To red-team an AI chatbot responsibly, define what you are testing, get explicit authorization, and put practical controls around the test before trying adversarial prompts. Treat role-play and persona bypasses as one test class alongside prompt injection, jailbreaks, and instruction overrides. A result describes the specific model, safeguards, interface, and threat model you tested; it does not prove universal safety.
What an AI red-team test can establish
Red teaming probes misuse, high-risk interactions, and failure modes: for example, whether a chatbot follows an instruction embedded in untrusted content or abandons its safeguards when asked to adopt a different persona. An evaluation can also measure intended behavior against defined criteria. These approaches serve different purposes and can complement one another; adversarial prompting alone is not a complete safety assessment. OpenAI’s API safety guidance recommends red-teaming an application against adversarial input, but that vendor recommendation is not an independent standard or a guarantee that a system is safe.
Start by naming the claim you want to assess. Are you probing a capability, checking safeguard performance, or comparing two configurations? State that claim before testing so the prompts, scoring, and eventual conclusion stay aligned.
Set authorization and scope before testing
Test only systems you own or have explicit permission to assess. OpenAI’s red-teaming guidance makes this authorization boundary explicit. Permission to use a chatbot does not, by itself, authorize testing its provider’s infrastructure, accounts, or connected services.
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Write down the scope in terms a tester can follow, rather than relying on a general instruction to “test safely.” Specify:
- System: owner, model and version, configuration, and deployment being assessed.
- Interface: the approved chat endpoint, application, or test harness, including whether any connected tools may be used.
- Allowed inputs and data: what prompts, files, accounts, and test data are permitted.
- In-scope behaviors: the safeguards and failure modes the exercise is meant to probe.
- Prohibited targets: systems, accounts, services, data, or actions testers must not touch.
- Authority and escalation: who approved the exercise, who receives incident reports, and who can stop the work.
Include ordinary representative interactions as well as adversarial ones. OpenAI’s safety best practices discuss testing with representative and adversarial inputs. Without ordinary cases, it is harder to tell whether a failure is specific to an attack or reflects a broader problem with the application.
Include role-play and other attack classes in the test plan
Role-play testing asks whether framing the interaction as fiction, simulation, or a change of identity alters the system’s behavior. A persona test might ask the model to act as a character who ignores the normal rules. The point is not to treat every role-play request as unsafe; it is to check whether the system retains the relevant safeguards when the framing changes.
Define each case by the behavior it is intended to elicit and the outcome that would count as a failure. Include applicable classes such as:
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- Persona changes and role-play framing: requests to adopt a character, simulated role, or fictional setting that conflicts with the system’s stated constraints.
- Prompt injection: untrusted text, such as supplied content, that attempts to redirect the model away from its intended task.
- Jailbreak attempts and instruction overrides: direct requests to disregard, replace, or reveal instructions or safeguards.
- Multi-turn chains: conversations that gradually change the request or try to establish a new set of rules.
- Control retention: whether the system continues to follow the intended boundaries as the conversation or task evolves.
- Safety-control conflicts: cases where a user request, application instruction, or tool action pulls against a safety constraint.
- Out-of-bounds conversation: requests outside the application’s intended purpose or the exercise’s authorized scope.
These categories align with the OWASP GenAI Red Teaming Guide, which includes role-play and persona bypasses among its test areas. Keep the purpose of each case clear: what behavior is being tested, what response would be acceptable, and what observable result would indicate a failure?
Contain the exercise operationally
A written scope is not a substitute for controls that limit what the test can reach. Choose an environment appropriate to the possible impact, then verify the boundaries before adversarial testing begins. OpenAI’s report on third-party cyber evaluations describes incidents involving evaluation boundaries and identifies isolation, credential controls, monitoring, and stop conditions as relevant safeguards.
- Network access: establish which services the test can reach and verify that prohibited destinations are blocked.
- Credentials and permissions: give testers only the access needed for the approved interface; do not expose production secrets or unrelated accounts.
- Isolation: use a controlled environment suited to the test’s potential impact, especially when tools or live access are involved.
- Monitoring: record activity and identify who is watching for unexpected access, outputs, or system behavior.
- Stop conditions: define concrete triggers to pause or terminate the exercise, such as unexpected access to a live system or exposure of sensitive data.
- Incident escalation: specify how testers notify the owner and who decides whether testing may resume.
If live access or reduced safeguards are necessary to answer the test question, treat that as an explicit risk decision. Document the reason, authorization, compensating controls, and limits; do not assume that a prompt-only boundary will prevent actions beyond the intended test.
Combine human and automated methods carefully
Human testers can bring domain, language, and cultural perspectives to scenarios that automated case generation may miss. Automated approaches can produce cases at larger scale. Neither method makes its own output sufficient evidence: review generated cases for quality, relevance, and diversity before drawing conclusions from them.
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NIST’s ARIA Evaluation Planning Manual, published September 18, 2026, presents Model Testing, Red Teaming, and User Testing as complementary evaluation types. A broader program can combine them: model testing for defined behavior, red teaming for adversarial failures, and user testing for how people experience the system in use.
Record findings so they can be reproduced
A useful finding includes enough context for another evaluator to understand what was tested and try the case again. Record:
- the model and version, configuration, and safeguards enabled;
- the claim and threat model, including the assumed tester capability;
- tester instructions, interface or harness, and available tools;
- the prompts and relevant conversation context;
- the elicitation method and effort, attempt count, or budget;
- the observed output and reproduction steps;
- the severity rationale and any evidence-validity checks.
Review failures against the application’s existing policy. A result may reveal a model behavior, a gap in the application’s safeguards, or an unclear policy boundary; distinguish these rather than treating every surprising response as the same kind of defect. Convert well-supported cases into repeatable evaluations so future versions or configurations can be checked consistently. OpenAI’s work on red teaming with people and AI discusses scoping, tester selection, versioning, instructions, documentation, and reusable evaluations. Its shared playbook for trustworthy third-party evaluations emphasizes documenting claims, elicitation setup, harness, and budget so results can be interpreted.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare results on equivalent terms
If the purpose is to compare models or configurations, keep the conditions equivalent where possible and disclose any differences that remain. Otherwise, an apparent difference may come from the setup rather than the system.
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|---|---|
| Model | Model name and version for each result. |
| Safeguards | Which safeguards were enabled and how the configurations differed. |
| Threat model | Assumed attacker capabilities and tester expertise. |
| Interface and tools | Harness, available tools, and access to external services. |
| Elicitation | Attack strategy and the number of attempts or total testing budget. |
| Containment | Isolation, credentials, and network configuration. |
| Scoring | Failure criteria, scoring method, and checks on evidence validity. |
Different harnesses and testing budgets can change what behavior a test elicits. Report those conditions with the result instead of presenting a comparison as though it were a general ranking.
State the limits of every conclusion
Bound the conclusion to the system configuration, harness, safeguards, threat model, and budget actually tested. A failure under a simple prompt setup does not establish how the system would respond to a stronger attacker. Conversely, a test with unusually permissive access does not automatically describe an ordinary deployment.
Describe how the behavior was elicited and what the test can support. Do not turn a pass in a limited set of cases into a claim that role-play can never bypass safeguards, or that the system is universally safe. No broadly applicable efficacy statistic for role-play boundary testing is established in the sources cited here, so a generic pass rate would not be a sound summary.
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