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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsAn AI agent can be hijacked through ordinary content it is asked to process: a web page, email, or file can contain instructions intended to redirect the agent from the user’s task. The risk is not just that the agent reads hostile text. It is that the agent may be able to act through tools, credentials, persistent context, or delegated access. To detect this kind of attack, look for a connection between untrusted content and an unexpected action—such as an unauthorized data transfer, tool call, privilege use, or code execution.
How can an AI agent be hijacked?
NIST’s Center for AI Standards and Innovation (CAISI) describes agent hijacking as a form of indirect prompt injection: an attacker places malicious instructions in data an agent may ingest, hoping to make it take unintended, harmful actions. The agent might encounter those instructions while performing a legitimate task; the attacker does not need to begin with a direct command to the agent.
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As NIST CAISI technical staff put it in a post published January 17, 2025, and updated December 19, 2025: “Currently, many AI agents are vulnerable to agent hijacking, a type of indirect prompt injection in which an attacker inserts malicious instructions into data that may be ingested by an AI agent, causing it to take unintended, harmful actions.” NIST gives attempted data exfiltration and downloading and running malicious code as examples.
“Agent-shaped attacks” is a plain-language way to describe this risk, not a single official technical category. The attack surface depends on the system: an agent with no ability to send messages, access private data, or change files has fewer opportunities to cause those particular harms. A deployment that uses persistent memory, multiple tools, or delegated permissions has different risks. Those capabilities should be assessed rather than assumed.
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Why an agent’s tools change the risk
A language model can be influenced by instructions it encounters. An agent connects that behavior to capabilities: it may retrieve data, call an application, write a file, or trigger another agent. The core security question is therefore not only “Did the model follow hostile text?” but also “What could it do next, with whose authority, and what checks stood between the instruction and the action?”
What kinds of agent attacks should you distinguish?
Prompt injection is one route to unsafe behavior, but it is not the whole threat model. OWASP’s agent-security guidance covers several related risks. They describe different failure paths, so a test that checks only for hostile text in a prompt will miss important ways an agent can be misused.
| Risk | What to investigate |
|---|---|
| Indirect prompt injection or goal hijacking | Whether instructions in retrieved or supplied content divert the agent from the user’s authorized task. |
| Tool abuse or privilege escalation | Whether the agent calls a tool, uses a credential, or reaches a resource beyond what the task requires. |
| Data exfiltration | Whether the agent exposes information to an unauthorized destination or audience. |
| Memory poisoning | Whether untrusted content is retained and later influences behavior, including across sessions or users. |
| Excessive autonomy and cascading failures | Whether an agent can repeat consequential actions or pass unsafe output to another agent without an independent check. |
For systems using the Model Context Protocol (MCP), OWASP separately identifies issues including tool poisoning, supply-chain attacks, command injection, prompt injection through contextual payloads, and missing audit or telemetry. These are MCP-specific concerns; not every agent uses MCP. OWASP labels its MCP Top 10 a beta, living document, so its categories may change.
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Look for a mismatch between the user’s task, the content the agent encountered, and the action it took. A suspicious event is an indicator to investigate, not proof by itself: a legitimate task can sometimes require an unusual tool call, and there is no universal signature that identifies every agent hijack.
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- Unexpected tool or parameters: The agent selects a tool the task does not call for, or uses unusual recipients, destinations, file paths, scopes, or arguments.
- Access or transmission beyond the task: It reads information unrelated to the request, or attempts to send data to an unapproved location or person.
- Unexpected downloads or code execution: It retrieves or runs code when the user’s task did not authorize that behavior.
- Unauthorized privilege use: It uses administrative or delegated authority that is unnecessary for the work.
- Persistence or cross-session influence: Content from one task appears to change later behavior, especially for another user or session that should be isolated.
- Unsafe action chains: A tool result prompts a second consequential action, or one agent’s output triggers another agent, without the expected review or authorization.
Trace the path from content to action
When investigating an alert, reconstruct the sequence rather than treating a tool call as an isolated event. Establish what the user authorized, what external or tool-provided content the agent received, which instructions or policies were active, what tools and credentials were available, what decision the agent made, and whether an approval or policy check intervened. This helps distinguish an injection attempt from an unrelated error and shows where a control did—or did not—limit its effects.
OWASP’s MCP Top 10 identifies missing audit and telemetry as a risk. That supports keeping enough records to reconstruct relevant tool and authorization decisions; it does not prescribe a complete logging standard. Choose records appropriate to the system and protect them as sensitive data, since they may contain user content, credentials metadata, or details of internal operations.
How do you test an AI agent for prompt injection?
Test the integrated agent in conditions that reflect how it will actually be used. A model-only check cannot establish how the deployed system behaves with its tools, permissions, memory, approval rules, and operating context. Include benign-looking web pages, emails, files, and tool outputs that attempt to redirect the task, while keeping the test environment and data controlled.
- Define the authorized task and boundaries. Specify what information the agent may access, which actions it may take, and which actions require approval.
- Place adversarial instructions in content the agent is meant to process. Test the relevant input routes, such as retrieved pages, email, files, or contextual tool output—not only direct user prompts.
- Observe attempted actions as well as completed ones. Record tool selection and arguments, access attempts, outbound transfers, code execution, authorization decisions, and whether a human gate stopped a consequential action.
- Test the complete workflow. Include the real tool policy, credentials, memory behavior, and any agent-to-agent handoffs that are part of the deployment.
- Repeat and vary the attacks. Change the injected content and task context, and consider repeated attempts if an attacker could retry in production.
- Turn failures into regression tests. Preserve safe test cases for known injection, memory, and tool-abuse failures, then rerun them after changes to prompts, models, tools, policies, or memory.
What NIST’s results do—and do not—show
In a 2026 public red-teaming competition spanning tool-use, coding, and computer-use scenarios, NIST CAISI reported more than 250,000 attack attempts, over 400 participants, and 13 frontier models. The competition found at least one successful attack against every target model. This is evidence that the tested models were vulnerable in that competition; it is not a real-world compromise rate for all agents or deployments.
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In a separate 2025 evaluation, CAISI attempted five injection tasks 25 times each and reported that average attack success rose from 57% to 80% after repeated attempts. Those figures describe that evaluation’s setup, not the probability that an arbitrary agent will be compromised. They do show why a single attempt can understate exposure when an attacker can try again.
NIST’s findings support evaluating with current attack scenarios, adapting attacks to the system under test, examining task-level results rather than only an aggregate score, and considering repeated attempts. When comparing evaluations, check whether they cover the relevant attack types, tasks, and integrated tools; whether they report per-task outcomes; and whether they test one attempt or repeated attempts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which controls reduce the chance and impact of an agent-shaped attack?
Detection is more useful when the agent’s authority is limited and consequential actions have independent checks. OWASP’s agent-security guidance supports the following controls; none should be treated as a guarantee that an attack will be prevented.
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- Limit tool authority. Give the agent only the tools and resource scope needed for its task. Separate read and write permissions where practical.
- Treat retrieved and tool-provided content as untrusted input. Validate and sanitize external inputs. Do not grant instructions embedded in that content the authority of the user’s authorized goal.
- Gate consequential actions. Require human review or an independent check before high-impact, irreversible, financial, administrative, or externally visible actions.
- Isolate context and memory. Prevent untrusted content from one user or session from silently influencing another.
- Monitor and test the full agent behavior. Include tool policies, approvals, credentials, and repeatable adversarial cases in evaluation. Rerun tests when relevant system components change.
- Bound action loops and handoffs. Set limits on repeated actions and require checks before unsafe output can propagate between agents or trigger further consequential steps.
When assessing an agent deployment, compare its permission scope, treatment of external content and context, approval gates, audit visibility, and ability to run repeatable adversarial regression tests. This is a practical way to compare controls, not a vendor ranking.
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