH by the devices seen connecting to this IP address was highly anomalous, indicating that this suspicious activity formed part of the attack.

Writes of the script programdata\syc\OpenSSHUtils.psm1 were also observed into January, highlighting the continuation of the attack that had begun three months earlier.

On December 19 and 20, Darktrace detected a DNS server within the customer’s network making anomalous outgoing connections to an external IP address not previously seen in the environment: 193.161.193[.]99. This IP address has been reported by open-source-intelligence (OSINT) as being associated with C2 infrastructure, having been linked to several remote access trojans (RATs) and botnets in the past.

This activity a shift towards the infrastructure-as-a-service (IaaS) model, underscoring the growing trend around As-a-Service Cybercrime models and the increasing the industrialization of botnets. The presence of extensive digital botnets, often leased to other criminal organizations, means the group gaining initial access is not necessarily the same group conducting ransomware deployment or data theft; botnets now act as shared underlying infrastructure enabling multiple forms of cybercriminal activity [7].

Furthermore, connections to this IP address (193.161.193[.]99) were made over port 1194, which is associated with OpenVPN, suggesting that World Leaks may have leveraged it to obfuscate C2 communication with attacker-controlled infrastructure.

Darktrace’s detection of the IP address 193.161.193[.]99, noting that it was first seen within the customer’s network on December 19, 2025.
Figure 1: Darktrace’s detection of the IP address 193.161.193[.]99, noting that it was first seen within the customer’s network on December 19, 2025.

Data exfiltration

In November, Darktrace detected the threat actors carrying out one of their Attack on Objective tactics: data exfiltration. Multiple local devices within the compromised network began transferring data to Backblaze and MEGA domains, both of which provide cloud storage services; 80+GB of data was transferred to MEGA in late December 2025. Endpoints associated with this activity included: backblazeb2[.]com and gfs302n520[.]userstorage[.]mega[.]co[.]nz, as well as related user agents such as AS40401 BACKBLAZE) and MegaClient/10.3.0/64.

Notably, Darktrace researchers identified two known World Leaks TTPs in this attack: the use of MEGA, a known tool abused by the group, and Rclone, a command-line tool used to manage files on cloud storage, which was observed in the user agent of the MEGA data-transfer connections: rclone/v1.69.0 [4].

Cyber AI Analyst Incident highlighting data upload activity to backblaze[.]com endpoints.
Figure 2: Cyber AI Analyst Incident highlighting data upload activity to backblaze[.]com endpoints.\

Ransomware deployment & encryption

The encryption stage of this attack was confirmed by the presence of a ransom note found on the network in a file with a seemingly randomized nine-character string preceding README.txt, attributing the incident to World Leaks, along with an extension with the same nine characters appended to encrypted files. Darktrace also observed SMB writes of files named world.exe and task.bat, with the compromised ‘localadmin’ credential used during the SMB logins. It is likely that these files served as the vector for the ransomware payload.

 Packet Capture (PCAP) of the ransom note claiming that the attack was carried out by World Leaks.
Figure 3: Packet Capture (PCAP) of the ransom note claiming that the attack was carried out by World Leaks.

Conclusion

Though traditional ransomware relies on encryption, recent trends show that cyber threat actors no longer need to rely on noisy encryption tools and can eliminate much of the risk and technical complexity associated with encrypting systems. This is the model reportedly preferred by World Leaks after their rebrand from Hunters International.

In addition to reducing noise around these attacks, extortion‑only operations may be favored by threat actors over encryption‑focused ones for several reasons, including the fact that traditional security tools may struggle to detect data theft compared to encryption, that attackers leave less evidence behind when encryption is avoided, and that the long‑term impacts of stolen data on organizations can be greater than the loss of systems caused by encryption processes, which can be restored [8]. This is supported by analysis of data leak sites suggesting that almost 1,500 incidents in 2025 relied on data theft alone. Attackers can simply steal victim data and attempt to extort a ransom by threatening to publish it, without needing to deploy ransomware at all [9]. Furthermore, although World Leaks aims to function as an affiliate‑based EaaS operation, security teams should remain aware that their affiliates may have different criminal objectives.

Contrary to reports that World Leaks’ typical attack style has an extortion‑only objective, Darktrace detected an incident in which a World Leaks attack did end with the encryption of customer data. This highlights the need for adaptive defenses and reinforces the importance of network defenders staying proactive in the face of attacks, particularly as they may progress in ways that are unexpected compared to previous trends associated with a given threat actor.

Credit to Tiana Kelly (Senior Cyber Analyst and Analyst Manager) and Emily Megan Lim (Senior Cyber Analyst)

Edited by Ryan Traill (Content Manager)

Appendices

IoCs

  • world.exe – Executable File – Possible Ransomware Payload
  • task.bat – Script File – Possible Ransomware Payload
  • ‘^[A-Z][a-z]{3}[A-Z][a-z][A-Z]{3}[.]README[.]txt' – Ransom Note
  • [.]^[A-Z][a-z]{3}[A-Z][a-z][A-Z]{3} – Ransomware file extension

·       51.15.109[.]222 – IP Address - Possible C2 Infrastructure

·       193.161.193[.]99 – IP Address – Probable C2 Infrastructure

Darktrace Model Detections (Enhanced Monitoring models denoted with an asterisk)

·      Device / Attack and Recon Tools

·      Device / Suspicious SMB Scanning Activity

·      Device / Anomalous NTLM Brute Force

·      Compliance / Connection to Tunnelling Service

·      Device / Suspicious New User Agents

·      Device / New or Unusual Remote Command Execution

·      Compliance / SMB Drive Write

·      Anomalous Connection / Uncommon 1 GiB Outbound

·      Compromise / Ransomware / Ransom or Offensive Words Written to SMB

·      Device / Multiple Lateral Movement Model Alerts*

·      Device / SMB Lateral Movement

·      Unusual Activity / Sustained Anomalous SMB Activity

·      Device / Large Number of Model Alerts

·      Compromise / Ransomware / SMB Reads then Writes with Additional Extensions

·      Compromise / Ransomware / Suspicious SMB Activity*

·      Anomalous File / Internal / Additional Extension Appended to SMB File

·      Unusual Activity / SMB Access Failures

·      Unusual Activity / Enhanced Unusual External Data Transfer*

·      Device / Suspicious File Writes to Multiple Hidden SMB Shares

·      Anomalous Server Activity / Rare External from Server

·      Unusual Activity / Unusual Mega Data Transfer*

·      Device / Possible SMB/NTLM Brute Force

·      Anomalous Connection / Unusual Admin RDP Session

·      Anomalous Connection / Active Remote Desktop Tunnel

·      Anomalous Connection / Data Sent to Rare Domain

·      Anomalous Connection / New or Uncommon Service Control

·      Anomalous Connection / New or Uncommon Service Enumeration

·      Anomalous Connection / Rare WinRM Outgoing

·      Anomalous Connection / SMB Enumeration

·      Anomalous Connection / Unusual Admin RDP Session

·      Anomalous Connection / Unusual Incoming Long Remote Desktop Session

·      Anomalous Connection / Upload via Remote Desktop

·      Anomalous File / Internal / Executable Uploaded to DC

·      Anomalous File / Internal / Unusual SMB Script Write

·      Compliance / SSH to Rare External Destination

·      Device / Anomalous Github Download

·      Device / Anonymous NTLM Logins

·      Device / Network Scan

·      Device / New or Uncommon WMI Activity

·      Device / New User Agent To Internal Server

·      Device / Possible Brute-Force Activity

·      Device / RDP Scan

·      Device / SMB Session Brute Force (Admin)

·      Device / SMB Session Brute Force (Non-Admin)

·      Device / Suspicious Network Scan Activity

·      Unusual Activity / Successful Admin Brute-Force Activity

·      Unusual Activity / Unusual External Data to New Endpoint

·      Unusual Activity / Unusual External Data Transfer

·      Unusual Activity / Unusual File Storage Data Transfer

·      User / New Admin Credentials on Server

Cyber AI Analyst Incidents

·      Scanning of Multiple Devices

·      Large Volume of SMB Login Failures to Multiple Devices

·      Suspicious Chain of Administrative Connections

·      SMB Write of Suspicious File

·      Suspicious DCE-RPC Activity

·      Unusual External Data Transfer

·      Unusual External Data Transfer to Multiple Related Endpoints

·      Unusual External Data Transfer to Endpoints

MITRE ATT&CK Mapping

·      Initial Access – T1190 – Exploit Public-Facing Application

·      Defense Evasion, Initial Access, Persistence, Privilege Escalation – T1078 – Valid Accounts

·      Resource Development – T1588.001 – Obtain Capabilities: Malware

·      Reconnaissance – T1590.005 – Gather Victim Network Information: IP Addresses

·      Reconnaissance – T1592.004 – Gather Victim Host Information: Client Configurations

·      Reconnaissance – T1595.001 – Active Scanning: Scanning IP Blocks

·      Reconnaissance – T1595.002 – Active Scanning: Vulnerability Scanning

·      Reconnaissance – T1595.003 – Active Scanning: Wordlist Scanning

·      Discovery – T1018 – Remote System Discovery

·      Discovery – T1046 – Network Service Discovery

·      Discovery – T1083 – File and Directory Discovery

·      Discovery – T1135 – Network Share Discovery

·      Command and Control – T1219 – Remote Access Tools

·      Command and Control – T1219.002 – Remote Access Tools: Remote Desktop Software

·      Command and Control – T1571 – Non-Standard Port

·      Command and Control – T1572 – Protocol Tunneling

·      Command and Control – T1573.001 – Encrypted Channel: Symmetric Cryptography

·      Credential Access – T1110 – Brute Force

·      Credential Access – T1110.001 – Brute Force: Password Guessing

·      Defense Evasion – T1006 – Direct Volume Access

·      Defense Evasion – T1564.005 – Hide Artifacts: Hidden File System

·      Defense Evasion – T1564.012 – Hide Artifacts: File/Path Exclusions

·      Execution – T1047 – Windows Management Instrumentation

·      Execution – T1569.002 – System Services: Service Execution

·      Lateral Movement – T1021 – Remote Services

·      Lateral Movement – T1021.001 – Remote Services: Remote Desktop Protocol

·      Lateral Movement – T1021.002 – Remote Services: SMB/Windows Admin Shares

·      Lateral Movement – T1021.006 – Remote Services: Windows Remote Management

·      Lateral Movement – T1080 – Taint Shared Content

·      Lateral Movement – T1210 – Exploitation of Remote Services

·      Lateral Movement – T1570 – Lateral Tool Transfer

·      Collection – T1039 – Data from Network Shared Drive

·      Collection – T1074 – Data Staged

·      Exfiltration – T1041 – Exfiltration Over C2 Channel

·      Exfiltration – T1048 – Exfiltration Over Alternative Protocol

·      Exfiltration – T1567.002 – Exfiltration Over Web Service: Exfiltration to Cloud Storage

References

[1] https://www.levelblue.com/blogs/levelblue-blog/extortion-as-a-service-the-latest-threat-actor-criminal-ecosystem/

[2] https://blackpointcyber.com/wp-content/uploads/2025/12/World-Leaks.pdf

[3] https://blackpointcyber.com/threat-profile/world-leaks-ransomware/

[4] https://www.halcyon.ai/threat-group/worldleaks

[5] https://www.moxfive.com/resources/moxfive-threat-actor-spotlight-world-leaks

[6] https://thehackernews.com/2024/08/cybercriminals-abusing-cloudflare.html

[7] https://www.trendmicro.com/vinfo/tw/security/news/threat-landscape/the-industrialization-of-botnets-automation-and-scale-as-a-new-threat-infrastructure

[8] https://www.morphisec.com/blog/ransomware-without-encryption-why-pure-exfiltration-attacks-are-surging-and-why-theyre-so-hard-to-catch/

[9] https://sed-cms.broadcom.com/sites/default/files/2026-01/RWN-2026-WP100_1.pdf

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About the author
Tiana Kelly
Senior Cyber Analyst & Team Lead
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