Effective LLM safety test cases start with a narrow risk claim and specify what the system should do in a reproducible scenario. Include direct and indirect adversarial inputs, test the model in its intended application setup, and define observable scoring criteria before running the test. The result is evidence about that setup—not proof that a model is universally safe.
What an effective safety test case should establish
First decide what the test is meant to show. A case can test whether a model can perform a capability, whether a safeguard holds under a particular attack, or whether one system performs differently from another. These are distinct claims and need different test designs. OpenAI’s third-party evaluation guidance recommends stating the claim and providing evidence that the test validly exercises it.
For example, “the assistant is safe” is too broad to evaluate. A testable claim would be: “With this application configuration, the assistant does not follow instructions embedded in retrieved, untrusted text that ask it to disclose a specified private field.” That claim identifies a behavior, an attack context, and a boundary the reviewer can assess. It does not imply anything about unrelated risks or configurations.
Define the risk and the system under test
Scope the threat model
Describe the intended use, the people or systems that could be affected, and the plausible route to harm. State who or what is attempting which outcome and under what application conditions. Prioritize risks in the context of the deployed product rather than treating every imaginable misuse as equally relevant. Depending on the application, risks may include policy-violating requests, prompt injection, privacy exposure, adversarial inputs, or service disruption.
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Test the system as users encounter it. A model’s behavior can depend on the application’s instructions, policies, retrieval sources, tools, safeguards, and state. A test of a bare model cannot by itself establish how the full application behaves.
Write the claim before writing prompts
Make the claim specific enough that a test can support or fail to support it. Identify the behavior to elicit, the relevant configuration, and the kind of evidence that would count. If the goal is to measure safeguard robustness against a credible adversary, a single obvious request may be too weak to exercise that claim. If the goal is simply to check a basic policy boundary, a straightforward case may be appropriate.
Build scenario families, not one-off prompts
For each risk claim, create a small family of cases that varies how the risk appears. Include direct requests and contextual or indirect attempts, including adversarial variants relevant to the product. Google’s Responsible Generative AI Toolkit recommends explicit and implicit adversarial queries and a safety dataset suited to the application.
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- Direct: The user explicitly asks the assistant to perform the disallowed action.
- Contextual or implicit: The risk is embedded in surrounding content, an indirect request, or the task context rather than stated plainly.
- Adversarial: The input attempts to bypass or confuse the safeguard, in a way consistent with the threat model.
- Multi-turn or tool-mediated: The case tests retained context, retrieval, or actions when the product can use these features.
Keep the scenario realistic to the application. A diverse set of carefully reviewed cases is more informative than a large set of superficial prompt rewrites. For agentic or multi-step systems, include the relevant sequence of turns and tool interactions; a single prompt may not reproduce the path to the behavior being tested.
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Specify expected behavior and scoring before the run
State what a passing response or action looks like, and what would count as a failure for the claim. Where appropriate, note safe alternatives the system may provide. Write criteria that different reviewers can apply consistently; avoid relying on impressions such as “seems safe.”
Choose a scoring method that fits the case: human review, an automated evaluator, or a documented combination. Preserve the relevant output and evidence for the decision. Check whether a system could score well through a shortcut—for example, refusing everything without demonstrating the behavior the test is meant to measure. OpenAI’s evaluation guidance also flags reward hacking, refusals that obscure the behavior under test, and contamination as threats to validity.
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Use a reproducible case record
A score without the setup is hard to interpret or repeat. Record the following for every case:
- Case ID and version: A stable identifier plus revision history.
- Risk claim: The precise safety behavior, capability, or safeguard being tested.
- Scenario and threat model: The actor, attempted outcome, and application conditions.
- Input sequence: The complete prompt or multi-turn interaction, including relevant context and direct or indirect variants.
- System under test: Model and version; application configuration; policies; tools; retrieval sources; and safeguards that may affect the result.
- Harness and budget: Interface, scaffolding, tool access, time or token limits, allowed effort, and other constraints.
- Expected behavior: The concrete response or action criterion, including acceptable safe alternatives when relevant.
- Scoring rule and evidence: The evaluator, rubric, examples for borderline cases, and the interaction evidence used to assign a score.
- Validity checks: Checks for scorer shortcuts, ambiguous refusals, or cases and answers that may be known to the system.
- Results and follow-up: Relevant interaction, score, reviewer decision, severity, remediation, regression status, and date or version last run.
This is a practical template synthesized from published guidance, not a prescribed standard. Adapt it to the system and risk while retaining enough information for another team to understand what was tested.
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Preserve the model and system versions, safeguards, tools, harness, and effort limits. For long-running or agentic interactions, document the scaffolding and elicitation instructions as well as the interface. The harness can determine whether a behavior is elicited at all; an underpowered or mismatched setup may fail to exercise the capability named in the claim. Results therefore describe performance under the stated conditions, not an absolute capability ceiling.
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When comparing systems, align the risk claim, scenarios and attack strength, model or system versions, harness and tools, budget, and scoring method. If some dimensions differ, disclose how; otherwise an apparent difference may come from the setup rather than the system. When effort or budget can affect success, report it and, where useful, cost per successful attempt alongside success rate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use red teaming to find cases, then evaluate them consistently
Red teaming and evaluation answer related but different questions. OpenAI’s API documentation describes evaluations as a way to measure whether a system behaves as intended, and red teaming as probing behavior under adversarial, abusive, or unexpected inputs. Human testers can uncover failures that a prewritten set missed; automated approaches can help expand attack generation. Review findings for relevance and quality, then turn suitable cases into recurring evaluations.
Red teaming alone is not a complete risk assessment. A campaign is exploratory and time-bound; an evaluation applies explicit criteria to a defined set of cases. Use both: exploration to discover candidate failures, and repeatable tests to check whether selected behaviors recur or regress.
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Maintain the suite and report what it cannot show
A test set can become stale as models, applications, and attacks change. Reassess it after meaningful system changes, add cases for newly observed risks, and backtest against known incidents. Check whether test awareness or evaluation gaming could distort results. OpenAI’s safety-case guidance discusses backtesting, worst-case stress tests, gaming, and the freshness of monitoring evaluations.
Report the residual uncertainty: which configurations and scenarios were covered, what the harness allowed, how results were scored, and which relevant risks were outside the test. A well-designed suite makes evidence easier to reproduce and interpret; following a template does not guarantee safety.
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