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January 3, 2023

Analyst's Guide to the ActiveAI Security Platform

Understand Darktrace's full functionality in preventing and detecting cyber threats, and how analysts can benefit from Darktrace's AI technology.
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
Gabriel Hernandez
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03
Jan 2023

On countless occasions, Darktrace has observed cyber-attacks disrupting business operations by using a vulnerable internet-facing asset as a starting point for infection. Finding that one entry point could be all a threat actor needs to compromise an entire organization. With the objective to prevent such vulnerabilities from being exploited, Darktrace’s latest product family includes Attack Surface Management (ASM) to continuously monitor customer attack surfaces for risks, high-impact vulnerabilities and potential external threats. 

An attack surface is the sum of exposed and internet-facing assets and the associated risks a hacker can exploit to carry out a cyber-attack. Darktrace / Attack Surface Management uses AI to understand what external assets belong to an organization by searching beyond known servers, networks, and IPs across public data sources. 

This blog discusses how Darktrace / Attack Surface Management could combine with Darktrace / NETWORK to find potential vulnerabilities and subsequent exploitation within network traffic. In particular, this blog will investigate the assets of a large Australian company which operates in the environmental sciences industry.   

Introducing ASM

In order to understand the link between PREVENT and DETECT, the core features of ASM should first be showcased.

Figure 1: The PREVENT/ASM dashboard.

When facing the landing page, the UI highlights the number of registered assets identified (with zero prior deployment). The tool then organizes the information gathered online in an easily assessable manner. Analysts can see vulnerable assets according to groupings like ‘Misconfiguration’, ‘Social Media Threat’ and ‘Information Leak’ which shows the type of risk posed to said assets.

Figure 2: The Network tab identifies the external facing assets and their hierarchy in a graphical format.

The Network tab helps analysts to filter further to take more rapid action on the most vulnerable assets and interact with them to gather more information. The image below has been filtered by assets with the ‘highest scoring’ risk.

Figure 3: PREVENT/ASM showing a high scoring asset.

Interacting with the showcased asset selected above allows pivoting to the following page, this provides more granular information around risk metrics and the asset itself. This includes a more detailed description of what the vulnerabilities are, as well as general information about the endpoint including its location, URL, web status and technologies used.

  Figure 4: Asset pages for an external web page at risk.

Filtering does not end here. Within the Insights tab, analysts can use the search bar to craft personalized queries and narrow their focus to specific types of risk such as vulnerable software, open ports, or potential cybersquatting attempts from malicious actors impersonating company brands. Likewise, filters can be made for assets that may be running software at risk from a new CVE. 

Figure 5: Insights page with custom queries to search for assets at risk of Log4J exploitation.

For each of the entries that can be read on the left-hand side, a query that could resemble the one on the top right exists. This allows users to locate specific findings beyond those risks that are categorized as critical. These broader searches can range from viewing the inventory as a whole, to seeing exposed APIs, expiring certificates, or potential shadow IT. Queries will return a list with all the assets matching the given criteria, and users can then explore them further by viewing the asset page as seen in Figure 4.

Compromise Scenario

Now that a basic explanation of PREVENT/ASM has been given, this scenario will continue to look at the Australian customer but show how Darktrace can follow a potential compromise of an at-risk ASM asset into the network. 

Having certain ports open could make it particularly easy for an attacker to access an internet-facing asset, particularly those sensitive ones such as 3389 (RDP), 445 (SMB), 135 (RPC Epmapper). Alternatively, a vulnerable program with a well-known exploitation could also aid the task for threat actors.

In this specific case, PREVENT/ASM identified multiple external assets that belonged to the customer with port 3389 open. One of these assets can be labelled as ‘Server A'. Whilst RDP connections can be protected with a password for a given user, if those were weak to bruteforce, it could be an easy task for an attacker to establish an admin session remotely to the victim machine.

Figure 6: Insights tab query filtering for open RDP port 3389.

N or zero-day vulnerabilities associated with the protocol could also be exploited; for example, CVE-2019-0708 exploits an RCE vulnerability in Remote Desktop where an unauthenticated attacker connects to the target system using RDP and sends specially crafted requests. This vulnerability is pre-authentication and requires no user interaction. 

Certain protocols are known to be sensitive according to the control they provide on a destination machine. These are developed for administrative purposes but have the potential to ease an attacker’s job if accessible. Thanks to PREVENT/ASM, security teams can anticipate such activity by having visibility over those assets that could be vulnerable. If this RDP were successfully exploited, DETECT/Network would then highlight the unusual activity performed by the compromised device as the attacker moved through the kill chain.  

There are several models within Darktrace which monitor for risks against internet facing assets. For example, ‘Server A’ which had an open 3389 port on ASM registered the following model breach in the network:

Figure 7: Breach log showing Anomalous Server Activity / New Internet Facing System model for ‘Server A’.

A model like this could highlight a misconfiguration that has caused an internal device to become unexpectedly open to the internet. It could also suggest a compromised device that has now been opened to the internet to allow further exploitation. If the result of a sudden change, such an asset would also be detected by ASM and highlighted within the ‘New Assets’ part of the Insights page. Ultimately this connection was not malicious, however it shows the ability for security teams to track between PREVENT to DETECT and verify an initial compromise.  

A mock scenario can take this further. Using the continued example of an open port 3389 intrusion, new RDP cookies may be registered (perhaps even administrative). This could enable further lateral movement and eventual privilege escalation. Various DETECT models would highlight actions of this nature, two examples are below:

Figure 8: RDP Lateral Movement related model breaches on customer.

Alongside efforts to move laterally, Darktrace may find attempts at reconnaissance or C2 communication from compromised internet facing devices by looking at Darktrace DETECT model breaches including ‘Network Scan’, ‘SMB Scanning’ and ‘Active Directory Reconnaissance’. In this case the network also saw repeated failed internal connections followed by the ‘LDAP Brute-Force Activity model’ around the same time as the RDP activity. Had this been malicious, DETECT would then continue to provide visibility into the C2 and eventual malware deployment stages. 

With the combined visibility of both tools, Darktrace users have support for greater triage across the whole kill chain. For customers also using RESPOND, actions will be taken from the DETECT alerting to subsequently block malicious activity. In doing so, inputs will have fed across the whole Cyber AI Loop by having learnt from PREVENT, DETECT and RESPOND.

This feed from the Cyber AI Loop works both ways. In Figure 9, below, a DETECT model breach shows a customer alert from an internet facing device: 

Figure 9: Model breach on internet-facing server.

This breach took place because an established server suddenly started serving HTTP sessions on a port commonly used for HTTPS (secure) connections. This could be an indicator that a criminal may have gained control of the device and set it to listen on the given port and enable direct connection to the attacker’s machine or command and control server. This device can be viewed by an analyst in its Darktrace PREVENT version, where new metrics can be observed from a perspective outside of the network.

Figure 10: Assets page for server. PREVENT shows few risks for this asset. 

This page reports the associated risks that could be leveraged by malicious actors. In this case, the events are not correlated, but in the event of an attack, this backwards pivoting could help to pinpoint a weak link in the chain and show what allowed the attacker into the network. In doing so this supports the remediation and recovery process. More importantly though, it allows organizations to be proactive and take appropriate security measures required before it could ever be exploited.

Concluding Thoughts

The combination of Darktrace / Attack Surface Management with Darktrace / NETWORK provides wide and in-depth visibility over a company’s infrastructure. Through the Darktrace platform, this coverage is continually learning and updating based on inputs from both. ASM can show companies the potential weaknesses that a cybercriminal could take advantage of. In turn this allows them to prioritize patching, updating, and management of their internet facing assets. At the same time, Darktrace will show the anomalous behavior of any of these internet facing devices, enabling security teams or respond to stop an attack. Use of these tools by an analyst together is effective in gaining informed security data which can be fed back to IT management. Leveraging this allows normal company operations to be performed without the worry of cyber disruption.

Credit to: Emma Foulger, Senior Cyber Analyst at Darktrace

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
Gabriel Hernandez

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August 7, 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.

This is the approach Darktrace has taken for years: learning what is normal across an organization’s digital environment, identifying meaningful deviations, and responding to emerging threats without relying on known attack signatures. As autonomous AI agents introduce new and unpredictable behaviors, that foundation becomes increasingly important to securing the enterprise.

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
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