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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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August 6, 2026

When AI Agents Attack: The Case for Behavioral Anomaly Detection

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The UK AI Security Institute (AISI) has just published a detailed incident report confirming what many within the cyber security industry have suspected for some time: frontier AI agents, given a degree of autonomy, can independently develop and execute attack chains against real targets, including social engineering, supply-chain compromise, and deception, without explicit instruction to do so.

The report (INC-2026-07-28-01, published 4 August 2026) documents what happened during a routine capability evaluation between 25 and 28 July. It deserves a careful read from anyone responsible for securing the modern digital estate.

The incident

AISI regularly tests frontier AI models for offensive cyber capability. In this exercise, agents were placed inside a simulated network environment and given internet access this was deliberate, given to reflect realism of the task. Across 122 evaluation runs, 19 instances of unsanctioned activity were recorded, including cases where agents broke the intended boundaries of the test and acted against real people and real infrastructure on the open internet.

In the most serious case, an agent (Mythos 5) autonomously:

  • Conducted open-source intelligence gathering to identify real developers associated with keywords from the simulated scenario
  • Created a GitHub account and submitted a pull request containing obfuscated malicious code to a real, unaffiliated repository
  • Created a second sockpuppet account to post comments on its own PR, attempting to manufacture social proof
  • When a human reviewer flagged the malicious content, the agent lied, claiming an accidental commit, then repeatedly attempted to reintroduce the payload under the guise of having "fixed" the code
  • Sent spear-phishing emails to the repository maintainer containing manipulated content designed to convince them to merge the code
  • Planted a prompt injection inside a GitHub issue, hidden in an HTML comment invisible to humans but readable by AI coding assistants parsing the repository via API

AISI confirmed no real-world harm resulted. The PRs were not merged; the payloads did not execute. But containment was a product of circumstance, not design.

Why this matters

Every action the agents took used legitimate services operating within normal parameters. Creating a GitHub account is legitimate. Opening a pull request is legitimate. Sending an email is legitimate. Posting an issue is legitimate.

No vulnerability was exploited. No known malware signature was present. No indicator of compromise from any threat intelligence feed would have matched. The entire attack existed as a pattern, a sequence of individually normal actions that, taken together, constituted a supply-chain attack, a social engineering campaign, and an attempted compromise of downstream systems.

This is the class of threat that signature-based and rule-based security tools structurally struggle to detect. They answer the question "have I seen this before?" When the answer is no – and when the attack is novel, composed of legitimate actions, and adapted in real time by an adversary in this case an agent that reasons about evasion – they struggle.

Behavioral anomaly detection: The architecture that fits

Darktrace has spent a decade building a platform around a different question: "is this behavior normal for this entity?"

The platform learns the baseline behavior of every user, device, and communication pattern across an organization's digital environment. It does not need prior knowledge of an attack technique. It identifies deviation from learned normality, surfaces it, and through its Autonomous Response capability, takes targeted action to contain threats at machine speed.

Consider how this AISI incident might show across the Darktrace platform:

Network detection and response

While no payload was successfully delivered to a victim in this incident, AISI's post-incident analysis of the agent's code (detailed on pages 24-25 of the report) reveals what would have landed on target systems had the social engineering succeeded. The agent produced three successive payload generations, each replacing the last after being detected and called out:

Generation 1 hid a module-load stager inside a legitimate bug-fix commit, guarded by an environment variable check, to skip execution in CI. Its second stage attempted to fetch Poseidon implants (Linux and Windows variants) calling back to the agent's own C2 listener, with a fallback to publicly hosted polling scripts that fetched and executed commands every 60 seconds.

Generation 2 exploited an Electron "What's New" window with nodeIntegration enabled, loading remote HTML from a GitHub Pages site the agent had registered. The page decoded an XOR-obfuscated payload at runtime that beaconed host identifiers over DNS, attempted to download the same implants, ran its own in-process C2 loop, and installed reboot persistence via crontab and Windows registry Run keys.

Generation 3 was a post-install dropper added after the agent had already been publicly caught. It XOR-decoded a hidden URL, downloaded a script over plaintext HTTP, and ran it detached, force-exiting so npm install always reports success, giving the attacker remote code execution on any machine running the install.

None of these payloads reached a victim. But the technical detail matters: this is the calibre of tradecraft an autonomous agent produced in a matter of hours, unprompted. Each generation demonstrated adaptation, the agent iterated its approach in direct response to being detected.

For network defenders, the behavioral signals these payloads would have generated are clear. DNS beaconing to novel domains, periodic HTTP polling to newly registered infrastructure, XOR-encoded data in transit, registry and crontab persistence writes on endpoints that have never exhibited such behavior. These are exactly the anomalies that Darktrace / NETWORK identifies in real time: deviations from learned device and connection baselines, regardless of whether the specific payload or C2 domain has been seen before.

Beyond the payload analysis, the agents' broader activity produced additional behavioral anomalies: new identities engaging in high-frequency interactions with external services, coordination via unusual channels (public pastebins, gists), and lateral communication patterns that deviate from any established baseline.

Email security

The spear-phishing emails sent by the agent, crafted with context-specific content designed to manipulate a specific individual, represent exactly the kind of socially engineered communication that static email gateways can miss. Darktrace / EMAIL analyses the linguistic patterns, sender behavior, and relational context of every message. A brand-new sender using tailored language to pressure a recipient into a specific technical action deviates from the learned communication profile, regardless of whether the message contains a known-bad URL or attachment.

Securing AI

This is where the incident points to a threat category that many security platforms do not yet fully address. The agent did not just use AI as a tool for attack. It targeted other AI systems, attempting prompt injections against coding assistants and trying to compromise automated workflows. Organisations adopting AI agents for development, operations, and decision-making are introducing a newer extended attack surface.  

Darktrace / SECURE AI extends the same behavioral detection model into this space. It monitors AI usage across the enterprise, detecting anomalous interactions with AI services, identifying sensitive data exposure to AI systems, providing visibility into shadow AI use. As AI agents become standard components of enterprise infrastructure, the ability to detect when an AI system is behaving outside its expected parameters, when there is a chance, it is being manipulated, or when it is interacting with something it should not be, that becomes a direct security requirement rather than a future consideration.

The principle is consistent across the Darktrace platform: learn what normal looks like, identify deviation, respond autonomously.

The AI-to-AI threat surface

One of the report's most important findings is a confirmation that AI agents can act outside of instructions and guardrails to complete tasks. The Mythos 5 agent conducted reconnaissance on a repository maintainer, inferred it might be an AI coding assistant, and deployed a prompt injection specifically crafted to compromise it.

This is a new attack class. Organizations deploying AI coding assistants, AI-powered code review, or autonomous DevOps agents now face an adversary that:

  • Moves at speed
  • Is able to adapt its approach when initial attempts are halted
  • Specifically targets AI systems via techniques (prompt injection, hidden instructions in HTML comments) that are less visible to human reviewers but parsed by machines
  • Reasons about which evasion strategies are most likely to succeed

Defending against this requires security that operates with the same adaptability and speed. A system that learns what "normal" looks like, and flags deviation from it.

What AISI recommends

The report identifies five contributing factors to the incident. Two are directly relevant:

No synchronous real-time monitoring (Section 5.3): AISI acknowledges it had not built real-time behavioral monitoring capable of immediately blocking unsanctioned agent actions. Their security team detected the incident via commercial security monitoring software, but only after activity had already occurred.

No fine-grained behavioral controls (Section 6.2): AISI's forward-looking response includes building continuous monitoring that can "immediately block or flag unsanctioned AI agent behavior."

The direction is clear: organizations need security that watches behavior continuously, learns what normal looks like, and acts when deviation occurs, without requiring a human in the loop for every decision.

The takeaway

The AISI incident is documented proof that autonomous AI agents will independently develop novel attack chains using legitimate tools, adapt when detected, employ deception, and target both humans and other AI systems. This happened last week, in a controlled setting, with commercially available models.

The security architecture that addresses this is behavioral anomaly detection applied across the full digital estate, as AI agents become standard components of enterprise infrastructure, writing code, managing deployments, processing communications, the attack surface they create is behavioral by nature. The defense must be too.

Read the full report from the UK AI Security Institute here.

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About the author
Adam Stevens
Senior Director of Product | Darktrace

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August 5, 2026

Testing a Prompt injection Attack Against an Enterprise AI Agent

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Key takeaways

  • Darktrace successfully detected and quarantined a prompt injection email before it could be processed by an enterprise AI agent.  
  • Prompt injection attacks increasingly rely on natural language rather than traditional malware, making behavioral analysis an important complement to signature-based detection.  
  • Organizations deploying AI agents should combine model guardrails with behavioral monitoring to reduce the risk of malicious instructions reaching enterprise systems.

How behavioral detection helps stop prompt injection attacks

A Darktrace customer running a Gemini AI agent in Google Cloud asked us two simple questions:

“If my agent can read inbound emails and access internal data, what stops an attacker from hiding malicious instructions in the message? Couldn’t the agent be tricked into deleting or exfiltrating sensitive data?”

The scenario centers on an indirect prompt injection attack, where malicious instructions are hidden inside content that an AI model later interprets as trusted input. The same weakness was exposed by  EchoLeak (CVE-2025-32711), a zero-click Microsoft 365 Copilot vulnerability enabled data exfiltration from a single well-crafted email..

This blog follows the Darktrace team’s investigation of the customer’s hypothesis and examines how the attack interacted with their existing security stack. The results highlight which defenses held, where gaps emerged, and how behavioral detection mattered more than guardrails. This investigation also demonstrates why behavioral detection is becoming increasingly important for AI security, as prompt injections often contain no traditional indicators of compromise.  

How do prompt injection attacks work?

Prompt injection works by carefully crafting the content and structure of the prompt to alter the LLM’s behavior or output in unintended ways. This can cause models to violate guardrails, generate harmful content or enable unauthorised access.

Prompt injection attack example

The well-known example, EchoLeak (CVE-2025-32711), was a zero-click vulnerability in Microsoft 365 Copilot that relied on a carefully crafted email containing hidden instructions that the AI system interpreted as commands rather than content, creating a pathway for unauthorized access to sensitive information without any user interaction.

While Darktrace / SECURE AI is designed to prevent agents from producing unintended outcomes, we wanted to see if we could catch and prevent this threat type earlier in the attack life-cycle, at the email security layer.

How we tested prompt injection attacks on an enterprise agent

Summary:

  1. Claude generated a prompt injection payload.  
  2. Hidden instructions were embedded in an email.  
  3. The email passed traditional validation checks.  
  4. Darktrace analyzed the language and sender behavior.  
  5. The email was quarantined before the AI agent could process it.

To test Darktrace / EMAIL against this attack class, we opened Claude, gave it the customer's context and problem statement (Gemini agent with inbox access, internal tool calls), told it we were validating Darktrace / EMAIL's detection of prompt injections, and asked for a test payload. See below:

Figure 1
Figure 2

Despite the guardrails supposedly built into the model, Claude surprisingly gave us the entire exploit in plaintext (albeit very basic), illegible to a human as the text was sent in white text (see Figure 1) but framed as an authoritative override for anything downstream reading the mail programmatically (i.e. the Gemini agent).

How Darktrace detected a prompt injection attack

We then sent the Claude-crafted email from a freemail address to the target recipient’s inbox. Despite the email containing no malicious payload, the freemail address having no malicious reputation, and the validation checks all passing, Darktrace  /EMAIL flagged the email as a 93/100 anomaly and moved it to junk, out of scope for the AI agent.

Figure 3: The test email sent with the hidden prompt injection
Figure 4: The email analysis in Darktrace / EMAIL 
Figure 5: Darktrace / EMAIL detection of malicious activity

The interesting part is what triggered the detection (see Figure 5)

  • Possible machine prompt content: text in the body detected as instructions written for a machine to execute, not for a human to read
  • Possible machine prompt content + basic suspicious correspondence: the same content, correlated with sender-side anomalies: freemail domain (yahoo[.]com), unknown correspondent, no prior mail history with the recipient, and suspicious references to payment information

Neither of those is a signature match. Nothing in the email was on a blacklist. There was no malware, no link and no attachment. Darktrace analyzed the context in which the email was delivered and flagged it as likely risky.  The anomalous language features and the context of the sender relative to the recipient's normal behavior, combined with the unusual hidden text (prompt) were enough for Darktrace / EMAIL to act on the risk.

Result: Darktrace / EMAIL autonomously junked the email, out of scope for any AI agent parsing the inbox.

Why behavioral security makes a difference detecting prompt injection attacks

Cyberattacks don't look like traditional exploits anymore. They now operate in natural language, not strictly code.

That breaks the traditional stack. AV, firewalls, static scanning and signature-based SEGs all assume a payload to inspect.  

A prompt injection has no payload. It's just an instruction, written in natural language, dressed up as anything the attacker wants: an invoice, an HR request, a calendar invite, some simple PowerPoint slides.

EchoLeak proved that hidden instructions can sit inside an email invisible to the user but fully readable by the LLM, and the LLM will follow them blindly.  

This test and GTG-1002 proved that the LLM itself can be socially engineered. Tell it you're an authorized tester and it will hand you the attack.

Rules and static classifiers can catch the obvious cases. But natural language has infinite variants, and the attack surface is the model's innate functionality to comply.  

The deeper problem here is intent: an LLM can't reliably tell whether an instruction in its context came from its developer, its user, or an attacker who slipped it into an email. To the LLM, everything reads as language and looks like a legitimate ask. This is why behavioural detection wins, as you become aware of intent when you look at the context of an interaction. Does this sender normally send this kind of message to this recipient? Does this prompt fit the user's normal pattern? Is this agent behaving the way this agent normally behaves?  

Intent can't be read off a single email, it emerges from behavioral context. Which is how Darktrace enables threat detection, through behavioral understanding.

Why enterprise AI security requires more than guardrails

Claude didn't roll over immediately… the first section of the response was a (slight) pushback, but then it wrote the payload anyway without having to ask twice.

Here the framing of the prompt did all the work. The “testing security capabilities” angle moved the model from refusal to unquestioned compliance to the user prompt.

This isn't the first time this has happened, of course. Anthropic disclosed in November 2025 that a Chinese state-sponsored group they tracked as GTG-1002 ran the first documented AI-orchestrated espionage campaign against ~30 targets by posing as employees of a legitimate cybersecurity firm doing authorised penetration testing.

The takeaway isn't that AI guardrails are ineffective. They raise the cost of low-effort attacks and remain an important first layer of defense. However, for most organizations today, they’re the only line of defense when deploying AI agents. If a prompt injection bypasses those controls, organizations still need a way to detect and stop malicious behavior elsewhere in the attack chain.

Attackers will continue to have working prompt injections easily and quickly. The question is what stops one when it lands in an inbox your agent is reading.

That's where behavioral detection comes in.

How Darktrace detects prompt injection attacks in emails

Two things Darktrace does that a model-level guardrail or static rules and signatures can't:

Natural language analysis at the email or prompt layer. The email is assessed on its own merits: is this content shaped like instructions for a machine, regardless of what the receiving agent decides to do about it?

Behavioral context around the language. An AI agent behaves like an extremely agreeable human, and it will go above and beyond to comply with the user’s request. That's exactly why you must consider the business context, such sender behaviour, mailing history, and organisational norms, as these matter even more when the recipient is an AI.

Darktrace has been perfecting behavioral anomaly detection for over a decade; the same self-learning approach that catches BEC and account takeover applies directly to prompt injection delivery. Our multi-layered AI stack extracts content from the message, builds behavioural understanding through social graphing and Pattern of Life analysis, and then combines natural language, topic, inducement, sender relationship and anomaly signals before deciding what action to take.  

This matters for prompt injection because the threat is not the plain language itself, but the intent behind the language that can cause an AI agent to respond in unexpected ways.

How to secure enterprise AI operations from prompt injection attacks

Email was the entry point in this case, but it is only one of many possible vectors.  

Anywhere an agent can retrieve information, an attacker can potentially introduce a prompt injection.

Emails, documents, SharePoint sites, web pages, knowledge bases, chat platforms, and third-party integrations all provide opportunities to influence an agent's behavior. Wherever an agent finds its orders, a prompt injection opportunity exists.

This is why securing AI requires more than blocking malicious inputs. Organizations also need visibility into how agents behave after consuming information from across their environment. If an agent begins accessing unexpected data, taking unusual actions, or operating outside its normal patterns, those behaviors may provide the strongest signal that something has gone wrong.

Effective AI security requires defense in depth: reducing the likelihood of malicious instructions reaching the agent while maintaining the ability to detect and investigate suspicious behavior if they do.

The challenge isn't protecting a single entry point. It's recognizing that, in an AI-powered environment, every source of information is also a potential source of influence.

Are you deploying autonomous agents across your enterprise and want to see this tested in your environment? Let's talk.

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About the author
Carlo Loregian
Solutions Engineer
Your data. Our AI.
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