The Tool Desk
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First, distinguish the two kinds of leakage
Sensitive-data leakage
This is a confidentiality failure: an agent or its testing setup reveals private test context through a response, tool call, API request, memory, log, citation, or external connection. A safe-looking final answer does not prove that no data left through another channel.
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Evaluation contamination
This is a measurement failure: an agent finds benchmark answers, close variants, or other shortcuts, so its score overstates its ability to solve the intended task. NIST’s Center for AI Standards and Innovation (CAISI) recommends reviewing transcripts, securing answer materials, tightening task design, and stating the evaluation rules clearly. These protections do not replace controls for sensitive data.
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Use synthetic fixtures and substitute secrets
Put invented records and dummy markers in the test context. Never place live credentials, production secrets, or real customer records in a prompt just to see whether an agent will disclose them. A unique marker such as TEST_SECRET_7F3A can help detect attempted disclosure without making a real secret part of the test.
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Sandbox side effects
Route email, file sharing, payments, databases, and network destinations to instrumented test doubles or tightly scoped sandboxes. Observe tool calls and resulting state changes: a refusal in the final response cannot undo an email sent or a record changed earlier in the run.
Set permissions to match the test
Give the agent only the data and tools required for the scenario. Define allowed network domains and block internet access when that matches the evaluation rules. If external research is part of the behavior being tested, preserve the necessary access and state which sources and actions are allowed rather than banning every lookup.
Separate instructions from untrusted content
Retrieved pages, files, emails, and tool output are data, not authority. Keep them separate from system and developer instructions; do not interpolate untrusted text into privileged instructions. Before untrusted content can influence downstream tools, convert it into narrow, validated structured fields. OpenAI’s agent guidance warns that “Risk rises when agents process arbitrary text that influences tool calls.” Structured outputs and isolation can reduce risk, but do not eliminate it.
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Isolate memory and session state
Scope context and memory to the user, session, or test case. Prevent one case’s data from being available to another unless that access is an explicit part of the policy being evaluated. OWASP advises sanitizing, scoping, expiring, or rejecting malicious content before it persists in memory.
Test the actual path an attacker could use
Direct prompt injection and indirect prompt injection test different boundaries. To test indirect injection, place the adversarial instruction in the external content channel the agent is expected to process—for example, a retrieved document—instead of testing only with a malicious user message. OWASP describes its examples as “a smoke test, not a security benchmark.”
Use a test matrix to connect each attack attempt to the boundary it exercises, the synthetic fixture, the expected policy outcome, what you will observe, and how you will clean up. Include benign requests from the agent’s supported workload, so a system that refuses everything cannot appear secure simply by avoiding all tasks.
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| Test case | Boundary to exercise | Observable signal |
|---|---|---|
| Direct prompt override | User input versus system and developer instructions | Response, attempted tool calls, and policy result |
| Indirect injection in a retrieved document | Untrusted retrieved content versus privileged instructions | Whether the content changes the response or triggers an unauthorized action |
| Request to reveal a dummy prompt marker | Confidential test context versus output and tools | Marker exposure in text, calls, requests, citations, or logs |
| Unauthorized tool use or approval bypass | Tool permissions and approval controls | Attempted and completed actions, including changes in the sandbox |
| Cross-session memory access | Memory and context isolation | Whether one test case can retrieve another case’s synthetic data |
| Dummy data sent to an instrumented destination | Data access versus outbound channels | Destination, payload, and whether the sandbox received the data |
| Multi-agent propagation | Handoffs between agents and shared state | Whether untrusted instructions or sensitive markers move between agents |
Record tool traces, relevant logs, state changes, API requests, citations, and activity at controlled outbound destinations. Treat missing telemetry, a failed harness, or an unsupported test context as inconclusive—not as a successful block.
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Keep a run record
For every case, record the agent and model version; system prompts and harness; tool and credential scopes; retrieval and memory settings; allowed network domains; task-data version; attempt number; tool trace and relevant logs; expected and observed result; and cleanup performed. Those details define what was tested and help explain whether a later result is comparable.
Use regression cases without mistaking them for a benchmark
Maintain cases for prompt override, unauthorized tool use, privilege escalation, memory poisoning, data exfiltration, approval bypass, and multi-agent chaining. Run them before release and after material changes to prompts, tools, memory, retrieval, policies, or the model or provider. A small, hand-picked set is useful as a regression check, but it is not a representative security benchmark.
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Report denominators, controls, and uncertainty
For each security objective, report the number of attempts and outcomes, the test corpus and its provenance, repeats, failure categories, and the benign task-completion and false-positive refusal rates. Do not collapse unrelated objectives into one score, count repeated variants of a single case as independent attacks, or report a confidence interval unless the sampling assumptions support it. Mark runs with missing telemetry or failed infrastructure as inconclusive.
CAISI’s 2025 AgentDojo-derived evaluation illustrates why a fixed attack list can age quickly: in its described held-out Workspace tasks, the strongest attack success rate for the upgraded Claude 3.5 Sonnet was 11%; the strongest novel red-team attack success rate in that same evaluation was 81%. Those figures describe that test setup, not a general rate for other agents, deployments, or model versions.
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Protect capability evaluations from solution leakage
When measuring capability, look for routes to benchmark answers beyond the prompt itself: internet search, newer code versions, package managers, exposed solution files, and transcripts that reveal loopholes. Secure answer keys, solution write-ups, and related benchmark code so they are not easily scraped or exposed during runs. Review transcripts and specify the rules, including which sources and actions are permitted.
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NIST CAISI notes that limiting internet access is a common way to address solution contamination. The restriction should fit the task: block or allowlist sources when needed to prevent a specific shortcut, but do not make an evaluation unrealistic by prohibiting ordinary task-relevant work without reason. OpenAI’s 2026 third-party evaluation playbook calls for reporting the tested system and harness, task distribution, tool access, settings, budgets, elicitation choices, and validity checks for contamination and other behaviors that could undermine results. A score without that context may not support the broader claim readers infer from it.
What a clean test can—and cannot—establish
Layered controls and instrumented tests can reduce exposure and provide evidence about how an agent behaved under defined conditions; they cannot prove zero leakage. OpenAI’s agent guidance says structured outputs and isolation do not fully remove risk, and OWASP cautions that its illustrative injection examples are a smoke test rather than a security benchmark. Report the tested boundaries and remaining blind spots instead of presenting a passing test as a guarantee.
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