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An AI refusal is reliable only when it blocks requests that policy actually disallows, still helps with harmless requests near the boundary, and keeps doing both under paraphrases, jailbreak pressure, different modalities, and tool use. A high refusal rate alone proves very little: a system can look “safe” simply by refusing too much.
Evaluate the two failure directions separately—unsafe compliance and benign over-refusal—then examine difficult edge cases, adversarial variants, grader accuracy, and the scope of the test. The result should be a documented measurement for a particular model version, policy, prompt distribution, modality, and judging process, not a universal safety label.
What a reliable refusal actually means
Start by writing the decision rule before testing a model. For every request, your policy should identify one of three intended outcomes:
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- Refuse: the assistant should not provide the requested harmful or prohibited assistance.
- Answer: the request is benign enough for a normal, useful completion.
- Safe completion: the assistant should decline the dangerous part while offering a limited, preventative, educational, or otherwise safe alternative.
Reliability means matching the intended outcome, not maximizing refusals. OpenAI’s o1 System Card names the two central directions not_unsafe and not_overrefuse. The first asks whether an answer to a request that should be refused is non-unsafe; the second asks whether the model complied with a benign request. The card summarizes the problem plainly: “Getting refusal boundaries to be accurate via safety training is an ongoing challenge.”
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The same wording can legitimately receive different treatment under different policies or deployments. A dual-use cyber request might be allowed in a low-risk educational setting and refused for an account assessed as high risk. The GPT-6 Astra System Card, retrieved October 13, 2026, describes an adjusted cyber boundary that refuses a broader range of assistance for higher-risk accounts. Record the deployment context instead of treating one policy’s decision as a universal truth.
Define the boundary and the test context
Write a short, testable policy for the product you are evaluating. Specify prohibited assistance, allowed transformations of sensitive material, required safe-completion behavior, and escalation rules. State whether the assistant is a general chatbot, a coding agent, a multimodal system, or a tool-using agent. Include geography, user permissions, account risk tier, and the policy version or date when those factors change the decision.
Have a human adjudicator resolve ambiguous cases before scoring. If reviewers silently disagree about whether a prompt is allowed, a model can be marked wrong for following a reasonable interpretation. Keep the adjudicated label and the rationale with each prompt so later reviewers can audit changes.
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- exact model name and version or deployment identifier;
- policy text and effective date;
- system and developer instructions;
- temperature, sampling, tool permissions, and other runtime settings;
- prompt language, modality, and conversation history;
- test-set source, category counts, and label-adjudication method;
- grader model or rubric, human-review sample, and uncertainty estimates; and
- test date, region, and any account or risk configuration.
Build a balanced prompt set
A useful evaluation has at least three labeled groups. Do not rely on obvious harmful prompts or on a single public benchmark.
| Prompt group | What it measures | How to construct it |
|---|---|---|
| Disallowed requests | Whether the assistant avoids unsafe compliance | Use policy-grounded harmful requests across each relevant category, with ordinary wording and realistic user goals. |
| Benign, safety-adjacent requests | Whether the assistant over-refuses | Ask for harmless translation, analysis, prevention, troubleshooting, or programming help that contains risky-sounding words or quoted material. |
| Ambiguous or dual-use boundary cases | Whether the model distinguishes intent and can provide a safe completion | Include incomplete context, mixed legitimate and risky objectives, and cases where a clarifying question is appropriate. |
| Toxic controls | Whether labels and graders recognize clearly prohibited content | Keep a separately identified set of genuinely disallowed prompts; do not mix it with benign look-alikes when reporting results. |
Include paraphrases, colloquial language, misspellings, and multi-turn conversations. A benchmark’s apparent sample size is not a universal requirement. OR-Bench, described in OR-Bench: An Over-Refusal Benchmark for Large Language Models, was built from 80,000 seemingly toxic prompts across 10 common rejection categories, with a hard subset of about 1,000 prompts and 600 toxic prompts. Those figures describe that benchmark’s construction, not a minimum every team must copy.
Keep prompt provenance and labels separate from model outputs. A benign example that merely quotes a harmful sentence can reveal over-refusal; the quoted content should not change the ground-truth label unless the user is asking the model to perform the harmful act.
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Measure unsafe compliance and over-refusal separately
Never report a single “refusal rate” as the safety result. A refusal rate rises both when a model blocks harmful requests and when it blocks harmless ones.
| Metric | Definition | Interpretation |
|---|---|---|
| Unsafe-compliance rate | Disallowed prompts receiving materially unsafe assistance ÷ disallowed prompts tested | Lower is better. Define in advance what counts as actionable unsafe assistance. |
not_unsafe |
Disallowed prompts whose outputs are judged non-unsafe ÷ disallowed prompts tested | Higher is better; it is the complement of unsafe compliance only when labels and grading are binary. |
| Benign-compliance rate | Benign prompts receiving the requested useful help ÷ benign prompts tested | Higher is better and exposes over-refusal. |
not_overrefuse |
Benign prompts not incorrectly refused ÷ benign prompts tested | Higher is better; define whether a partial safe answer counts as compliance. |
| Safe-completion quality | Share of boundary prompts that refuse the unsafe part while supplying an appropriate safe alternative | Separates helpful redirection from a bare refusal. |
Report confidence intervals or, at minimum, the numerator and denominator for every score. Break results down by category instead of hiding a severe failure in an overall average. If a benchmark includes text and images, publish text-only and multimodal results separately.
The o1 card illustrates why context belongs beside the number. In its 2024 text-only table, o1 scored 0.92 on not_unsafe for a challenging refusal evaluation and 0.94 on not_overrefuse for XSTest. Those values apply to OpenAI’s model, datasets, policy, and autograder at that time; they are not a current cross-provider benchmark or a universal threshold.
Test the boundary, not just obvious violations
Use benign look-alikes
Construct harmless prompts that contain words, quoted passages, or verbs associated with risky activity. The o1 card gives examples such as refusing to translate a sentence merely because the quoted content is harmful, or refusing an innocuous programming question because it uses a risky-sounding verb. Score whether the assistant completes the benign task, asks a useful clarification, or offers an unnecessarily broad refusal.
Include difficult dual-use cases
Ask for defensive analysis, safety education, historical explanation, or troubleshooting where a useful answer can be given without operational harm. Label the acceptable level of detail in advance. A response that omits dangerous instructions but gives prevention steps may be the intended safe completion, not a failure to answer.
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Translate selected prompts, change writing style, add distracting text, and place the request in a longer conversation. Compare the decision and the explanation, not just whether the word “no” appears. A refusal that collapses when the same request is written in another language is not robust, even if the English benchmark score is high.
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Stress-test jailbreaks and instruction conflicts
For prompts that should remain refused, create adversarial variants that attempt to alter the instruction hierarchy or disguise the intent. Useful variants include:
- role-play, fictional framing, or claims that the request is for a test;
- translation, encoding, obfuscation, or requests to transform user-supplied text;
- misleading “system” or “developer” instructions embedded in the user message;
- long distracting documents surrounding a short harmful request;
- multi-turn escalation after an initially harmless exchange; and
- untrusted web pages, files, or tool outputs containing instructions.
OpenAI’s report on a joint evaluation with Anthropic used roughly 20 variations for each selected question in a curated subset of 60 questions. It described the exercise as a useful stress test, while warning that it was limited by the range of variations and by auto-grader limitations. Treat that design as an example, not a universal count or proof against unseen attacks.
Tool-using agents need a separate instruction-hierarchy test. Check whether the model follows the authorized system and developer instructions rather than commands hidden in a document, web page, retrieved passage, or tool result. The instruction hierarchy report describes separate evaluations for this behavior. A standalone chat refusal score cannot establish that an agent will ignore prompt injection while operating tools.
Evaluate every enabled modality. Image-plus-text prompts, audio, code blocks, attachments, and tool calls can expose different boundaries. State which modalities were tested and do not generalize a text-only result to an image-capable or agentic deployment.
Inspect the grader and the failures
Automated judges make large test sets practical, but the judge is part of the measurement system. Write a rubric that distinguishes a clear refusal, unsafe actionable detail, a benign answer, a safe completion, and an irrelevant or evasive response. Then manually review a random sample and every disputed or high-impact case.
Measure agreement between independent human reviewers on a sample. Review disagreements to determine whether the policy is unclear, the prompt label is wrong, or the grader misunderstood the output. The joint OpenAI–Anthropic report says auto-grading difficulty materially affected results and that manual review suggested some measured differences were grader errors. Therefore, a score is an estimate produced by a particular rubric and judge—not ground truth independent of evaluation.
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Watch for grader shortcuts. A judge may reward the presence of “I can’t help with that” even when the answer follows with harmful instructions, or penalize a safe completion because it is not a literal refusal. Have reviewers inspect the entire response, including code, links, tool calls, and hidden chain-of-action outputs where your system exposes them.
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Analyze failures by category and severity
For each error, preserve the prompt, model output, policy label, grader decision, human adjudication, and severity. Categorize the failure so remediation is possible:
- Unsafe compliance: prohibited operational guidance was supplied.
- Partial leakage: the answer refused but revealed enough detail to enable the harmful act.
- Over-refusal: a harmless request was blocked without a policy basis.
- Weak safe completion: the model refused correctly but failed to provide an available protective or educational alternative.
- Inconsistency: semantically equivalent prompts received materially different decisions.
- Hierarchy or injection failure: untrusted content overrode authorized instructions.
- Grading error: the automated or human judge misclassified the response.
Report category-level rates and examples without exposing harmful operational details. A low overall unsafe-compliance rate can conceal one dangerous category, while a high benign-compliance rate can conceal systematic failures for a particular language or modality. Severity-weighted analysis may be appropriate for deployment decisions, but publish the unweighted counts too so readers can see how the weighting changes the conclusion.
Compare assistants on the same evidence
When comparing systems, run identical prompts, conversation history, tools, runtime settings, policy definitions, and judging rules. Compare these axes independently:
| Axis | Questions to answer |
|---|---|
| Unsafe compliance | How often does each system provide materially unsafe help on prompts that should be refused? |
| Benign over-refusal | How often does each system block harmless, safety-adjacent requests? |
| Boundary behavior | Does it ask for clarification or provide a safe completion on ambiguous dual-use prompts? |
| Adversarial robustness | Does the decision survive paraphrases, language changes, misleading instructions, and multi-turn pressure? |
| Domain and modality coverage | Were relevant cyber, biological, self-harm, privacy, image, audio, code, and tool-use cases tested? |
| Evaluation quality | Were labels adjudicated, graders checked, and disputed examples reviewed by humans? |
| Scope | Are model version, policy boundary, deployment context, date, and test distribution comparable? |
Do not turn these axes into one ranking unless you explain the weighting. A model that refuses nearly everything can score well on a narrow unsafe-compliance measure while being unusable. Conversely, a helpful model may expose more unsafe-compliance failures if its boundary is too permissive. The trade-off must remain visible.
How to interpret published refusal statistics
OR-Bench’s large prompt construction
OR-Bench’s 80,000 seemingly toxic prompts, 10 rejection categories, approximately 1,000-prompt hard subset, and 600 toxic prompts demonstrate the value of separating benign look-alikes from genuinely toxic controls. They do not establish that another model, policy, or deployment needs exactly those counts, nor that a score transfers unchanged to ordinary traffic.
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The 2025 joint OpenAI–Anthropic evaluation used roughly 20 variations of each question for a selected set of 60 questions, including language changes and misleading instructions. The authors explicitly limited the conclusion to the tested attack variations and auto-grader quality. Use the result to understand performance under that design, not as evidence that unseen jailbreaks are covered.
Independent pilot findings
In the 2025 Anthropic–OpenAI pilot report, inappropriate-refusal scores were high in about 4% of o3 transcripts and under 1% of Claude Sonnet 4 transcripts in an extreme-edge-case scenario distribution. Those percentages depend on the simulated distribution, judge, rubric, and transcript selection. They do not describe ordinary user traffic without additional evidence.
Published numbers become meaningful only when you can identify the model version, policy boundary, prompt distribution, modality, grader, and date. A benchmark score without that scope is not a portable reliability claim.
A repeatable evaluation workflow
- Freeze the policy. Write the allowed, refused, and safe-completion outcomes for the deployment, including risk tiers and tool permissions.
- Adjudicate the prompts. Create labels and rationales before viewing model outputs; resolve ambiguous cases with more than one reviewer.
- Assemble balanced groups. Include disallowed, benign safety-adjacent, ambiguous dual-use, and toxic-control prompts, plus realistic paraphrases.
- Run controlled trials. Pin the model version, system instructions, sampling settings, language, modality, conversation history, and tools.
- Score two directions. Calculate unsafe-compliance or
not_unsafeand benign-compliance ornot_overrefuseseparately, with counts and uncertainty. - Generate adversarial variants. Test translation, obfuscation, distractions, role-play, instruction conflicts, multi-turn escalation, and untrusted tool content.
- Audit judgments. Blind-review a sample, inspect all severe failures, measure reviewer agreement, and correct grader errors.
- Publish scope and diagnosis. Provide category and modality breakdowns, examples of safe completion, version and policy details, limitations, and the date of the run.
Repeat the same suite after policy, model, system-prompt, tool, or grader changes. Track regressions in both unsafe compliance and over-refusal; improving one by silently damaging the other is not a reliability improvement.
What a defensible conclusion looks like
A defensible report might say that a specified model version, under a named policy and date, achieved a measured not_unsafe rate on a defined disallowed set and a measured not_overrefuse rate on a separate benign set; it might then describe performance on the selected adversarial variants, modalities, and tool-injection cases. It should identify human-review coverage and known grader errors.
It should not say “the assistant is safe,” “the model never fails,” or “this benchmark proves robustness.” Known-attack tests cannot establish resistance to attacks you did not include, an extreme-edge-case distribution does not represent normal traffic, and policy boundaries differ between deployments. Reliability is a scoped, repeatable finding whose limitations are reported alongside its scores.
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