Effective LLM safety test cases start with a narrow risk claim and a clear rule for judging the model’s behavior. A useful case also captures the application context, input sequence, model and safeguards, test harness, effort budget, and scoring evidence. That makes the result repeatable—and keeps it from being mistaken for proof that a model is universally safe.
Start with the claim the test is meant to support
Write the claim before drafting prompts. A test can measure whether a model can produce a behavior, whether a safeguard resists a defined attack, or whether one system performs better than another. These are different questions and need different setups. OpenAI’s third-party evaluation guidance, published May 29, 2026, groups evaluation claims into capability elicitation, safeguard performance, and comparison, and calls for evidence that the test is valid for its stated claim.
Keep each claim specific to a risk, configuration, and condition. For example: “With this retrieval configuration, the assistant does not follow instructions embedded in untrusted retrieved text.” This is a testable formulation, not a conclusion about any particular model. A broad claim such as “the model is safe” does not say what behavior was tested or where the result applies.
Define the real-world scenario and threat model
Describe who or what might cause the risk, what outcome they seek, and how the application could enable it. Include the product’s intended use and the people who could be affected. Prioritize scenarios using the application context, expected capabilities, and known failures rather than choosing prompts only because they are easy to write.
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Build scenario families for each risk. Vary wording, context, and attack strength; add multi-turn sequences or tool-mediated actions when the product retains state or can act on a user’s behalf. A direct prompt may test basic behavior, but it does not establish robustness against a credible adversary if the claim concerns stronger attacks.
Write expected behavior and scoring rules before the run
State what observable response or action counts as meeting the claim. If several safe outcomes are acceptable, list them. Define what constitutes a failure, and provide examples or a rubric for borderline cases so reviewers apply the same standard consistently.
Choose a scorer that can judge the behavior you are testing, and document whether it is human, automated, or a combination. Check whether it can be fooled by superficial signals: a refusal may obscure whether the system would have taken an unsafe action, while a scorer that rewards one phrase may encourage the model to produce that phrase without behaving safely. OpenAI’s evaluation guidance also flags reward hacking and contamination—when a system has encountered test material or answers—as threats to validity.
Do not conflate a safe refusal with a useful answer unless the claim requires both. For a test of whether the system avoids a specified unsafe action, an irrelevant refusal might satisfy the narrow safety criterion but still reveal a separate product-quality problem. Keep those judgments distinct.
Record enough detail to reproduce each case
Capture the full context that could change the result, not just the final user prompt. A practical case record can use this template:
- Case ID and version: A stable identifier and revision history.
- Risk claim: The specific behavior or safeguard being tested.
- Scenario and threat model: The actor, desired outcome, and application conditions.
- Input sequence: Relevant context and turns, including direct and indirect or adversarial variants.
- System under test: Model and version, application configuration, policies, tools, retrieval sources, and safeguards.
- Harness and budget: Interface, scaffolding, tool access, effort or time limits, and other constraints.
- Expected behavior: The response or action criterion and acceptable safe alternatives.
- Scoring rule and evidence: The evaluator, rubric, examples, and relevant interaction record.
- Validity checks: Possible scorer shortcuts, obscuring refusals, and test or answer contamination.
- Results and follow-up: Score, reviewer decision, severity, remediation, regression status, and date and version of the run.
This is a practical template synthesized from public guidance, not a prescribed standard. The point is to preserve enough information for another person to understand what was tested and reproduce the conditions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Match the harness and budget to the claim
The harness affects what a test can elicit. For multi-step or agentic behavior, document the tools, scaffolding, elicitation instructions, and allowed effort. A harness that cannot provide the model with the relevant tools or interaction path may fail to elicit the behavior the claim is about. Conversely, results from a limited setup should not be described as an absolute ceiling on model capability.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsFor a comparison, keep the risk claim, scenarios, attack strength, system configuration, harness, effort budget, and scoring method aligned. If they differ, say how; otherwise a result may reflect unequal conditions rather than a meaningful system difference. When budget can affect success, report it and, where useful, cost per successful attempt alongside the success rate. OpenAI’s evaluation playbook emphasizes that capability claims depend on choosing a harness suited to the task and the capability being measured.
Use red teaming to find cases, then evaluate them repeatedly
Red teaming and evaluation are complementary, not interchangeable. OpenAI’s API documentation on red teaming describes evaluations as a way to measure whether a system behaves as intended and red teaming as a way to probe behavior under adversarial, abusive, or unexpected inputs.
Human testers can uncover varied, surprising failures. Automated methods can help generate or expand attack attempts. Review findings for relevance and quality, then turn appropriate examples into repeatable evaluation cases with defined scoring. OpenAI’s paper on external red teaming describes this kind of campaign work and cautions that red teaming alone is not a complete risk assessment.
Keep the suite current and report what it cannot establish
Revisit cases when the model, application, safeguards, tools, or risks change. Backtest against known incidents, add cases for newly observed failure modes, and check whether test awareness or gaming has made the suite easier to pass without improving the behavior of interest. OpenAI’s September 28, 2026 discussion of safety cases highlights backtesting, evaluation gaming, worst-case stress tests, and the risk that monitoring evaluations become stale.
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