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November 5, 2020

Mimecast Link Rewriting: A False Sense of Security Exposed

Gain insight into modern email security methods to ensure you avoid pitfalls of traditional email gateways. Learn why rewriting links isn't the best approach.
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
Dan Fein
VP, Product
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05
Nov 2020

Many organizations feel secure in the knowledge that their email gateway is rewriting all of the harmful links targeting their employees. Link rewriting is a common technique that involves encoding URLs sent via email into a link that redirects the user to the gateway’s own servers. These servers contain some unique codes that then track the user and perform later checks to determine whether the link is malicious.

This blog reveals why the sense of protection this gives is a fallacy, and how rewriting links does not equate to protecting the end user from actual harm. In fact, gateways’ reliance on this technique is actually an indicator of one of their fundamental flaws: their reliance on rules and signatures of previously recognized threats, and their consequent inability to stop threats on the first encounter. The reason these tools pre-emptively rewrite links is so they can make a determination later on: with the link now pointing to their own servers, they can leverage their updated assessment of that link and block a malicious site, once more information has become available (often once ‘patient zero’ has become infected; and the damage is already done).

Email security that recognizes and blocks threats on the first encounter has no need to rewrite every link.

How to measure success

If the sheer number of links rewritten is to be our measure of success, then traditional gateways win every time. For instance, Mimecast will usually rewrite 100% of the harmful links that Antigena Email locks. In fact, it rewrites nearly 100% of all links. That even includes links pointing to trusted websites like LinkedIn and Twitter, and even emails containing links to the recipient’s own website. So when tim.cook[at]apple.com receives a link to apple.com, for example, ‘mimecast.com’ will still dominate the URL.

Some organizations suffering from low first-encounter catch rates with their gateways have responded by increasing employee education: training the human to spot the giveaways of a phishing email. With email attacks getting more targeted and sophisticated, humans should never be considered the last line of defense, and rewriting links makes the situation even worse. If you’re training your users to watch which links they’re clicking, and every one of those links reads ‘mimecast.com’, how are your users supposed to learn what’s good, bad, or sketchy when every URL looks the same?

Moreover, when Mimecast’s URL gateway is down, these rewritten links don’t work (and the same applies to protected attachments). This results in business downtime which is intolerable for businesses in these critical and challenging times.

We can see the effect of blanket rewriting through Darktrace’s user interface, which shows us the frequency of rewritten links over time. Looking back over three days, this particular customer – who was trialing Antigena Email alongside Mimecast, received 155,008 emails containing rewritten links. Of those, 1,478 were anomalous, and Darktrace’s AI acted to immediately lock those links, protecting even the first recipient from harm. The remaining 153,530 links were all unnecessarily rewritten.

Figure 1: Over 155,000 inbound emails contained rewritten Mimecast links

If it comes to actually stopping the threat when a user goes to click that rewritten link, gateway tools fail. Their reliance on legacy checks like reputation, deny-lists, and rules and signatures mean that malicious content will sometimes sit for days or weeks without any meaningful action, as the technology requires at least one – and usually many – ‘patient zeros’ before determining a URL or an attachment as malicious, and updating their deny-lists.

Let’s look at the case of an attack launched from entirely new infrastructure: from a freshly purchased domain, and containing a newly created malicious payload. None of the typical metrics legacy tools search for appear as malicious, and so of course, the threat gets through, and ‘patient zero’ is infected.

Figure 2: ‘Patient Zero’ denotes the first victim of an email attack.

It inevitably takes time for the malicious link to be recognized as malicious, and for that to be reported. By this point, large swathes of the workforce have also become infected. We can call this the ‘time to detection’.

Figure 3: The time to detection

As legacy tools then update their lists in recognition of the attack, the malware continues to infect the organization, with more users engaging in the contents of the email.

Figure 4: The legacy tool reacts

Finally, the legacy tool reacts, updating its deny-list and providing substantive action to protect the end user from harm. By this point, hundreds of users across multiple organizations may have interacted with the links in some way.

Figure 5: Many ‘patient zeros’ are required before the threat is deny-listed

Email gateways’ reliance on rewriting links is directly related to their legacy approach to detection. They do it so that later down the line, when they have updated information about a potential attack, they can take action. Until then, it’s just a rewritten link, and if clicked on, it will bring the user to whatever website was hiding underneath it.

These links are also rewritten in an attempt to grasp an understanding of what user network behavior looks like. But far from giving an accurate or in-depth picture of network activity, this method barely scratches the surface of the wider behaviors of users across the organization.

Alongside Darktrace’s Enterprise Immune System, Antigena Email can pull these insights directly from a unified, central AI engine that has complete and direct visibility over an organization’s entire digital estate – not just links accessed from emails, but network activity as a whole – and not a makeshift version where it is assumed people only visit links through emails. It also pulls insights from user behavior in the cloud and across SaaS applications – from Salesforce to Microsoft Teams.

Taking real action in real time

While gateways rewrite everything in order to leave the door open to make assessments later on, Darktrace is able to take action when it needs to – before the email poses a threat in the inbox. The technology is uniquely able to do this due to its high success rates for malicious emails seen on first encounter. And it’s able to achieve such high success rates because it takes a much more sophisticated approach to detection that uses AI to catch a threat – regardless of whether or not that threat has been seen before.

Darktrace’s understanding of ‘normal’ for the human behind email communications allows it to not only detect subtle deviations that are indicative of a cyber-threat, but respond to that threat at the point of delivery. This response is targeted, proportionate and non-disruptive, and varies according to the nature of the attack. While Darktrace’s unsupervised machine learning can accurately identify deviations from ‘normal’, its supervised machine learning models are able to classify the intention behind the email; what the attacker is trying to do (extort information, solicit a payment, harvest credentials, or convince the user to download a malicious attachment).

Crucially, organizations trialing both approaches to security find that Antigena Email consistently identifies threats that Mimecast and other tools miss. With the scale and sophistication of email attacks growing, the need for a proactive and modern approach to email security is paramount. Organizations need to ensure they are measuring their sense of protection with the right yardstick, and adopt a technology that can take meaningful action before damage is done.

Trial Antigena Email today

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
Dan Fein
VP, Product

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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.

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Carlo Loregian
Solutions Engineer

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August 4, 2026

Extending AI Security Visibility with Darktrace and Microsoft Agent 365

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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.

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