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January 8, 2024

Uncovering CyberCartel Threats in Latin America

Examine the growing threat of cyber cartels in Latin America and learn how to safeguard against their attacks.
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
Alexandra Sentenac
Cyber Analyst
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08
Jan 2024

Introduction

In September 2023, Darktrace published its first Half-Year Threat Report, highlighting Threat Research, Security Operation Center (SOC), model breach, and Cyber AI Analyst analysis and trends across the Darktrace customer fleet. According to Darktrace’s Threat Report, the most observed threat type to affect Darktrace customers during the first half of 2023 was Malware-as-a-Service (Maas). The report highlighted a growing trend where malware strains, specifically in the MaaS ecosystem, “use cross-functional components from other strains as part of their evolution and customization” [1].  

Darktrace’s Threat Research team assessed this ‘Frankenstein’ approach would very likely increase, as shown by the fact that indicators of compromise (IoCs) are becoming “less and less mutually exclusive between malware strains as compromised infrastructure is used by multiple threat actors through access brokers or the “as-a-Service” market” [1].

Darktrace investigated one such threat during the last months of summer 2023, eventually leading to the discovery of CyberCartel-related activity across a significant number of Darktrace customers, especially in Latin America.

CyberCartel Overview and Darktrace Coverage

During a threat hunt, Darktrace’s Threat Research team discovered the download of a binary with a unique Uniform Resource Identifier (URI) pattern. When examining Darktrace’s customer base, it was discovered that binaries with this same URI pattern had been downloaded by a significant number of customer accounts, especially by customers based in Latin America. Although not identical, the targets and tactics, techniques, and procedures (TTPs) resembled those mentioned in an article regarding a botnet called Fenix [2], particularly active in Latin America.

During the Threat Research team’s investigation, nearly 40 potentially affected customer accounts were identified. Darktrace’s global Threat Research team investigates pervasive threats across Darktrace’s customer base daily. This cross-fleet research is based on Darktrace’s anomaly-based detection capability, Darktrace DETECT™, and revolves around technical analysis and contextualization of detection information.

Amid the investigation, further open-source intelligence (OSINT) research revealed that most indicators observed during Darktrace’s investigations were associated to a Latin American threat group named CyberCartel, with a small number of IoCs being associated with the Fenix botnet. While CyberCartel seems to have been active since 2012 and relies on MaaS offerings from well-known malware families, Fenix botnet was allegedly created at the end of last year and “specifically targets users accessing government services, particularly tax-paying individuals in Mexico and Chile” [2].

Both groups share similar targets and TTPs, as well as objectives: installing malware with information-stealing capabilities. In the case of Fenix infections, the compromised device will be added to a botnet and execute tasks given by the attacker(s); while in the case of CyberCartel, it can lead to various types of second-stage info-stealing and Man-in-the-Browser capabilities, including retrieving system information from the compromised device, capturing screenshots of the active browsing tab, and redirecting the user to fraudulent websites such as fake banking sites. According to a report by Metabase Q [2], both groups possibly share command and control (C2) infrastructure, making accurate attribution and assessment of the confidence level for which group was affecting the customer base extremely difficult. Indeed, one of the C2 IPs (104.156.149[.]33) observed on nearly 20 customer accounts during the investigation had OSINT evidence linking it to both CyberCartel and Fenix, as well as another group known to target Mexico called Manipulated Caiman [3] [4] [5].

CyberCartel and Fenix both appear to target banking and governmental services’ users based in Latin America, especially individuals from Mexico and Chile. Target institutions purportedly include tax administration services and several banks operating in the region. Malvertising and phishing campaigns direct users to pages imitating the target institutions’ webpages and prompt the download of a compressed file advertised in a pop-up window. This file claims enhance the user’s security and privacy while navigating the webpage but instead redirects the user to a compromised website hosting a zip file, which itself contains a URL file containing instructions for retrieval of the first stage payload from a remote server.

pop-up window with malicious file
Figure 1: Example of a pop-up window asking the user to download a compressed file allegedly needed to continue navigating the portal. Connections to the domain srlxlpdfmxntetflx[.]com were observed in one account investigated by Darktrace

During their investigations, the Threat Research team observed connections to 100% rare domains (e.g., situacionfiscal[.]online, consultar-rfc[.]online, facturmx[.]info), many of them containing strings such as “mx”, “rcf” and “factur” in their domain names, prior to the downloads of files with the unique URI pattern identified during the aforementioned threat hunting session.

The reference to “rfc” is likely a reference to the Registro Federal de Contribuyentes, a unique registration number issued by Mexico’s tax collection agency, Servicio de Administración Tributaria (SAT). These domains were observed as being 100% rare for the environment and were connected to a few minutes prior to connections to CyberCartel endpoints. Most of the endpoints were newly registered, with creation dates starting from only a few months earlier in the first half of 2023. Interestingly, some of these domains were very similar to legitimate government websites, likely a tactic employed by threat actors to convince users to trust the domains and to bypass security measures.

Figure 2: Screenshot from similarweb[.]com showing the degree of affinity between malicious domains situacionfiscal[.]online and facturmx[.]info and the legitimate Mexican government hostname sat[.]gob[.]mx
Figure 3: Screenshot of the likely source infection website facturmx[.]info taken when visited in a sandbox environment

In other customer networks, connections to mail clients were observed, as well as connections to win-rar[.]com, suggesting an interaction with a compressed file. Connections to legitimate government websites were also detected around the same time in some accounts. Shortly after, the infected devices were detected connecting to 100% rare IP addresses over the HTTP protocol using WebDAV user agents such as Microsoft-WebDAV-MiniRedir/10.0.X and DavCInt. Web Distributed Authoring and Versioning, in its full form, is a legitimate extension to the HTTP protocol that allows users to remotely share, copy, move and edit files hosted on a web server. Both CyberCartel and Fenix botnet reportedly abuse this protocol to retrieve the initial payload via a shortcut link. The use (or abuse) of this protocol allows attackers to evade blocklists and streamline payload distribution. In cases investigated by Darktrace, the use of this protocol was not always considered unusual for the breach device, indicating it also was commonly used for its legitimate purposes.

HTTP methods observed included PROPFIND, GET, and OPTIONS, where a higher proportion of PROPFIND requests were observed. PROPFIND is an HTTP method related to the use of WebDAV that retrieves properties in an exactly defined, machine-readable, XML document (GET responses do not have a define format). Properties are pieces of data that describe the state of a resource, i.e., data about data [7]. They are used in distributed authoring environments to provide for efficient discovery and management of resources.  

Figure 4: Device event log showing a connection to facturmx[.]info followed by a WebDAV connection to the 100% rare IP 172.86.68[.]104

In a number of cases, connections to compromised endpoints were followed by the download of one or more executable files with names following the regex pattern /(yes|4496|[A-Za-z]{8})/(((4496|4545)[A-Za-z]{24})|Herramienta_de_Seguridad_SII).(exe|jse), for example 4496UCJlcqwxvkpXKguWNqNWDivM.exe. PROPFIND and GET HTTP requests for dynamic-link library (DLL) files such as urlmon.dll and netutils.dll were also detected. These are legitimate Windows files that are essential to handle network and internet-related tasks in Windows. Irrespective of whether they had malicious or legitimate signatures, Darktrace DETECT was able to recognize that the download of these files was suspicious with rare external endpoints not previously observed on the respective customer networks.

Figure 5: Advanced Search results showing some of the HTTP requests made by the breach device to a CyberCartel endpoint via PROPFIND, GET, or OPTIONS methods for executable and DLL files

Following Darktrace DETECT’s model breaches, these HTTP connections were investigated by Cyber AI Analyst™. AI Analyst provided a summary and further technical details of these connections, as shown in figure 6.

Figure 6: Cyber AI Analyst incident showing a summary of the event, as well as technical details. The AI investigation process is also detailed

AI Analyst searched for all HTTP connections made by the breach device and found more than 2,500 requests to more than a hundred endpoints for one given device. It then looked for the user agents responsible for these connections and found 15 possible software agents responsible for the HTTP requests, and from these identified a single suspicious software agent, Microsoft-WebDAV-Min-Redir. As mentioned previously, this is a legitimate software, but its use by the breach device was considered unusual by Darktrace’s machine learning technology. By performing analysis on thousands of connections to hundreds of endpoints at machine speed, AI Analyst is able to perform the heavy lifting on behalf of human security teams and then collate its findings in a single summary pane, giving end-users the information needed to assess a given activity and quickly start remediation as needed. This allows security teams and administrators to save precious time and provides unparalleled visibility over any potentially malicious activity on their network.

Following the successful identification of CyberCartel activity by DETECT, Darktrace RESPOND™ is then able to contain suspicious behavior, such as by restricting outgoing traffic or enforcing normal patterns of life on affected devices. This would allow customer security teams extra time to analyze potentially malicious behavior, while leaving the rest of the network free to perform business critical operations. Unfortunately, in the cases of CyberCartel compromises detected by Darktrace, RESPOND was not enabled in autonomous response mode meaning preventative actions had to be applied manually by the customer’s security team after the fact.

Figure 7. Device event log showing connections to 100% rare CyberCartel endpoint 172.86.68[.]194 and subsequent suggested RESPOND actions.

Conclusion

Threat actors targeting high-value entities such as government offices and banks is unfortunately all too commonplace.  In the case of Cyber Cartel, governmental organizations and entities, as well as multiple newspapers in the Latin America, have cautioned users against these malicious campaigns, which have occurred over the past few years [8] [9]. However, attackers continuously update their toolsets and infrastructure, quickly rendering these warnings and known-bad security precautions obsolete. In the case of CyberCartel, the abuse of the legitimate WebDAV protocol to retrieve the initial payload is just one example of this. This method of distribution has also been leveraged by in Bumblebee malware loader’s latest campaign [10]. The abuse of the legitimate WebDAV protocol to retrieve the initial CyberCartel payload outlined in this case is one example among many of threat actors adopting new distribution methods used by others to further their ends.

As threat actors continue to search for new ways of remaining undetected, notably by incorporating legitimate processes into their attack flow and utilizing non-exclusive compromised infrastructure, it is more important than ever to have an understanding of normal network operation in order to detect anomalies that are indicative of an ongoing compromise. Darktrace’s suite of products, including DETECT+RESPOND, is well placed to do just that, with machine-speed analysis, detection, and response helping security teams and administrators keep their digital environments safe from malicious actors.

Credit to: Nahisha Nobregas, SOC Analyst

References

[1] https://darktrace.com/blog/darktrace-half-year-threat-report

[2] https://www.metabaseq.com/fenix-botnet/

[3] https://perception-point.io/blog/manipulated-caiman-the-sophisticated-snare-of-mexicos-banking-predators-technical-edition/

[4] https://www.virustotal.com/gui/ip-address/104.156.149.33/community

[5] https://silent4business.com/tendencias/1

[6] https://www.metabaseq.com/cybercartel/

[7] http://www.webdav.org/specs/rfc2518.html#rfc.section.4.1

[8] https://www.csirt.gob.cl/alertas/8ffr23-01415-01/

[9] https://www.gob.mx/sat/acciones-y-programas/sitios-web-falsos

[10] https://www.bleepingcomputer.com/news/security/bumblebee-malware-returns-in-new-attacks-abusing-webdav-folders/

Appendices  

Darktrace DETECT Model Detections

AI Analyst Incidents:

• Possible HTTP Command and Control

• Suspicious File Download

Model Detections:

• Anomalous Connection / New User Agent to IP Without Hostname

• Device / New User Agent and New IP

• Anomalous File / EXE from Rare External Location

• Multiple EXE from Rare External Locations

• Anomalous File / Script from Rare External Location

List of IoCs

IoC - Type - Description + Confidence

f84bb51de50f19ec803b484311053294fbb3b523 - SHA1 hash - Likely CyberCartel Payload IoCs

4eb564b84aac7a5a898af59ee27b1cb00c99a53d - SHA1 hash - Likely CyberCartel payload

8806639a781d0f63549711d3af0f937ffc87585c - SHA1 hash - Likely CyberCartel payload

9d58441d9d31b5c4011b99482afa210b030ecac4 - SHA1 hash - Possible CyberCartel payload

37da048533548c0ad87881e120b8cf2a77528413 - SHA1 hash - Likely CyberCartel payload

2415fcefaf86a83f1174fa50444be7ea830bb4d1 - SHA1 hash - Likely CyberCartel payload

15a94c7e9b356d0ff3bcee0f0ad885b6cf9c1bb7 - SHA1 hash - Likely CyberCartel payload

cdc5da48fca92329927d9dccf3ed513dd28956af - SHA1 hash - Possible CyberCartel payload

693b869bc9ba78d4f8d415eb7016c566ead839f3 - SHA1 hash - Likely CyberCartel payload

04ce764723eaa75e4ee36b3d5cba77a105383dc5 - SHA1 hash - Possible CyberCartel payload

435834167fd5092905ee084038eee54797f4d23e - SHA1 hash - Possible CyberCartel payload

3341b4f46c2f45b87f95168893a7485e35f825fe - SHA1 hash - Likely CyberCartel payload

f6375a1f954f317e16f24c94507d4b04200c63b9 - SHA1 hash - Likely CyberCartel payload

252efff7f54bd19a5c96bbce0bfaeeecadb3752f - SHA1 hash - Likely CyberCartel payload

8080c94e5add2f6ed20e9866a00f67996f0a61ae - SHA1 hash - Likely CyberCartel payload

c5117cedc275c9d403a533617117be7200a2ed77 - SHA1 hash - Possible CyberCartel payload

19dd866abdaf8bc3c518d1c1166fbf279787fc03 - SHA1 hash - Likely CyberCartel payload

548287c0350d6e3d0e5144e20d0f0ce28661f514 - SHA1 hash - Likely CyberCartel payload

f0478e88c8eefc3fd0a8e01eaeb2704a580f88e6 - SHA1 hash - Possible CyberCartel payload

a9809acef61ca173331e41b28d6abddb64c5f192 - SHA1 hash - Likely CyberCartel payload

be96ec94f8f143127962d7bf4131c228474cd6ac - SHA1 hash -Likely CyberCartel payload

44ef336395c41bf0cecae8b43be59170bed6759d - SHA1 hash - Possible CyberCartel payload

facturmx[.]info - Hostname - Likely CyberCartel infection source

consultar-rfc[.]online - Hostname - Possible CyberCartel infection source

srlxlpdfmxntetflx[.]com - Hostname - Likely CyberCartel infection source

facturmx[.]online - Hostname - Possible CyberCartel infection source

rfcconhomoclave[.]mx - Hostname - Possible CyberCartel infection source

situacionfiscal[.]online - Hostname - Likely CyberCartel infection source

descargafactura[.]club - Hostname - Likely CyberCartel infection source

104.156.149[.]33 - IP - Likely CyberCartel C2 endpoint

172.86.68[.]194 - IP - Likely CyberCartel C2 endpoint

139.162.73[.]58 - IP - Likely CyberCartel C2 endpoint

172.105.24[.]190 - IP - Possible CyberCartel C2 endpoint

MITRE ATT&CK Mapping

Tactic - Technique

Command and Control - Ingress Tool Transfer (T1105)

Command and Control - Web Protocols (T1071.001)

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
Alexandra Sentenac
Cyber Analyst

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July 13, 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 Trail, 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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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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July 9, 2026

When AI Infrastructure Becomes Part of the Attack Surface

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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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