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March 17, 2026

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

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.
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
Tiana Kelly
Senior Cyber Analyst & Team Lead
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 image
17
Mar 2026

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

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
Tiana Kelly
Senior Cyber Analyst & Team Lead

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July 15, 2026

Security After Signatures: Operating in a World of Pre‑CVE Disclosure Exploitation, Collapsed Trust Boundaries, and Autonomous Systems

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Three shifts have reshaped what it means to defend an enterprise securely.  

First, exploitation often begins before defenders have a Common Vulnerabilities and Exposures (CVE) identifier, a security advisory, or an entry in the Cybersecurity and Infrastructure Security Agency's (CISA) Known Exploited Vulnerabilities (KEV) catalog.

Secondly, the trust boundary has moved beyond the network edge into identities, tokens, APIs, and Software-as-a-Service (SaaS) workflows.  

Third, an increasing share of business activity is executed through automation, integrations, and AI agent-like systems that can act faster than teams can verify intent.  

If your security model still relies on detecting known bad artefacts, triaging isolated alerts, and waiting for confirmation before acting, you are already behind the threat.  

This is not a failure of security teams; it’s a failure of the operating model to keep pace with how the environment has changed.

A SOC built around alerts and signatures assumes that malicious activity will eventually surface as an event. In real incidents, however, the decisive evidence is rarely a single event. Instead, it is a chain of individually explainable actions that only appears malicious once you connect the dots across identity, non-human identity, cloud, email, SaaS, operational technology (OT), and network telemetry.

The defenders succeeding today observe behaviors, link them into sequences, understand what those sequences mean, and contain impact before the full story unfolds. That is the operating model the current threat environment demands.  

Exploitation before disclosure

The first shift is the straightforward: the time to exploit has dropped to nearly zero.  

In one example, Darktrace observed a sequence of subtle but strategically significant anomalies within a customer environment that later aligned with exploitation of CVE‑2025‑0994 in Trimble Cityworks by likely Chinese-nexus threat actors. Behavioral indicators were visible at least 18 days before public disclosure, with related anomalies emerging 40 to 50 days earlier during the intrusion window.  

This case illustrates a familiar pattern: clusters of weak‑signal anomalies combing to form an actionable picture of intrusion long before a CVE is published. Such activity reflects long‑horizon, option‑preserving operator models often associated with mature state‑linked activity.  

Figure 1: Darktrace’s detection of malicious exploitation of CVE 2025-0994, later tied to Chinese-nexus threat actors targeting critical national infrastructure (CNI) in the US, weeks before public disclosure.

Throughout 2025 and 2026, Darktrace has continued to observe the value of anomaly-based detections across a range of incidents.

CVE CVE Public Disclosure Date Darktrace Detection Date Days Between Detection of Exploitation and CVE Public Disclosure
CVE 2025 0994
(Trimble City Works)
2025-02-06 2025-01-19 18 Days
CVE 2025-24183
(Apache)
2025-03-10 2025-02-18 20 days
CVE 2025-10035
(Fortra GoAnywhere)
2025-09-18 2025-09-11 7 days

Identity is the real control plane

The second shift is that identity has replaced perimeter as the primary control plane. As Darktrace’s Annual Threat Report 2026 illustrated, identity remains the main challenge in defending against modern intrusions. A clear example is the Adversary-in-the-Middle (AiTM) case published by Darktrace in December 2025. A phishing email led to the compromise of an Office 365 account. Session hijacking bypassed multi-factor authentication (MFA), and the compromised account was used for follow-on phishing and persistence activities including the creation of malicious email rules.  

Every step in that sequence mattered. A successful login alone does not prove legitimacy. An inbox rule, on its own, may not appear catastrophic. Mail activity, viewed in isolation, may seem operationally normal. But the behavioral chain tells a different story: credential theft, token abuse, persistence, and onward compromise through a trusted identity.  

This is why the question is no longer “Did the user authenticate successfully”. The more important question is, “Does this identity action make sense right now, in this context, given what came before it?” The AiTM case shows how identity can be compromised. In practice, however, attacks rarely remained confined to identity alone.  

In another Darktrace case, a compromised SaaS account triggered activity across the email, SaaS, and network layers, including inbox rule changes, phishing propagation, and connections to suspicious infrastructure. Viewed in isolation, none of these events were decisive. Together, however,  they formed a behavioral sequence that revealed the intrusion, with the full attack story automatically correlated and surfaced to defenders by Darktrace’s Cyber AI Analyst.  

Figure 2: Cyber AI Analyst correlated and appended additional events to the incident, including other users who connected to the suspicious redirect link after outbound phishing emails were sent.

AI accelerates the threat  

The third shift is the one many teams still underestimate: trusted tooling, integrations, and AI agent-like systems can create actions that appear legitimate but are strategically dangerous.  

The shift becomes clearer when examining how governments are now framing AI risk. In 2026, guidance published by CISA, UK’s National Cyber Security Centre (NCSC) and Five Eyes partners warned that agentic systems expand attack surfaces, accumulate privilege, and can behave in ways that are difficult to predict or explain [1]. The advice is simple: assume unexpected behavior and design controls around it.  

The real risk is not AI usage. It is unknown autonomy: systems with credentials, data access, and action paths that can execute workflow steps without sufficient behavioral validation, traceability, or human oversight. Darktrace’s Model Context Protocol (MCP) risk analysis provides a useful framework for understanding this challenge. Over-privileged agents, content injection, and tool abuse become high-consequence risks when connected systems can dynamically retrieve data, execute actions, and communicate externally.  

Whether security teams like it or not, AI is already in the enterprise. It will help drive innovation, but it will also be abused, whether accidentally or maliciously. In each of the cases below, AI either scaled the attacker, built the tooling, or existed within the environment as something to exploit or misuse.

1. AI as an Attack Multiplier

In one campaign targeting Mexican government entities, a single operator used commercial AI platforms to generate exploits, automate reconnaissance, and process large volumes of data, compressing work that would traditionally have required an entire team into a single workflow [2].  

Darktrace is also observing this trend further down the stack. In one case, Darktrace identified AI-generated malware exploiting React2Shell, where an attacker used a Large Language Model (LLM) to produce working exploit code and deploy it at scale.  

[darktrace.com], [darktrace.com]

2. AI as an Attack Surface

Attempted AI exploitation is now appearing within customer environments. In one case involving an automation technology manufacturer, a compromised LLM proxy was seemingly used as a stepping stone to access additional AI services. When that attempt failed, the attacker pivoted to cryptomining.

What is clear is that the AI layer has already become an asset worth probing, exploiting, and pivoting through. It is also clear that defenders benefit from rapidly understanding how these activities connect. In this case, Cyber AI Analyst automatically pieced together the intrusion, while Darktrace’s Managed Threat Detection service alerted to the customer, enabling the activity to be contained before it could progress further.

Figure 3: Cyber AI Analyst's investigation into a compromised LLM proxy that was abused for cryptomining activity.

AI as a trusted but dangerous actor

This does not require a cinematic vision of “rogue AI.” The Salesloft incident provides a more grounded example, where AI and automation operate with legitimate access but served malicious intent. In that case, attackers abused compromised OAuth tokens associated with the Drift AI chat agent to export significant volumes of data from Salesforce environments.  

The activity resembled legitimate API usage and relied on trusted SaaS integrations rather than malware or other obvious signs of intrusion. That is precisely the challenge. Traditional security controls are good at detecting forced entry, but far less effective when a trusted application integration behaves in a way that is technically permitted yet operationally harmful.  

In these scenarios, the security challenge shifts from validating access to validating behavior.

This is what that looks like in practice: AI-linked identities executing legitimate actions that require behavioral validation rather than access validation.

Figure 4: Darktrace / SECURE AI highlights anomalous activity across AI identities, surfacing critical behavior that requires validation and containment.

Early observations from Darktrace / SECURE AI deployments reinforce this reality. Across Darktrace's observed fleet, AI service connections per deployment increased 13% during the first half of 2026, reaching over 16 million connections overall. The typical organisation now interacts with seven different AI providers, evidence that AI is no longer operating at the edges of the enterprise. It is increasingly woven into day-to-day business activity.

The most common risks are not compromised models or advanced AI attacks. Instead, they stem from employees and business functions exposing sensitive information through entirely legitimate-looking interactions. Darktrace has observed repeated submission of personally identifiable information (PII), tax information, identification documents, and medical data into LLM prompts, alongside widespread use of unsanctioned (shadow) AI services and growing AI activity from mobile devices.  

For defenders, the challenge is increasingly one of context: understanding when legitimate business use crosses into material risk, while preserving privacy and user trust.

Conclusion

Across all three shifts, the pattern is the same: behavior precedes understanding. Security teams are not losing because adversaries have become invisible. An increasingly outdated security model assumes that malicious activity will reveal itself cleanly and early. It no longer does.  

In 2026 and beyond, defenders win by understanding behavioral sequences, continuously validating trust, and acting before certainty becomes hindsight. That is security after signatures. That is security in the AI era.

Credit to: Daniel Levy, Threat Hunting Data Scientist

Edited by: Ryan Traill, Content Manager

References

[1] https://www.cyber.gov.au/business-government/secure-design/artificial-intelligence/careful-adoption-of-agentic-ai-services  

[2]https://www.latimes.com/business/story/2026-02-26/hacker-used-anthropics-claude-ai-to-steal-mexican-government-data

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Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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July 9, 2026

When AI Infrastructure Becomes Part of the Attack Surface

ai infrastructure cybersecurityDefault blog imageDefault blog image

AI Infrastructure and the Evolving Attack Surface

As organizations deploy generative AI into production environments, a new layer of infrastructure has emerged inside enterprise cloud environments: AI gateways.

What is an AI gateway?

AI gateways are systems that sit between users, applications, and foundation models, often holding privileged cloud permissions and managing access to AI services at scale.

Because of that role, AI gateways are becoming an increasingly important part of the enterprise attack surface. A compromise may provide attackers with access not only to compute resources, but also to cloud identities, model services, sensitive prompts, and other connected systems.

This blog examines how Darktrace investigated a compromised AI gateway connected to Amazon Bedrock services that was subsequently observed communicating with cryptomining infrastructure. Based on its configuration and associated Identity and Access Management (IAM) role, the instance appeared to function as a gateway to Amazon Bedrock-hosted AI services. Following suspected compromise activity, the host was observed communicating repeatedly with known cryptomining infrastructure before subsequently being shut down. Darktrace detected and escalated the activity through its Enhanced Monitoring and Managed Threat Detection services.

While the ultimate impact in this case appeared to be unauthorized cryptomining, the incident is notable because of where it occurred. The compromised asset sat at the intersection of cloud infrastructure, identity, and AI services. Recent research has highlighted how AI gateways such as LiteLLM can become attractive targets due to their ability to centralize credentials, model access, and cloud permissions. Although Darktrace found no evidence linking this activity directly to publicly disclosed LiteLLM vulnerabilities, the incident demonstrates why organizations should treat AI infrastructure as part of their critical attack surface rather than as a standalone application tier [1].

Why cryptomining remains a common cloud post-compromise activity

Cryptomining can be a lucrative post-compromise activity in cloud environments. After gaining access to a cloud asset, attackers may deploy mining software to abuse the victim’s compute resources for financial gain. This type of activity is likely to be opportunistic, targeting exposed services, weak credentials, leaked access keys, vulnerable applications, or misconfigured cloud workloads.

A typical cloud cryptomining intrusion may involve:

  • Identifying exposed or vulnerable cloud infrastructure
  • Gaining access through exposed services, credentials, or application weaknesses
  • Downloading and executing mining software
  • Establishing repeated outbound connectivity to mining pool infrastructure
  • Continuing to consume compute resources until the activity is detected and disrupted

The notable element in this case is not the cryptomining alone, but where it occurred: on cloud infrastructure supporting AI-related activity. This shows how assets used to enable AI services can still be exposed to familiar cloud compromise risks.

Investigating a compromised AI gateway connected to Amazon Bedrock

On June 12, 2026, Darktrace observed activity consistent with active cryptomining from an Amazon Web Service (AWS) EC2 instance named LiteLLM-Proxy. The instance appeared to support LiteLLM activity and was associated with an instance profile that had access to Amazon Bedrock resources.

AI gateways are designed to centralize access to large language models, often handling authentication, routing, logging, and policy enforcement for AI applications. From a security perspective, they also aggregate cloud permissions, model access, and application workflows into a single control point. As a result, compromise of an AI gateway can have implications beyond the affected host itself.

While the exact initial access vector could not be confirmed, the activity appears to follow a sequence often seen in compromises of internet-facing systems: brute-forced access, payload delivery, and repeated outbound connectivity to mining pool infrastructure.

Stage 1: Internet-exposed SSH enabled initial access

Prior to the observed cryptomining activity, the LiteLLM-Proxy EC2 instance appeared to be externally exposed over SSH, with port 22 open to 0.0.0.0/0.

Figure 1: Darktrace’s misconfiguration alert EC2 instance allowing all inbound traffic to SSH port 22.

Prior to the cryptomining activity, Darktrace observed a large volume of inbound connection attempts to the instance over port 22 from external IP addresses, predominantly from 145.241.123[.]102, suggesting brute-force activity [2]. Many of these connections were short-lived, lasting only a few seconds, indicating scanning or failed login attempts.

Figure 2: Darktrace’s detection of unusual incoming connection attempts to the device over port 22.

The available telemetry did not confirm whether any inbound SSH connection resulted in successful authentication, preventing this activity from being confirmed as the initial access vector. However, the combination of public SSH exposure, inbound connections from external IP addresses, and subsequent miner activity suggests that SSH was a plausible access path.

Stage 2: XMRig malware downloaded to the AI gateway

Before the first observed connection to the mining pool, the EC2 instance downloaded 3.42 MB of data over an HTTP connection on port 80 to the external endpoint, 185.62.1[.]8, which appears to host a ZIP file containing XMRig crypto-mining malware [3][4]. As host-level logs were not available, Darktrace could not confirm how the miner was executed or whether the earlier SSH activity directly enabled payload delivery. However, the timing of the download, followed shortly by repeated mining pool connectivity, supported the assessment that the instance had been compromised and was being used for unauthorized compute activity.

Stage 3 – Compromised AI gateway communicates with cryptomining infrastructure

Just a few minutes later, Darktrace observed the LiteLLM-Proxy EC2 instance connecting to the hostname pool.hasvault[.]pro over HTTPs on port 443. Following the initial connection, repeated outbound connectivity to the same hostname was observed. This pattern is consistent with active cryptomining pool communication, where a compromised host communicates with mining infrastructure to receive work and submit results.

This activity triggered the Enhanced Monitoring model “Compromise / High Priority Crypto Currency Mining”, which was escalated to the customer by Darktrace’s SOC. The activity was also summarized by Darktrace’s Cyber AI Analyst, which grouped the relevant events into a single investigation narrative, helping to identify the repeated mining pool connectivity from the affected cloud asset.

Figure 3: Cyber AI Analyst’s investigation of the cryptocurrency mining activity.

The use of HTTPS over port 443 is notable because, when viewed in isolation, this traffic may not appear inherently suspicious. In this case, however, the destination, volume of connections, and lack of similar activity provided the behavioral context needed to identify the communication as suspicious.

Stage 4: Managed Threat Detection identifies active resource abuse

The cryptomining activity was received by Darktrace’s Managed Threat Detection service and reviewed by Darktrace’s SOC. Following review, the activity was escalated to the customer. This escalation provided the customer with timely notification of active resource abuse in the AWS environment.

Stage 5: Suspicious IAM activity suggests possible cloud credential misuse

Separately, on June 13, Darktrace observed suspicious activity originating from an additional IAM user.

Figure 4: Darktrace’s Advanced Search highlighting suspicious activity performed by a second IAM user.

First, the user was observed attempting the “GetSendQuota” event, an action that had not performed by the account within at least the previous three months. Additionally, the source IP address of this command appeared to be 14.176.1[.]47, geolocated in Vietnam, whereas activity for this user had mostly been seen from Amazon IP addresses. Furthermore, the AWS CLI was also observed being used for this activity, which was also unusual for the user. This was detected by the model “IaaS / Unusual Activity / Unusual AWS CLI Activity”.

Figure 5: Darktrace’s detection of the “GetSendQuota” event.

Further suspicious activity was observed from the IAM user using the long-term access key. Notably, failed “InvokeModel” and “ListFoundationModels” commands were detected, suggesting attempted interaction with Amazon Bedrock services, including model enumeration or invocation. While this may suggest relation to the LiteLLM compromise observed the previous day, there is insufficient evidence to conclusively link the two events.

The attempted “CreateUser” command was also notable because the requested username appeared low-meaning, which may indicate an attempt to establish persistence by creating a new account. This activity triggered the model “IaaS / Admin / New AWS User Account Creation”.

Figure 6: Darktrace’s detection of the “CreateUser” event.

Even without a confirmed link between the two incidents, the IAM activity remains significant. It demonstrates the importance of incorporating workload both telemetry and control-plane telemetry into cloud compromise investigations. While the EC2 cryptomining activity indicated compute resource abuse, the IAM activity suggested potential credential compromise or misuse involving long-term access keys, along with attempted cloud service abuse.

Key lessons for securing AI infrastructure

This incident was notable not because of the cryptomining activity itself, but because of where it occurred. The compromised system appeared to function as an AI gateway with access to Amazon Bedrock services, placing it at the intersection of cloud infrastructure, identity, and AI operations. As organizations deploy AI capabilities into production environments, these platforms are becoming part of the same attack surface that adversaries already target through exposed services, credential theft, and cloud misconfigurations.

While the exact intrusion path could not be confirmed, and no definitive link was established between the compromised workload and the suspicious IAM activity observed during the investigation, both events reinforce a broader reality: AI infrastructure must be secured as part of the wider cloud environment rather than treated as a separate technology stack.

In this case, the most obvious sign of compromise was communication with cryptomining infrastructure. The more important lesson is that Darktrace’s behavioral analysis revealed risk surrounding a privileged AI-enabled asset before the full scope of the incident was understood. As AI gateways increasingly concentrate cloud permissions, model access, and application workflows, defenders will need to focus less on individual alerts and more on understanding how behaviors connect across workloads, identities, and services.

Credit to Angel Arribas Lopez (Associate Principal Cyber Analyst), Nathaniel Jones (Field CISO/VP Threat Research), Emma Foulger (Global Threat Ops),  and Mark Turner (Security Researcher)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

·       Compromise / High Priority Crypto Currency Mining

·       Compromise / Monero Mining

·       Device / Internet Facing Device with High Priority Alert

·       IaaS / Unusual Activity / Unusual AWS CLI Activity

·       IaaS / Admin / New AWS User Account Creation

MITRE ATT&CK Mapping

Initial Access – External Remote Services – T1133

Initial Access – Valid Accounts – T1078

Execution – Command and Scripting Interpreter – T1059

Persistence – Create Account – T1136

Discovery – Cloud Service Discovery – T1526

Impact – Resource Hijacking – T1496

References

[1] https://docs.litellm.ai/blog/security-update-march-2026

[2] https://www.abuseipdb.com/check/145.241.123.102

[3] https://urlscan.io/search/#185.62.1.8

[4] https://www.virustotal.com/gui/file/85de36ff66fae9f4b059cbedf6d36e017ebc26c828f99f911a96e78636f21200/community

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About the author
Angel Arribas Lopez
Associate Principal Cyber Analyst
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