Blog
/
/
August 6, 2020

Guarding Against Threats Beyond IT

We explore insights from a vast customer database, exposing the widespread adoption of ICS protocols within IT settings.
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
David Masson
VP, Field CISO
Default blog image
06
Aug 2020

Key takeaways

  • Multiple well-known ICS attacks have been successful by gaining an initial foothold into the IT network, such as EKANS, Black Energy, and Havex
  • Stage One of the ICS Cyber Kill Chain is network reconnaissance, and so IT/OT network segregation is critical
  • Darktrace finds that many organizations’ networks have at least some level of IT/OT convergence
  • Visibility across ICS infrastructure, actions, and commands provides a better picture into potentially malicious internal activity

IT & OT Convergence Threats

Shipping, manufacturing, and other forms of heavy industry are seeing an ever-increasing convergence of IT and OT systems with the growth in Industrial Internet of Things (IIoT). At the same time, it remains critical to segment IT from OT networks, as the lack of segmentation could provide a malicious actor – either a hacker or rogue insider – easy access to pivot into the OT network.

High-profile attack campaigns such as Havex or Black Energy show traditional network security monitoring tools can be insufficient in preventing these intrusions. After the initial compromise, these ICS attacks progressed from IT to OT systems, showing that the convergence of IT and OT in cyber-physical ecosystems calls for technology that can understand how these two systems interact.

More recently, analysis of the EKANS ransomware revealed that attackers are attempting to use malware to actively disrupt OT as well as IT networks. The attack contained ICS processes on its ‘kill list,’ which allowed it to halt global manufacturing for large organizations like Honda.

More often than not, a lack of visibility is a major challenge in protecting critical ICS assets. Security specialists benefit when they have visibility over unusual or unexpected connections, or more crucially, when ICS commands are being sent by malicious actors attempting to perform industrial sabotage.

Investigation details

Darktrace analysts investigated the use of industrial protocols in the enterprise environments of various customers. The industries ranged from banking to government, retail to food manufacturing and beyond, and included companies with Industrial Control Systems that leverage Darktrace to defend their corporate networks.

In some cases, the security teams may not have been aware of IT/OT convergence within their enterprise environments. In other cases, the IT team may be aware of the ICS segments, but do not see them as a security priority because it does not fall directly within their remit.

The results revealed that hundreds of companies are using OT protocols in their enterprise environments, which suggests that IT/OT systems are not properly segmented. Specifically, Darktrace detected over 6,500 suspected instances of ICS protocol use across 1,000 environments. Note that this data was collected anonymously, only keeping track of the industry for analysis purposes.

Figure 1: A chart showing the percentage of ICS protocol use in enterprise environments

The ICS protocol which was detected the most was BacNet, seen in approximately 75% of instances. BacNet is used in Building Management Systems, so it is not surprising that it is widely used across multiple industries and within corporate networks. It is likely the security teams are aware that their BMS is part of the enterprise network, but may not appreciate how its use of the BacNet OT protocol increases the attack surface for the business and can be a blind spot for security teams.

Core ICS protocols

Darktrace also detected ‘core’ ICS protocols, Modbus and CIP (Common Industrial Protocol). These are normally associated with traditional ICS industries such as manufacturing, oil and gas, robotics, and utilities, and provides further evidence of IT/OT convergence.

This increased IT/OT convergence creates new blind spots on the network and sets up new pathways to disruption. This offers opportunities for attackers, and the public are now increasingly aware of attacks that have pivoted from IT into OT.

Improper segmentation between IT and OT systems can lead to highly unusual connections to ICS protocols. This can be seen in our recent analysis of industrial sabotage, with the timeline of the attack’s main events presented below.

Figure 2: A timeline showing the events of an incident of industrial sabotage

This is just one example of an attack that began in IT systems before affecting OT. More high-profile attacks that follow this pattern are presented below:

EKANS ransomware

The recent EKANS attack involved a strain of ransomware with close links to the MEGACORTEX variant, which gained infamy following an attack on Honda’s global operations in June 2020. Like many ransomware variants, EKANS encrypts files in IT systems and demands ransom in order to unlock the infected machines. However, the malware also has the ability to kill ICS processes on infected hosts. Notably, it is the first public example of ransomware that can target ICS operations.

Havex

Havex utilized multiple attack vectors, including spear phishing, trojans, and infected vendor websites, often known as a ‘watering hole attack’. It targeted IT systems, Internet-connected workstations, or a combination of the two. With Havex, attackers leveraged lateral movement techniques to pivot into Level 3 of ICS networks. The attack’s motive was data exfiltration to a C2 server, likely as part of a government-backed espionage campaign.

Black Energy 3

Black Energy 3 favored macro-embedded MS Office documents delivered via spear phishing emails as attack vectors. Older variants of Black Energy targeted vulnerabilities in ICS HMIs (Human Machine Interfaces) which were connected to the Internet. The attack’s motive was industrial sabotage and is what was used against the Ukrainian electric grid in 2015, leading to power outages for over 225,000 civilians and requiring a switch to manual operations as substations were taken offline.

Lessons learned

Each of the attack campaigns detailed above was in some way enabled by IT/OT convergence. Attackers still favor targeting IT networks with their initial attack vectors, as IT networks have significantly more interaction with the Internet through emails, and various other interconnected technologies. Poor network segmentation allows attackers easy access to OT systems once an IT network has been compromised.

In all of these ICS cyber-attacks, devices deviated from their normal patterns of life at one or more points in the cyber kill chain. Indicators of compromise can include anything from new external connections, to network reconnaissance using active scanning, to lateral movement using privileged credentials, ICS reprogram commands, or ICS discovery requests. With proper enterprise-wide visibility, across both IT and OT systems, and security tools that are able to detect these deviations, a security team would be alerted to these compromises before an attacker could carry out their objectives.

Ultimately, visibility is crucial for cyber defenders to protect industrial property and processes. Darktrace/OT enables many Industrial Model Detections, a selection of which are listed below:

  • Anomalous IT to ICS Connection
  • Multiple Failed Connections to OT Device
  • Multiple New Action Commands
  • Uncommon ICS Reprogram
  • Suspicious Network Scanning Activity
  • Unusual Broadcast from ICS PLC
  • Unusual Admin RDP Session

It is clear that attackers continue to exploit increasing IT/OT convergence to carry out industrial sabotage. Still, as revealed by our analysis of our customer base, many organizations continue to unknowingly use ICS protocols in their corporate environments, both increasing their attack surface and creating dangerous blind spots. A new, holistic approach to cyber defense is needed – one that can reveal this convergence of IT and OT, provide visibility, and detect deviations indicative of emerging cyber-attacks against critical systems.

Thanks to Darktrace analyst Oakley Cox for his insights on the above investigation.

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
David Masson
VP, Field CISO

More in this series

No items found.

Blog

/

AI

/

August 5, 2026

Testing a Prompt injection Attack Against an Enterprise AI Agent

prompt injectionDefault blog imageDefault blog image

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.

Continue reading
About the author
Carlo Loregian
Solutions Engineer

Blog

/

AI

/

August 4, 2026

Extending AI Security Visibility with Darktrace and Microsoft Agent 365

microsoft darktrace integrationDefault blog imageDefault blog image

AI agents are rapidly becoming embedded in everyday business operations, helping employees automate workflows, access information, and accelerate decision-making. As organizations embrace agentic AI, security teams face a growing challenge: understanding how AI is being used, what agents can access, and where risk may emerge.

As agents take on more business-critical work, security teams often need to move across multiple portals to understand risks spanning identity, data, and threat activity. This fragmented view can make it difficult to assess an agent's overall risk posture and determine where attention is needed. Organizations need a way to bring these signals together without disrupting existing security investments or workflows.

Today, Darktrace is announcing an integration between Darktrace / SECURE AI and Microsoft Agent 365 that brings Darktrace's Adaptive AI-driven risk signals directly into the Microsoft 365 Admin Center. By extending the visibility and risk understanding provided by Darktrace / SECURE AI into the Microsoft ecosystem, organizations can gain a more unified understanding of AI agent risk across their environments.

As one of the first security companies to partner with Microsoft to contribute third-party risk signals to the Agent Registry, Darktrace is helping shape how organizations understand and manage AI agent risk.

Extending visibility into the Microsoft Agent 365 experience

Microsoft Agent 365 provides administrators with a centralized registry of AI agents operating within their environment. As organizations expand their use of AI agents, this centralized visibility becomes increasingly important for governance and oversight.

This new integration extends that visibility by allowing Darktrace-generated risk signals to be surfaced directly within the Microsoft Agent 365 experience. By combining Microsoft's agent management and security capabilities with Darktrace's AI-powered risk analysis, organizations gain greater awareness of potential security concerns associated with AI agents operating across their environments.

By integrating Darktrace telemetry into Agent 365, customers can:  

  • Surface Darktrace-detected risks and signals alongside Microsoft-native signals in a single interface
  • Identify potentially compromised or anomalous AI agents more quickly
  • Gain unified understanding of context and agent behavior  

This approach reinforces a single control plane for AI security while allowing organizations to continue leveraging existing investments across both platforms.  

Why unified visibility of AI agent risk signals matters

As AI adoption accelerates across Microsoft environments, organizations must manage new forms of behavior, access patterns, and risk. Security teams need more than inventories and permissions. They need visibility into how AI systems operate and how risk evolves over time.  

This integration addresses a critical gap: how to bring behavioral AI security insights into the same control plane as identity, access, and agent management.  

With Darktrace and Microsoft Agent 365 together, organizations benefit from:  

  • Unified visibility: A single pane of glass for understanding AI agent risk signals across Microsoft and Darktrace signals  
  • Faster detection of abnormal agent behavior: Darktrace's Adaptive AI highlights deviations that may not be captured by static controls  
  • Operational efficiency: Security teams can triage and prioritize risk signals without switching between systems
  • Stronger trust in AI deployments: Clear attribution, context, and investigation pathways improve confidence in AI agent usage

Extending Microsoft's AI security model, not replacing it

Securing AI requires a layered approach that combines governance, visibility, threat detection, and risk management. This integration is designed to complement Microsoft's security capabilities, not duplicate them.  

Through Darktrace / SECURE AI, Darktrace contributes:  

  • Identification of risk via advanced prompt analysis  
  • Behavioral anomaly detection across AI agents  
  • Cross-environment threat correlation  
  • Autonomous insight into emerging or unknown risks  

Microsoft provides:  

  • Centralized agent management
  • Identity and access governance
  • Native detection of risk signals and enforcement capabilities

Together, these capabilities create a more complete, layered approach to securing AI-driven enterprises. Organizations gain the governance and policy controls needed to manage AI adoption while benefiting from continuous visibility into how AI is used across the business.

Building the future of secure AI

As AI agents become more deeply embedded in business processes, organizations need more than inventories and static controls. They need to understand how AI is being used, how agents behave, and where risk is emerging across the enterprise.

Darktrace / SECURE AI delivers that understanding through continuous visibility into AI activity, helping security teams assess intent, identify behavioral drift, and uncover emerging risk across both human and agent-driven workflows. Powered by Adaptive AI, it provides the context needed to secure AI as it evolves.  

The integration with Microsoft Agent 365 extends those insights into the workflows organizations already use. Agent 365 provides a unified control plane for governing and securing AI agents, while Darktrace contributes complementary behavioral risk signals that can be surfaced within the Agent 365 experience. Together, they give customers broader context on agent activity and risk while preserving the value of their existing Microsoft and Darktrace investments.

As enterprises move from AI experimentation to AI-powered execution, Microsoft and Darktrace help bring together governance, compliance, behavioral understanding, and oversight in a unified approach to AI security. For organizations adopting Microsoft 365 E7, Darktrace / SECURE AI further strengthens that foundation by providing continuous visibility into AI activity, agent behavior, and emerging risk as AI adoption scales.

Continue reading
About the author
The Darktrace Community
Your data. Our AI.
Elevate your network security with Darktrace AI