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Evaluate the complete deployed agent—not just its model—before production. Test how prompts, orchestration, tools, permissions, retrieved content, memory, approvals, integrations, and runtime limits behave together, using repeatable abuse cases tied to the harm they could cause. Release only when high-impact actions are independently controlled, serious failures are fixed or explicitly accepted, and the tested evidence is retained.
What makes an AI agent a security risk?
An agent can do more than produce an unsafe answer: it may call tools, retrieve private information, change records, send messages, run code, or delegate work to other agents. Its security therefore depends on the paths from inputs to actions, not only on whether its model follows a safety instruction.
OWASP’s AI Agent Security Cheat Sheet identifies risks including direct and indirect prompt injection, tool abuse and privilege escalation, data exfiltration, memory poisoning, goal hijacking, excessive autonomy, approval manipulation, cascading failures across agents, denial-of-wallet loops, sensitive-data exposure, and supply-chain risks. Not every system has every exposure. Map these threats to the capabilities and data flows in the application being reviewed.
Map the system and its trust boundaries
Document the agent’s purpose and users, data classification, model and provider, prompts and policies, orchestrator, tools, credentials and scopes, retrieval sources, memory persistence and isolation, inter-agent links, approval controls, outputs, logs, and deployment environment. Mark where trusted instructions meet untrusted content.
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Include every external source the agent consumes: webpages, files, emails, tool results, API responses, and messages from peer agents. Any of these can carry instructions that attempt to redirect the agent or elicit protected data.
How should you design security tests?
Write abuse cases around what an attacker can do and what the system must prevent. For each case, record the attacker capability, entry point, harmful action, protected asset, expected denial or containment, and likely business impact if it succeeds. Test direct user manipulation as well as indirect instructions embedded in retrieved or tool-returned data.
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Build cases around real capabilities
Use the following as a starting set, then add cases for the application’s particular tools, data, and consequences:
- Instruction override: Try to make the agent disregard its intended task or reveal protected instructions or data.
- Unauthorized tool use or privilege escalation: Vary tool arguments, identities, scopes, and call sequences to probe whether the agent can reach records or actions outside the user’s authorization.
- Indirect prompt injection and data leakage: Put hostile instructions in content the agent retrieves or receives from a tool; observe whether it follows them or exposes sensitive context.
- Memory poisoning: Attempt to plant content that changes later behavior, crosses user or session boundaries, or persists beyond its intended lifetime.
- Approval bypass: Test whether a high-impact action can proceed without valid approval, or whether an approval can be reused for different action parameters.
- Recursive tool abuse: Probe whether repeated calls, retries, or delegation can cause runaway work, resource exhaustion, or uncontrolled cost.
- Multi-agent boundary crossing: Test whether one agent can pass untrusted instructions or data to another in a way that bypasses the second agent’s controls.
Adapt the cases to actual exposure: for example, unauthorized database rows, broad cloud permissions, unsafe code execution, or externally visible communications matter when the application has those capabilities. A model’s refusal is not proof that the application is secure; independently verify that authorization controls reject an out-of-scope action even if the model attempts it.
What evaluation sequence gives useful evidence?
- Freeze and record the test configuration. Identify the model and provider, prompt and policy versions, tool and credential scopes, retrieval and memory settings, and relevant deployment configuration. Use an isolated environment without customer data or production side effects.
- Check normal behavior first. Run intended tasks and confirm that designed controls—such as permission checks, approval steps, and limits—work under expected conditions. This gives you a baseline for interpreting adversarial results.
- Run adversarial scenarios across the integrated system. Test the model’s behavior, application integration, infrastructure, and runtime controls. Include single-turn and multi-turn attempts, and test repeated attempts when retries are cheap or the deployed environment makes repetition plausible.
- Observe actions and boundaries, not just answers. Record tool calls, identity and scope used, data accessed or exposed, approvals and denials, timeouts, and whether a circuit breaker stopped excessive activity. Check access-control enforcement outside model-generated reasoning.
- Assess impact and set the release decision. Pair case-level findings with aggregate measures. Remediate and retest serious failures; document accepted residual risks with an owner and a compensating control.
Frameworks and benchmarks can supply scenarios, but they do not replace tests of the actual configuration. NIST describes AgentDojo as a set of simulated environments—Workspace, Travel, Slack, and Banking—with tools and hijacking scenarios; the Center for AI Standards and Innovation (CAISI) extended its suite with scenarios involving remote code execution, data exfiltration, and phishing. NIST’s ARIA framework distinguishes model testing, red-teaming, and field testing as different kinds of evaluation evidence.
How should you interpret attack results?
Keep task-specific outcomes alongside any overall success rate. Averages can conceal a serious failure on one sensitive task, and a low-frequency path to data exfiltration or code execution may carry more risk than a frequent, low-impact behavior.
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In a 2025 CAISI AgentDojo-based experiment, the strongest newly developed attack achieved an 81% success rate, compared with 11% for the strongest baseline attack in that setting. Across five injection tasks in the same reported evaluation, average attack success was 57% after one attempt and rose to 80% after 25 attempts. These are results from that experiment, not forecasts or pass thresholds for another agent. They illustrate why evaluations should consider attack strength, the task being tested, and the number of attempts.
As CAISI technical staff put it in a NIST technical blog published January 17, 2025: “Evaluations need to be adaptive. Even as new systems address previously known attacks, red teaming can reveal other weaknesses.”
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For each run, record the tested agent and model version, provider, prompt and policy versions, tool and credential scopes, retrieval and memory configuration, attack case and task, number of attempts, success definition, observed tool actions, data accessed or exposed, approval and denial behavior, timeouts or circuit-breaker results, and severity or impact. Preserve the expected result as well as what actually happened so later runs can be compared.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which evaluation methods should you use?
These methods answer different questions; none is a universal pass label. Select them by the coverage and evidence your risk decision requires.
| Method | What it exercises | Strength and limitation |
|---|---|---|
| Model testing | Model-level behavior under defined tests | Useful early, but does not establish that application permissions or tool authorization are secure. |
| Red teaming | Adversarial misuse cases and high-risk interactions in an integrated system | Can uncover novel failures; results depend on scope, attacker effort, and the exact configuration tested. |
| Field testing | Agent behavior in a deployment context | Adds contextual realism, but requires careful controls and monitoring. |
| Automated repeatable suites | Represented scenarios run consistently, often for regression workflows | Support CI/CD and comparison across changes; miss risks not covered by their scenarios and require ongoing updates. |
| Independent managed assessment | Specialist testing and reporting within the agreed assessment scope | Can add testing capacity; confirm the provider’s scope, data handling, independence, and current availability. |
When comparing methods or providers, check system coverage across model, implementation, infrastructure, and runtime; support for tools and retrieval; multi-turn and repeated attempts; task-level reporting; safe isolation; reproducibility; release-workflow integration; data handling; and clarity about residual risk.
What should the production release gate require?
Set criteria for the agent’s actual use, threat model, capabilities, and potential harms. The official OWASP and NIST materials described here do not establish a universal numeric pass score or a certification that guarantees safe deployment. A practical release gate should require evidence that:
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- High-impact actions require valid human approval bound to the action and its parameters.
- External content is treated as untrusted data rather than trusted instruction.
- Memory is isolated, sanitized, and governed for its intended use and lifetime.
- Sensitive data is protected in agent context and logs.
- Tool-chain depth, retries, token use, and cost have enforceable limits.
- Material failures have been remediated and retested, or remaining risks have an identified owner and compensating control.
Keep the test evidence with the release record. Put regression cases for previous failures into CI/CD, and rerun relevant tests when prompts, tools, memory, retrieval, policies, model provider, or credential scope materially change. OWASP’s AI Agent Security Cheat Sheet likewise calls for structured security testing before production and after material changes to these components.
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