Test an AI agent as a complete application, not just as a model prompt. A useful security assessment checks whether the agent, its tools, authorization controls, retrieved content, memory, orchestration, and any delegated agents resist malicious inputs and prevent unauthorized actions. Run adversarial tests before deployment, repeat them after material changes, and verify important permissions outside the agent itself.
What is AI agent security testing?
AI agent security testing assesses whether an agent application handles malicious or unexpected inputs safely while it reasons, retrieves information, calls tools, stores state, and coordinates with other agents. It combines conventional application security testing with agent-specific tests such as indirect prompt injection, unauthorized tool invocation, memory poisoning, and abuse of delegation chains.
The security boundary is the whole application. A model may produce a safe answer while a tool call, retrieval layer, orchestration rule, or stored memory creates risk. Conversely, an agent’s refusal is not a substitute for an independent access-control check. Test the system’s behavior and the controls around it.
What should an AI agent red team include?
Map the components and trust boundaries that can affect a task. Include user input, retrieved documents, tool outputs, persistent memory, orchestration, APIs, and messages between agents. Treat external content as untrusted even when it arrives through an ordinary email, file, web page, or tool response.
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- Agent and orchestrator: prompts, routing, retries, handoffs, task completion, and stopping behavior.
- Tools and APIs: available functions, credentials, scope, input validation, and server-side authorization.
- Retrieval and data: source permissions, document handling, citations, and whether results are restricted to the current user.
- Memory and state: what persists between tasks, who can alter it, and whether untrusted content can influence later behavior.
- Delegation: what context and authority pass to another agent, and whether the receiving agent can be induced to cross its own trust boundary.
- Operational controls: approval gates, timeouts, token or cost limits, circuit breakers, logs, and failure handling.
How do you test an AI agent for security?
- Set objectives and scope. Identify the deployment and use cases under test, the harms to prevent, and any systems or data that are out of scope.
- Record the configuration. Capture the model provider and version, prompts, tools, tool policy, permissions, retrieval setup, memory behavior, orchestration, and relevant deployment controls. Use a production-representative configuration where possible.
- Map threats and trust boundaries. Trace how user input, retrieved material, tool output, stored state, and inter-agent messages can influence decisions or actions.
- Build abuse cases with expected outcomes. For each scenario, specify the attacker’s objective, the control expected to stop it, and the evidence that would count as success or failure.
- Establish baseline behavior. Check normal task completion and expected control behavior before adversarial testing. This helps distinguish a security control from a system that simply cannot perform its ordinary task.
- Run tests through the real application controls. Exercise the agent workflow, then test authorization and tool-call enforcement independently rather than relying on what the agent says it will do.
- Record and prioritize findings. Document what happened, the affected assets and users, the attacker’s achieved objective, and the likely harm.
- Remediate and validate. Change the relevant control, rerun the failed case, and keep it as a regression test. Expand cases as new attack patterns or system changes warrant.
Which attack cases belong in the test suite?
Use a repeatable abuse-case matrix. The examples below are test objectives, not a claim that any particular agent has these weaknesses.
| Risk | What to test | Expected control behavior |
|---|---|---|
| Instruction override | Give the agent conflicting user or retrieved instructions that attempt to override policy, including across multiple turns. | The agent should not treat lower-trust content as authority to disregard policy or perform a prohibited action. |
| Unauthorized tool use | Ask for actions outside the user’s permissions, including calls to privileged tools or access to credentials. | Independent authorization controls reject the action even if the agent attempts it. |
| Retrieval access failure | Attempt to retrieve records belonging to another user or role. | The retrieval or data-access layer returns only records that the current user is authorized to access. |
| Memory poisoning | Introduce malicious or misleading content that could persist and influence a later task. | Untrusted information cannot silently become trusted instructions or durable authority. |
| Sensitive-data exposure | Try to extract private information through final responses, citations, tool results, or logs. | Data access and output controls prevent disclosure to an unauthorized party. |
| Runaway behavior | Trigger repeated retries, loops, or excessive tool use; test token, cost, and chain limits. | Configured limits halt or constrain the behavior and generate useful evidence. |
| Approval bypass | Attempt a high-impact action without valid approval or with an invalid, stale, or mismatched approval. | The action remains blocked until the required independent approval is satisfied. |
| Delegation-chain abuse | Test whether malicious instructions or excess authority can pass from one agent to another. | Each agent and handoff respects its defined scope and trust boundary. |
| Failure and workflow abuse | Exercise tool errors, partial task completion, unexpected orchestration, context-window saturation, and attempts to bypass business logic. | Failures do not silently grant access or trigger an unsafe action; workflow and stopping controls behave as designed. |
OWASP’s AI Security Testing Guide also calls out testing whether agents halt when instructed, avoid unbounded autonomy and looping, refrain from misusing tools or permissions, and cannot bypass workflow or business logic. Include conventional application vulnerabilities where they intersect with the agent’s tools and workflow.
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How do you test for prompt injection and tool misuse?
Test direct attempts from the user and indirect attempts embedded in content the agent consumes. An indirect prompt injection places malicious instructions in data—such as a document or tool response—that the agent may ingest. The agent can encounter that content after beginning a legitimate task, so test the full workflow rather than only a clean, single-turn prompt.
For each input surface, vary the source and timing of the instruction: user messages, retrieved passages, files, emails, web content, tool outputs, and inter-agent messages. Include single-turn and multi-turn attempts. Check whether the agent follows the hostile instruction, whether it attempts a prohibited tool call, and whether any external control blocks the action.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should results be measured?
Report results at the level of the attack and task, not only as a single aggregate score. For each case, record the tested configuration, the attack objective, the number and nature of attempts, whether the objective was achieved, and the severity of the resulting harm. Include task-level outcomes alongside any aggregate measure. Because agent behavior can vary, repeated attempts can reveal failures a single run misses.
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A NIST Center for AI Standards and Innovation (CAISI) technical blog, published January 17, 2025 and updated December 19, 2025, describes experiments in simulated Workspace, Travel, Slack, and Banking settings. In its held-out Workspace tasks, the strongest newly developed red-team attack achieved an 81% success rate, compared with 11% for the strongest baseline attack. These figures apply to that specific AgentDojo experiment and its documented model setup; they are not a current cross-vendor comparison or a general rate for AI agents. NIST’s example illustrates why evaluations should adapt to new systems, assess task-specific risk, and consider multiple attempts.
Do not treat a benchmark score as a guarantee for a different model, set of tools, permission structure, or deployment. OWASP’s AI Testing Guide states: “At present, prompt injection issues can be mitigated but not completely prevented in systems based on LLMs.” That qualification makes layered controls and ongoing validation important; it does not mean testing is futile.
When should an AI agent be security tested?
Run structured adversarial testing before production and repeat relevant tests after material changes to prompts, tools, memory, retrieval, policies, model providers, or deployment configuration. Rerun regression cases for known failures after fixes, and update the suite as new attack patterns emerge. A test result describes the configuration and cases exercised; it does not establish lasting safety after the system changes.
What should the security report retain?
Keep enough detail for another reviewer to understand what was tested, reproduce relevant cases, and assess residual risk. A useful record includes:
Quick Recap
- Agent and model versions or provider details, deployment configuration, tool policy, permissions, and retrieval and memory setup.
- Test objectives, abuse cases, expected outcomes, and the layers and threats covered or excluded.
- Observed actions and outcomes, including approvals, denials, timeouts, and circuit-breaker behavior.
- Findings, severity and impact, remediation, fix-validation results, and regression cases.
- Residual risks, compensating controls, and any limitations that affect the release decision.
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