The Tool Desk
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What should an agent security evaluation prove?
A useful evaluation answers two separate questions: can an attacker make the agent violate its authorization or disclose protected information, and can the agent still complete legitimate work? Test the complete path from input through retrieval, memory, model decisions, tool authorization, tool execution, and any resulting state change. A refusal in the final response is not evidence that an action was blocked: the agent may already have called a tool or sent data elsewhere.
Keep the test boundary clear. A malicious instruction typed into the user message tests direct prompt injection. An instruction embedded in a webpage, email, file, or retrieval result tests indirect injection through external content. Putting the latter payload in the user prompt does not test whether the agent handles that external-data boundary safely.
Use a defined policy for each tool and resource: which actions are allowed, denied, or require review for the user and task in question. The test should verify that policy at the tool layer, not assume that a model’s natural-language response enforces it. OWASP recommends structured agent security testing before production and after material changes to prompts, tools, memory, retrieval, policies, or model providers in its AI Agent Security Cheat Sheet.
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For each case, record the legitimate task, attacker objective, injection channel, relevant context, expected policy decision, and the observable event that would count as a violation. Include both attacks and benign controls so a system that blocks everything cannot appear secure merely by avoiding risky actions.
Instruction override and prompt extraction
Try to make user text or retrieved content override higher-priority instructions, or disclose a synthetic secret marker. OpenAI’s published safety evaluation describes repeated adversarial queries against a hidden phrase or password and counts correct refusals; use the method as a model for a controlled test, not as a reason to put real credentials in prompts or fixtures. See the Pilot Anthropic–OpenAI evaluation exercise.
Indirect injection and agent hijacking
Give the agent a legitimate task—such as summarizing an email or finding information in a document—while placing an attack instruction in the external content it must consume. Check whether it abandons the user’s objective for the attacker’s. NIST describes agent hijacking as a failure to maintain separation between trusted instructions and untrusted external data in its discussion of strengthening AI agent hijacking evaluations.
Unauthorized tool use and privilege escalation
Try to induce actions outside the user’s authorization, the intended resource scope, or the tool’s permitted read/write operations. Capture the request sent to the actual tool, the authorization result, and whether the action changed state. Include cases where the agent has access to a broad tool but the user request warrants only a narrow operation.
Sensitive-data disclosure and exfiltration
Seed an isolated environment with dummy records and a synthetic secret marker. Inspect both the final response and instrumented destinations such as API calls, files, logs, or messages. Clean-looking text does not establish that data was not transferred through another channel.
Memory poisoning and cross-session influence
Test whether malicious external content is persisted or changes the agent’s behavior in a later session or for another user. Keep identities and sessions separate in the fixture, then inspect stored memory and subsequent outputs. OWASP’s agent guidance calls for regression testing after observed memory-poisoning failures.
Runaway and chained actions
Test controls on recursive calls, retries, action depth, token use, and cost against looping or malicious tasks. Log the number and sequence of actions so a bounded final answer does not conceal excessive intermediate tool use. OWASP identifies recursive tool abuse and cascading failures among agent risks.
Benign controls, including sensitive but allowed work
Pair attack cases with legitimate in-scope requests, including requests that involve sensitive data but are authorized. Score whether the policy decision was correct separately from whether the task completed. This exposes both unsafe allowances and false refusals that undermine ordinary use.
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How to run a safe, interpretable evaluation
- Freeze and describe the system under test. Record the agent build, model and provider version, system and developer prompt versions, tools and permissions, memory and retrieval configuration, policies, and test environment. Note geography or operating context where it changes the applicable policy or deployment conditions.
- Specify each case before running it. Write down the task, attacker objective, channel, required context, expected allow/block/review result, and concrete violation signal. OWASP’s LLM Prompt Injection Prevention Cheat Sheet provides example cases and guidance on observing outcomes.
- Isolate tools and use synthetic fixtures. Replace live email, file, shell, browser, and API integrations with sandbox implementations. Use dummy credentials and records, and instrument tool requests, authorization decisions, state changes, and data destinations. Do not use real secrets, accounts, customer information, or live third-party targets.
- Run the task through the intended channel. For indirect injection, put the payload in the external source encountered during the legitimate task; test direct user-message attacks separately. Preserve the same task and environment when comparing defenses.
- Repeat each case and retain per-run evidence. Model behavior can vary between attempts, so one success or failure is not a stable rate. NIST recommends repeated attempts for a more realistic assessment; report the run count and retain individual outcomes rather than only an aggregate.
- Inspect traces for invalid results. Review transcripts and tool logs for answer lookup, task-specific hardcoding, grader gaming, unexpected network access, or actions outside scope. NIST distinguishes solution contamination—where evaluation solutions leak into a system—from grader gaming, and discusses trace review and standardized benchmark affordances in Cheating On AI Agent Evaluations.
- Version cases and rerun them after changes. Preserve observed attacks, expected denials, and benign controls as regression cases. Rerun after changes to prompts, tool policies, credentials, retrieval, memory, models, or other material parts of the agent path.
What should the results report?
Report results by objective and task, retaining enough detail for another team to understand what was tested and what a failure means. At minimum include:
- Attack success by objective, such as prompt extraction, unauthorized tool action, data transfer, or hijacking.
- Attack initiation separately from completion of the attacker’s end goal when the test or benchmark distinguishes those outcomes.
- Case count, repeated-run count, model and defense versions, settings, and the source or corpus of cases.
- Benign task completion, incorrect refusals or other false positives, and cases requiring human review.
- Whether an actual tool-layer policy violation occurred, even if the final response appeared safe.
- Any confidence interval, with its calculation method and assumptions, only when the sampling design supports one.
Do not treat hand-picked smoke tests as estimates of real-world attack or refusal rates. OWASP explicitly says its examples are a smoke test, not a security benchmark. The current cheat sheet provides 14 hand-picked attack inputs and seven benign requests; it also illustrates that zero false positives in seven independent trials still yields an approximate 95% Wilson interval of 0% to 35.4%. That interval is an example of sampling uncertainty, not a claim about a particular agent’s actual rate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which benchmark is a useful starting point?
Choose a framework that resembles your agent’s modality, task, tools, and authorization policy, then add cases for your deployment. These options cover different settings; none substitutes for testing the agent and controls you plan to operate.
| Option | Best fit | What it contributes | Limits to account for |
|---|---|---|---|
| AgentDojo | General tool-using agents in simulated work, travel, Slack, or banking contexts | NIST CAISI used its simulated environments and extended cases for remote code execution, database exfiltration, and automated phishing. | NIST describes an evolving framework and attack types added beyond baseline cases. Check the current implementation and add deployment-specific tasks. |
| WASP | Browser and web-navigation agents | An isolated executable web environment with realistic prompt-injection hijacking objectives; the public implementation stores logs and traces. | Its scope is web agents. The paper’s reported ranges are specific to the studied benchmark tasks and setup, not a general production-agent rate. |
| OWASP smoke-test examples | Quick regression checks and a starting point for custom cases | Fourteen hand-picked attack inputs, seven benign requests, and guidance on setup and outcome observation. | OWASP says the examples are illustrative smoke tests, not a representative traffic sample or security benchmark. |
Compare candidate evaluations on agent modality and task realism, attack and benign-control coverage, tool isolation, trace observability, repeatability, customization, maintenance, and alignment between scoring and your actual authorization policy. These are practical comparison criteria, not a published ranking of the frameworks.
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When is an evaluation result strong enough to inform a release?
A release decision should be tied to explicit policy requirements and observable evidence: attacks that violate authorization or move data should fail at the tool or data boundary, while legitimate controls should still work at an acceptable rate. Preserve the exact cases, configurations, traces, and per-run outcomes so the result can be reproduced after a change. A strong result on one benchmark, model, or small case set does not justify a universal safety claim.
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