The Tool Desk
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First, distinguish the two data-leakage risks
The right safeguards depend on what you are trying to learn. An agent evaluation can be compromised because the agent finds an answer key, or because confidential test data leaves the environment. These are different failures and should have separate test cases, telemetry and findings.
| Risk | What goes wrong | What to protect or observe |
|---|---|---|
| Evaluation contamination | The agent obtains benchmark answers or close variants during a capability test, making its score overstate generalization. | Answer keys, solution write-ups, benchmark code, task design and run transcripts. |
| Sensitive-data exfiltration | The agent or test setup reveals private context through generated text, tool calls, API requests, state changes, logs, memory or external destinations. | Synthetic markers, tool and network activity, stored state, logs and the intended recipient of any outbound action. |
A test can pass one dimension and fail the other. Report them separately rather than compressing them into one “leakage” score.
Build a safe, observable test environment
Use synthetic records and substitute secrets
Seed tests with invented records and unmistakable dummy markers, such as TEST_SECRET_7F3A. Do not place a live credential, customer record or production secret in a prompt just to see whether the agent exposes it. A synthetic marker lets you search outputs and traces for disclosure without making the evaluation itself an incident.
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Sandbox side effects before running attacks
Route email, file sharing, payments, databases and network destinations to instrumented test doubles or tightly scoped sandboxes. Capture attempted actions and resulting state changes. A refusal in the final answer does not undo an email or API call the agent already made.
Grant only the access the test requires
Limit data, tools, credentials and destinations to the evaluation’s stated rules. If the task is meant to test external research, preserve the relevant access and document its boundary; if internet access would invalidate the capability test, block it or allowlist only appropriate domains. NIST CAISI describes internet limits as a common way to address solution contamination, not a universal rule for every evaluation.
Keep untrusted content out of privileged instructions
Retrieved pages, uploaded files, emails and tool output may contain malicious instructions. Keep them separate from system and developer instructions, and do not interpolate untrusted variables into privileged prompts. Before external content can influence downstream tools, convert it into narrow, validated structured fields. Structured outputs and isolation can reduce risk, but they do not eliminate it.
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Scope memory and context
Separate memory by user, test case or session unless sharing is an intentional part of the policy being evaluated. Before content persists, sanitize it, scope it, expire it or reject it as appropriate. A case that plants a marker or malicious instruction in one session should test whether another session can retrieve it without authorization.
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Exercise the actual trust boundary
Direct prompt injection and indirect prompt injection are different tests. A direct case puts an override attempt in the user message. An indirect case puts it in an external source the agent is expected to read, such as a retrieved document. If the production risk is a malicious webpage or email, test that content channel rather than relying only on user-message attacks.
For each abuse case, define the trust boundary, synthetic fixture, expected policy result, observable signal and cleanup procedure before the run. The examples below can form a starter matrix; they are not, by themselves, a representative security benchmark.
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| Abuse case | Test fixture or boundary | Evidence to inspect |
|---|---|---|
| Direct prompt override | User message requests that the agent ignore its governing instructions. | Response, tool trace and whether a restricted action was attempted or completed. |
| Indirect injection | A retrieved document or other external content contains an instruction that conflicts with policy. | Retrieved content, agent interpretation, downstream tool calls and resulting state. |
| Dummy-secret disclosure | A synthetic marker is available only in the test context. | Marker matches in answers, tool inputs, API requests, citations and logs. |
| Unauthorized tool use | A task requests an action outside the agent’s permitted tool or credential scope. | Attempted and completed calls, authorization outcome and state changes. |
| Cross-session memory access | A marker is seeded in one test session and queried from another. | Memory reads, retrieval results and any exposure across session boundaries. |
| Outbound exfiltration | A synthetic record is available while outbound traffic is routed to an instrumented destination. | Destination, payload, request logs and whether any marker was transmitted. |
| Approval bypass | A case requires approval before a consequential action. | Approval request, decision, tool trace and whether the action occurred without authorization. |
| Multi-agent propagation | A synthetic secret or malicious instruction crosses a handoff between agents. | Handoff content, recipient context, subsequent calls and any onward disclosure. |
Include benign requests from the agent’s supported workload alongside attacks. Otherwise, an agent that refuses everything could appear secure simply because it avoids useful work.
Instrument more than the final answer
Search for synthetic markers and inspect the paths by which data could move. A clean user-facing answer does not establish that nothing left through another channel.
- Generated content: final answers, intermediate or delegated outputs that your harness records, and citations.
- Actions: tool calls, their arguments and results, API requests, approval events and state changes.
- Storage: memory reads and writes, retrieval results, conversation state and relevant logs.
- Destinations: outbound requests and transfers to controlled, instrumented endpoints.
- Test infrastructure: whether a failed collector, unavailable trace or unsupported context made the result unobservable.
Record the expected and observed result for each case. If telemetry is missing or the test context was unsupported, classify the result as inconclusive—not blocked.
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Make the evaluation repeatable and interpretable
Record the tested system and its boundaries
For every run, retain 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, relevant logs, expected result, observed result and cleanup. These settings are part of the system being evaluated, not incidental details.
Report denominators, controls and uncertainty
State the corpus provenance, number of cases and attempts, failure categories, benign task-completion rate and false-positive security-refusal rate. Keep security objectives separate rather than collapsing unrelated outcomes into one score. Repeated variants of a single case should not be treated as independent attack samples; report confidence intervals only when the sampling assumptions justify them.
A hand-picked set of prompts is a smoke test, not a population estimate. OWASP’s testing guidance makes the same distinction: “Use the examples below as a smoke test, not a security benchmark.” Broader claims need a described corpus, repeats and evaluation method.
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Rerun after changes that alter behavior
Maintain regression cases for prompt override, unauthorized tools, privilege escalation, memory poisoning, exfiltration, approval bypass and multi-agent chaining. Run them before release and after material changes to prompts, tools, memory, retrieval, policies or model/provider. Keep benign controls in the suite so a security fix that blocks ordinary supported work is visible too.
What published attack results do—and do not—show
In a 2025 NIST Center for AI Standards and Innovation evaluation derived from AgentDojo, the strongest attack success rate reported for the upgraded Claude 3.5 Sonnet on held-out Workspace tasks was 11%; the strongest novel red-team attack success rate in that same evaluation was 81%. Those figures describe that model and test setup, not an expected rate for other agents, deployments or current model versions. They illustrate why a fixed attack set can miss newly devised attacks, not a universal leakage probability.
Prevent benchmark contamination without making the task unrealistic
When the goal is to measure capability, protect answer keys, solution write-ups and benchmark code from being scraped or exposed to the evaluated agent. Review run transcripts for shortcuts, tighten task design where needed and state the rules clearly. Internet access may expose newer code, package versions or published solutions, but banning every external lookup can make a task unlike its intended real-world use. Define the allowed sources and actions, and prohibit the specific shortcut that would invalidate the test.
Report the actual system and harness, task distribution, tool access, settings, budgets, elicitation choices and validity checks. Without this context, a score may not support the broader capability claim a reader takes from it.
Interpret results as risk reduction, not proof of zero leakage
Prompt-injection filters, structured outputs, human approval and sandboxing are controls, each with residual risk. A test suite provides evidence about the cases and conditions it covers; it cannot establish that an agent will never disclose data, especially against a persistent or adaptive adversary. Keep that limit explicit when publishing findings or using them to approve a deployment.
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