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Use synthetic data, sandboxed tools and a controlled test environment—not real customer records, personal information or live credentials. Define what each test should prove, exercise the chatbot through the application path people actually use, and inspect tool calls and state changes as well as its final answer. A polite refusal by itself does not show that nothing was disclosed or that no action occurred.
Set up a safe, useful test
- Map the system. List the chatbot’s intended jobs, data sources, user roles, retrieval components and connected tools. Identify what information it could expose and what actions it could take. Include the application’s normal access controls. For a retrieval-augmented generation (RAG) system, treat retrieved or uploaded documents as a separate, potentially untrusted input path.
- Build synthetic fixtures. Use fictional people and records, dummy secrets and unique test-only markers. Keep live customer data and actual credentials out of prompts, test corpora and fixtures. Replace connected tools with sandbox substitutes that operate on dummy state.
- Define observable cases. For each test, record the legitimate task or intended security violation, the context the chatbot needs, and the evidence that will show what happened. Include benign tasks so you can assess whether defenses block legitimate work as well as attacks. OWASP recommends defining the intended outcome before testing in its LLM Prompt Injection Prevention Cheat Sheet.
- Use the real application path, safely. Submit tests through the interface or API path the application normally uses, while keeping data synthetic and tools sandboxed. Test direct prompt manipulation. If the chatbot processes external content, place malicious instructions in a test document or other content source and check the route that content actually follows; a user-message-only test does not cover indirect prompt injection.
- Check the effects. Look beyond the final response. Inspect whether a dummy marker appears in output, citations, logs or tool calls; whether authorization decisions were correct; whether dummy state changed; and whether data reached an instrumented test destination. A refusal after a tool has acted does not undo that action.
- Repeat and retain evidence. Run cases more than once because model behavior can vary. Keep results by case, along with the test corpus source, model and defense versions, settings and run count. Re-run relevant cases after material changes to prompts, tools, memory, retrieval, policies or model providers, as recommended in OWASP’s AI Agent Security Cheat Sheet.
- Report bounded findings. For each objective, record whether a violation was observed, not observed, inconclusive or not applicable. Include benign-task completion and false refusals. Missing telemetry, errors and unsupported test contexts are inconclusive—not proof that an attack was blocked.
What to test
Choose cases based on the chatbot’s actual data, integrations and intended use. OWASP’s AI Testing Guide treats trustworthiness as broader than conventional security checks, spanning application, model, infrastructure and data concerns.
- Disclosure: Check whether dummy sensitive values appear in answers, citations, logs, tool calls or other downstream channels.
- Prompt injection: Test direct user instructions and, where applicable, indirect instructions carried in untrusted documents or retrieved content.
- Authorization and action: Check tool permissions, access boundaries, approval requirements, least privilege and changes to dummy state. For agents, include unauthorized tool use and privilege-escalation attempts where relevant.
- Other trustworthiness risks: Test unsafe or policy-inconsistent outputs, hallucinations, bias and legitimate-task false refusals when those risks matter to the use case.
- Retrieval and data behavior: For RAG systems, check whether retrieved context is appropriate and supports the answer. NIST’s prototype report describes retrieval failures as a material limitation and documents filtering and access controls in its particular implementation; those details do not establish how another system behaves.
Interpret results without overclaiming
If a dummy marker does not appear in the final answer, you have established only that it was not observed there in that test. It does not prove that other content, a transformed disclosure or a downstream channel could not leak information. Likewise, a final refusal does not establish that no unauthorized action took place.
OWASP describes its hand-picked prompt-injection smoke test as illustrative, not representative of real traffic or attacks; passing it does not demonstrate resistance to a persistent adversary. Treat individual failures as findings to investigate, not as a complete measure of security. A small set of cases is useful for finding specific weaknesses, but it cannot support a population attack rate or a general claim of robustness.
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Keep conclusions tied to the evidence collected. If tool logs are unavailable, for example, report that tool activity could not be verified rather than counting the case as blocked. Separate results by security objective and include legitimate-task performance so a system that refuses everything is not mistaken for a safe, useful chatbot.
What a NIST chatbot evaluation illustrates
NIST’s July 2025 initial public draft, NIST IR 8579, describes an internal NCCoE chatbot evaluation. It used about 100 manually selected and revised questions with corresponding ground-truth answers. Responses were assessed automatically using an LLM-as-a-judge, with only a small subset receiving manual review. NIST reported a Pearson correlation coefficient of 0.7872 between the manual and automatic assessments, then said a much larger human-assessed set would be needed for conclusions with statistical confidence.
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The example shows how a test set, reference answers, automated assessment and human review can be combined—and why the evaluation’s limits matter. Its result is not evidence that another chatbot will achieve the same correlation or performance. NIST notes that this draft is not implementation guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check provider terms before using any non-synthetic input
Data retention, logging and use for model training depend on the provider, deployment, account, geography and current configuration. No single policy applies to every chatbot. Review the current terms and settings for the specific service before testing with anything other than synthetic information; for the safety procedure described here, dummy data remains the lower-risk choice.
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