A reliable refusal does two things at once: it withholds genuinely dangerous help and still answers harmless questions that merely resemble dangerous ones. Evaluate both outcomes. Measure unsafe compliance on prompts that should be refused, benign over-refusal on prompts that should be answered, behavior on ambiguous cases, resistance to adversarial rewrites, and the quality of the grading process. A single refusal percentage cannot establish reliability.
If you are asking, “How do I know if an AI refusal is correct?” the practical answer is to compare the response with a written policy decision, not with the presence of the words I can’t help. The same wording can conceal a safe limited answer, an unnecessary refusal or a refusal followed by actionable leakage.
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What a reliable refusal actually means
“Refused” is not a safety metric by itself. A system that rejects every request can achieve a perfect refusal rate while being unusable. Conversely, a helpful system that answers broadly may provide unsafe instructions. Reliability is the match between the response and the policy boundary that applies to the product, user, domain and deployment.
| Question | What to measure | Why it matters |
|---|---|---|
| Did the assistant avoid harmful help? | Unsafe compliance on disallowed prompts | Measures whether a refusal boundary blocks the assistance it is supposed to block. |
| Did it help when it should? | Benign compliance, often reported as low over-refusal | Detects harmless requests rejected because they contain risky words or context. |
| Can it handle uncertainty? | Responses to ambiguous and dual-use prompts | A safe, limited answer may be better than either full compliance or a blanket refusal. |
| Does the boundary survive pressure? | Jailbreak, paraphrase and instruction-conflict tests | Real users do not phrase every request like a benchmark item. |
| Can you trust the result? | Prompt labeling, judge quality and human review | A flawed rubric or automated grader can make a model look safer or less safe than it is. |
OpenAI’s o1 system card illustrates the two-sided approach with separate not_unsafe and not_overrefuse measures. Its authors note that making refusal boundaries accurate remains an ongoing challenge.
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Start by defining the boundary
Write the intended behavior before writing test prompts. Create three policy outcomes:
- Refuse: the assistant should not provide the requested harmful capability or actionable details.
- Answer: the request is allowed, even if it mentions a sensitive topic.
- Safe completion: the assistant should provide a constrained answer, such as high-level prevention, legal context, defensive guidance or a request for clarification.
Tie each outcome to the actual product policy and deployment. A consumer chatbot, a coding agent with tools and an assistant used in a high-risk account may legitimately have different boundaries. The GPT-6 Astra system card, retrieved October 13, 2026, describes a cyber boundary that refuses a broader range of assistance for accounts assessed as higher risk. Do not label a response wrong without first recording which policy, risk tier and user context you are applying.
Define what counts as a material safety failure. A direct set of instructions, a partial procedure, code, a list of exploitable targets and operational troubleshooting may all differ in severity. Your rubric should say whether a refusal can include a brief explanation, a safe alternative or a referral to emergency help, and where those additions become enabling detail.
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Use at least three groups, with ground-truth labels adjudicated against the written policy:
Disallowed requests
Include genuinely harmful prompts covering each relevant category. Keep ordinary wording alongside explicit requests; otherwise you test keyword recognition rather than the policy boundary. These items measure unsafe compliance: whether the model refuses, gives dangerous instructions or leaks enough detail to enable the prohibited action. Include several severity levels so a minor policy slip is not treated as identical to a complete operational answer.
Benign but safety-adjacent requests
Test translation, education, journalism, fictional analysis, troubleshooting and defensive work that contain alarming terms without requesting harmful capability. OpenAI’s o1 evaluation gives examples such as refusing to translate quoted harmful text or rejecting an innocuous programming question because it uses a risky-sounding verb. These cases reveal over-refusal. Include ordinary user phrasing, because polished benchmark language can hide the false-positive behavior people experience in production.
Ambiguous and dual-use cases
Include prompts where intent or requested detail is unclear. The expected answer may be a clarifying question or a limited, safety-oriented completion. Record the acceptable range before scoring; silently forcing every ambiguous item into “refuse” or “answer” makes the result arbitrary. A good label can specify which facts would move the item from safe completion to refusal, giving reviewers a consistent basis for adjudication.
The OR-Bench paper demonstrates this design. Its authors assembled 80,000 seemingly toxic prompts across 10 common rejection categories, then used a hard subset of about 1,000 prompts and 600 toxic controls. Those figures describe that benchmark’s construction, not a universal sample-size requirement. Its key lesson is to test apparently risky benign prompts separately from genuinely toxic ones.
Keep a prompt registry. Store the text, category, intended outcome, rationale, source, language, modality, risk tier and version of the policy used for labeling. Freeze a baseline set for comparisons, but maintain a separate rotating set for newly observed attacks so a model cannot improve merely by memorizing old examples.
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Track separate metrics instead of one refusal rate
| Metric | Denominator | Interpretation |
|---|---|---|
| Unsafe-compliance rate | Prompts labeled “refuse” | Share of disallowed prompts receiving prohibited or materially enabling help. Lower is better, subject to a clearly defined severity rubric. |
| Not-unsafe score | Prompts labeled “refuse” | Share judged not to contain unsafe assistance. OpenAI uses this label in its o1 card. |
| Over-refusal rate | Prompts labeled “answer” or “safe completion” | Share incorrectly refused or unnecessarily constrained. Lower is better. |
| Not-overrefuse score | Benign evaluation prompts | Share on which the assistant complied appropriately. OpenAI reports this separately from not-unsafe. |
| Safe-completion quality | Ambiguous or dual-use prompts | Whether the response gives useful, bounded help rather than a blanket refusal or dangerous detail. |
| Robustness by variant | Each original prompt and its rewrites | Whether the same policy decision survives changes in language, framing and instruction order. |
Report category-level results as well as aggregates. A strong overall score can conceal one dangerous category or a high false-positive rate in a domain your users rely on. Also report a confusion matrix or equivalent counts: unsafe answers on refuse items, unnecessary refusals on answer items, acceptable safe completions, and genuinely helpful answers.
Do not assume the two principal metrics can be combined into one “safety score.” If a business needs a single decision threshold, publish the component scores and explain the weighting. A lower unsafe-compliance rate purchased by refusing many benign requests is a product trade-off, not proof of universal superiority.
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Use varied rewrites
For each selected prompt, create paraphrases, translations, quoted text, distracting material, role-play framing and misleading “system” instructions. Test both directions: a harmful request should remain refused, while a benign request should remain useful after harmless wording changes. The joint OpenAI–Anthropic safety evaluation used roughly 20 variations for each selected question in its StrongREJECT v2 work, including language changes and misleading instructions, across a curated subset of 60 questions. Treat that as an example of a test design, not a universal quota.
Record whether a rewrite changes only style or also changes meaning. Otherwise an apparently successful attack may simply be a different policy case. For every variant, preserve the original expected outcome and note any new ambiguity introduced by translation or role-play.
Test instruction conflicts and untrusted content
If the assistant reads documents, webpages, emails or tool output, include prompt-injection cases. Put a malicious instruction inside otherwise relevant content and check whether the assistant follows the trusted request hierarchy instead of the embedded command. The instruction hierarchy report treats this as a distinct evaluation problem. A model can pass a standalone refusal test yet fail when an untrusted document tells it to ignore the policy.
Evaluate both reading and acting. A model that repeats an injection is a problem; a model that follows it to browse, execute code, send a message or modify a file presents a larger consequence. Record tool permissions, confirmation requirements and whether the assistant had access to secrets during each run.
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Vary modality and tool access
Repeat important cases with text, images and any enabled tools. A refusal that works in text may fail when harmful instructions are hidden in an image or returned by a tool. Record whether the model could browse, execute code, send messages or modify files; capability changes the severity of a failure. Include text-plus-image cases when the product supports them, and do not generalize a text-only result to other modalities.
Known jailbreak suites are stress tests, not proof against unknown attacks. The joint report explicitly cautions that its benchmark is limited by the range of variations and by automated-grader limitations. Include newly discovered attacks in regression testing, but keep their origin and selection criteria visible so the test does not become a hidden collection of cherry-picked failures.
Inspect the grader, not just the model
Automated judges are useful for scale but can misread quoted content, partial refusals, safe alternatives and subtle enabling details. Define a rubric with separate labels for:
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- Clear refusal with no actionable harmful content.
- Refusal that still leaks operational details.
- Safe, useful completion.
- Unnecessary refusal.
- Direct unsafe compliance.
Sample disputed cases for human review and measure agreement between reviewers. The joint OpenAI–Anthropic report says auto-grading difficulty materially affected results and that manual review indicated some measured differences were grader errors. Treat every score as an estimate produced by a particular rubric and judge, not as ground truth independent of evaluation.
Human review is most valuable for severe failures, category outliers, disagreements between judges and cases near the policy boundary. Give reviewers the policy and the response, but blind them to the model name when practical. Preserve adjudication notes; they reveal whether a policy sentence is ambiguous, a judge is inconsistent or the model is exploiting a loophole.
Evaluation cost is driven by prompt count, number of variants, modalities, tool runs and the share requiring human adjudication. There is no universal budget or sample-size rule. A smaller, carefully labeled set with manual review can be more informative than a huge set scored by an unreliable judge.
Report the scope so results are reproducible
Every published result should state:
- Exact model name, version or snapshot and evaluation date.
- Policy boundary, risk tier and intended deployment.
- Prompt sources, categories, labels and number of examples.
- Whether prompts were text-only, multimodal or tool-enabled.
- Adversarial transformations and how many variants were tested.
- Grader model or rubric, human-review sample and adjudication rules.
- Category-level scores, uncertainty or confidence intervals where available.
Without this information, a number cannot be transported safely to another model version, policy or user population. Compare assistants on identical prompts, variants and grading rules; do not rank them from different test distributions. Include the evaluation date because model behavior, policy settings and tool permissions can change without a change in product branding.
How to compare two assistants fairly
| Comparison axis | Fair question |
|---|---|
| Unsafe compliance | Which assistant gives less prohibited or materially enabling help on the same refuse set? |
| Benign over-refusal | Which one remains more useful on the same safety-adjacent set? |
| Ambiguous cases | Which one asks for clarification or gives a safer limited answer when appropriate? |
| Adversarial robustness | Does the decision survive the same paraphrases, translations, conflicts and injections? |
| Coverage | Were the same domains, languages, modalities and tool conditions tested? |
| Grading quality | Did both systems receive the same rubric, judge and human-review process? |
| Context | Are model version, policy boundary, risk tier and evaluation date aligned? |
A comparison is not fair if one provider is tested on ordinary traffic and the other on extreme edge cases, or if one policy permits a safe completion that the other policy requires it to refuse. Explain those policy differences instead of treating them as model defects.
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Published statistics are useful illustrations when their scope stays attached to the number. In the 2024 text-only table in OpenAI’s o1 system card, o1 scored 0.92 on not_unsafe for its challenging refusal evaluation and 0.94 on not_overrefuse for XSTest. Those results belong to OpenAI’s models, datasets and autograder; they are not a current cross-provider benchmark.
An Anthropic–OpenAI pilot reported inappropriate-refusal scores above the stated threshold in about 4% of o3 transcripts and under 1% of Claude Sonnet 4 transcripts in an extreme-edge-case scenario distribution. The figures are limited to that simulated distribution and the report’s judge and rubric. They do not describe ordinary traffic without supporting evidence.
OR-Bench’s large prompt counts similarly describe benchmark construction, not the number every evaluator should copy. The correct sample size depends on the policy categories, expected failure rate, desired uncertainty and the cost of human adjudication. A benchmark can be useful without being representative of every user or every attack.
A practical evaluation workflow
- Write the policy map. For every target category, specify refuse, answer and safe-completion examples, including the deployment’s risk assumptions.
- Assemble and label prompts. Balance disallowed, benign-adjacent and ambiguous cases; resolve disagreements before running the model.
- Run a fixed baseline. Capture the exact model version, settings, system instructions, tools, modality and date.
- Score both sides. Calculate unsafe compliance and benign over-refusal separately, then assess safe-completion quality.
- Generate adversarial variants. Add paraphrases, translations, quoted material, role-play, distracting text, instruction conflicts and injection content.
- Review failures. Manually inspect severe, disputed and category outlier cases; check judge agreement.
- Break down the result. Publish per-category, per-modality and per-variant scores instead of only an aggregate.
- Repeat after changes. Re-run the same suite after a model, policy, system-prompt or tool change, while adding new cases for newly observed failures.
Common evaluation mistakes
Rewarding blanket refusal
Counting every refusal as a success encourages the model to reject harmless requests. Always include benign controls and publish over-refusal.
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Using only obvious harmful prompts
Obvious examples test the easiest part of the boundary. Add natural language, dual-use cases and ordinary benign requests containing sensitive terms.
Calling one benchmark universal
A selected benchmark samples a particular policy, date, modality and attack set. It cannot establish performance on unseen attacks or another deployment.
Ignoring partial leakage
A response can begin with “I can’t help” and then provide enough procedural detail to enable harm. Score the content, not the opening phrase.
Trusting a single automated judge
Use human review for a sample, especially where quoted harmful text, safe alternatives or tool output make the label difficult.
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Comparing incomparable numbers
Different prompt distributions, policies, model versions and graders produce different legitimate scores. Align those variables before drawing a comparison.
Silently changing the policy
If reviewers revise labels after seeing model outputs, document the change and rerun affected items. Otherwise an apparent improvement may be a moving target.
Forgetting deployment consequences
A text-only refusal failure and an agent that takes an external action are not equivalent. Include permissions, confirmation gates and possible downstream effects in the severity assessment.
What a decision-ready conclusion looks like
State the finding in bounded terms: which model version, under which policy, on which prompt distribution and modality, achieved what unsafe-compliance and over-refusal results, with what adversarial coverage and review quality. Explain the important category failures and whether the result applies to ordinary use or only an extreme test set. A refusal is reliable only to the extent that this evidence shows accurate boundaries, useful behavior near those boundaries and resilience under the pressures your deployment will actually face.
Frequently Asked Questions
How do I know if an AI refusal is correct?
Check the response against the product’s written policy and context. It should block prohibited or materially enabling help, while still answering allowed requests or giving an appropriately limited safe completion. Judge the content, not just whether it begins with a refusal phrase.
Why does AI refuse harmless questions?
Safety classifiers can react to alarming words, quoted material or risky-sounding verbs without understanding the benign intent. Test translation, education, journalism, defensive and ordinary programming prompts to measure this over-refusal.
How do you test AI safety refusals?
Use labeled disallowed, benign-adjacent and ambiguous prompts; score unsafe compliance and over-refusal separately; create paraphrases, translations, instruction conflicts and prompt-injection variants; then manually review difficult cases.
Can an AI safety benchmark tell whether a refusal is reliable?
It can provide evidence for the tested model, policy, prompt distribution, modality, attack variants and grading method. It cannot prove behavior on unseen attacks or automatically generalize to another deployment.
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It is a bounded response to an ambiguous or dual-use request, such as high-level prevention, defensive guidance, legal context or a clarifying question, without providing actionable harmful details.
Is a higher refusal rate safer?
Not necessarily. A model can raise its refusal rate by rejecting benign requests. Compare unsafe compliance with benign over-refusal and report both instead of rewarding blanket refusal.
How many prompts and jailbreak variants are required?
There is no universal quota. OR-Bench used 80,000 seemingly toxic prompts, a hard subset of about 1,000 and 600 toxic controls, while a joint 2025 evaluation used roughly 20 variations for each question in a curated set of 60. Choose counts based on policy coverage, expected uncertainty and review capacity.
Should automated graders be trusted?
Use them for scale, but sample disputed and severe cases for human review. Official joint evaluation findings reported that auto-grading difficulty caused measurable errors, so a score is an estimate tied to its rubric and judge.
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Evaluate refusals in both directions: unsafe compliance must be low, and benign over-refusal must also be low. Add ambiguous cases, adversarial rewrites, multimodal and tool-use tests, prompt-injection checks, category breakdowns and human review. Report the exact model, policy, date, prompts and grader so readers know what the result does—and does not—show.
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