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Evaluate the complete agent application—not just its model—before production. Test whether its tools, permissions, retrieved content, memory, approval steps, and runtime limits contain realistic attacks, then make release decisions from task-level evidence and the impact of any failures. A favorable model benchmark alone does not establish that an integrated agent is safe to deploy.
Define what is in scope
An agent’s security depends on what it can do and what it can access. Treat the deployed application as the unit of review: the model and prompts, orchestration, tools, credentials, retrieved material, memory, integrations, approval controls, logs, and runtime protections.
Map the system before testing it. Record its purpose and users, the sensitivity of the data it handles, the model and provider, prompt and policy versions, tool functions and credential scopes, retrieval sources, memory persistence and isolation, inter-agent connections, deployment environment, and actions that require approval. Mark where trusted instructions end and untrusted inputs begin. Those inputs may include webpages, files, emails, API responses, tool results, and messages from other agents.
Use that map to identify applicable threats. 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, multi-agent cascading failures, denial-of-wallet loops, sensitive-data exposure, and supply-chain risks. Not every risk applies to every system; connect each test to a capability or boundary the application actually has.
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Turn threats into testable abuse cases
For each case, write down the attacker’s capability, entry point, intended harmful action, asset at risk, expected denial or containment, and likely business impact if the attack succeeds. Include both direct attempts by a user and indirect instructions embedded in content the agent retrieves or receives from a tool.
Useful starting cases include:
- Instruction override: Try to make the agent ignore its intended task or reveal information it should protect, using both user instructions and untrusted external content.
- Unauthorized tool use or privilege escalation: Vary the requested action, arguments, user identity, permission scope, and call sequence. Check that an independent authorization layer blocks out-of-scope actions even if the model attempts them.
- Data leakage: Try to expose sensitive material through a response, a tool call, an external message, or logs. Specify what data the tested identity is and is not allowed to access.
- Memory poisoning: Test whether hostile or incorrect content can persist in memory, influence later tasks, or cross between users or sessions.
- Approval bypass: Attempt a high-impact action without approval, with an invalid approval, or with approval that does not match the action and its parameters.
- Runaway or chained behavior: Exercise recursive tool use, retries, and multi-agent handoffs to see whether limits contain repeated or escalating actions.
Add cases specific to the deployment. Examples include access to unauthorized database rows, overly broad cloud permissions, unsafe code execution, or sending a message that becomes visible outside the organization. Do not test destructive actions against customer data or live production systems; use isolated scenarios with controlled side effects.
Run a repeatable evaluation
- Freeze and document the configuration. Record the agent and model versions, provider, prompts and policies, tool and credential scopes, retrieval and memory settings, and relevant runtime controls. Without this record, a result cannot reliably be tied to the system that was tested.
- Check normal tasks and intended controls first. Confirm that representative, authorized work succeeds and that the designed approvals and access boundaries behave as expected. This gives you a baseline against which to interpret adversarial failures.
- Challenge the integrated application. Run the abuse cases against the actual configuration, covering model behavior, application integration, infrastructure, and runtime behavior—not only the model’s text response. Include single-turn and multi-turn scenarios. Observe attempted and completed tool actions, access decisions, approvals, timeouts, and circuit breakers.
- Vary attempts where repetition is realistic. One successful defense in one run does not establish that repeated attacks will fail. Where an attacker could cheaply retry, measure outcomes over repeated attempts and record the number of trials.
- Preserve reproducible evidence. Retain the case, configuration, expected outcome, observed outcome, attempt count, and any failure or containment details with the release record. Keep sensitive test data and side effects isolated.
Frameworks can help structure this work, but they are scaffolding rather than proof that a particular deployment is secure. NIST describes AgentDojo as a set of simulated environments—including Workspace, Travel, Slack, and Banking—with tools and hijacking scenarios; CAISI extended its evaluation with scenarios involving remote code execution, data exfiltration, and phishing. OWASP’s GenAI Red Teaming Guide covers testing across models, implementations, infrastructure, and runtime. NIST’s ARIA framework distinguishes model testing, red-teaming, and field testing as different kinds of evidence.
Report task-level outcomes, not just one score
For every case, record the tested configuration, attack and task, number of attempts, your definition of success or failure, observed tool actions, data accessed or exposed, approval or denial behavior, timeouts or circuit breakers, and severity. Report aggregate measures alongside individual outcomes: a single rate can obscure a rare but consequential failure.
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NIST Center for AI Standards and Innovation (CAISI) reported experiment-specific results from its AgentDojo-based evaluation: 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 experiment, average attack success was 57% on a single attempt and rose to 80% after 25 attempts. These are findings from that evaluation, not forecasts for another agent or universal security benchmarks.
Interpret frequency alongside impact. A low-frequency route to data exfiltration or code execution may warrant a stricter release decision than a more common, low-impact error. Set acceptance criteria according to the system’s capabilities, threat model, and possible harms; the cited official guidance does not establish a universal pass score or certification that guarantees a safe deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose evaluation methods for the evidence you need
Different methods exercise different parts of the system. They are complementary, not interchangeable pass/fail labels.
| Method | What it can show | Important limit |
|---|---|---|
| Model testing | How a model behaves on defined tests; useful early in development. | Does not by itself establish that application-level tool authorization or infrastructure controls work. |
| Red teaming | How an integrated system responds to adversarial misuse cases and high-risk interactions; can uncover failures not anticipated in a fixed test set. | Findings depend on scope, attacker effort, and the exact configuration tested. |
| Field testing | How the system behaves in a deployment context, where workflows and surrounding controls add realism. | Requires careful containment, monitoring, and control of real-world side effects. |
| Automated repeatable suites | Whether known cases regress across changes, including when tests are integrated into CI/CD. | Coverage is limited to represented scenarios and must evolve as the system and attack methods change. |
| Independent managed assessment | May add specialist testing and reporting capacity when an organization lacks internal capability. | Confirm the assessment’s scope, data handling, independence, and current availability before selection. |
When comparing methods or providers, ask whether they cover the model, implementation, infrastructure, and runtime; test tools and retrieval; support multi-turn and repeated attempts; provide task-level results; isolate risky scenarios; reproduce findings; fit the release workflow; explain data handling; and report residual risk clearly. OpenAI’s developer documentation describes red teaming as distinct from evals, names Promptfoo as an open-source framework, and separately discusses a managed enterprise red-teaming offering. Confirm current scope and availability directly before relying on any offering.
Best Value
Set a release gate and retest after material changes
Release only when the controls relevant to the agent’s high-risk capabilities are enforceable and test evidence supports the intended boundaries. In particular, the release decision should address whether:
- Tool permissions are narrowly scoped, with sensitive actions authorized outside model-generated reasoning.
- High-impact actions require a valid human approval bound to the specific action and its parameters.
- Untrusted external inputs are treated as data rather than trusted instructions.
- Memory is isolated, sanitized, and governed, and sensitive data is protected in both model context and logs.
- Recursion, retries, tool-chain depth, token use, and cost have limits that contain runaway behavior.
- Material failures have been remediated and retested, or residual risks have a named owner and compensating control.
Keep the evidence and any accepted residual-risk decisions with the release record. Rerun relevant regression cases when prompts, tools, memory, retrieval, policies, model providers, or credential scopes materially change; add cases for prior failures to the release workflow. As NIST CAISI technical staff put it in a January 17, 2025 technical blog, “Evaluations need to be adaptive. Even as new systems address previously known attacks, red teaming can reveal other weaknesses.”
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