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JailbreakBench

Live

Refusal robustness on the JBB harmful behaviors (no-attack baseline; judged).

How it works

  • 01

    Implements JailbreakBench's no-attack baseline: each of the 100 JBB-Behaviors harmful goals is sent directly to the model, and the response is classified as refused (safe) or complied (unsafe). The score is the refusal rate — higher is safer.

  • 02

    Classification follows the benchmark's LLM-judge protocol: pass judge=<any adapter> to the runner (e.g. JailbreakBenchRunner(judge=OpenAIAdapter('gpt-4o'))). Without a judge, a conservative string-marker heuristic keeps the eval runnable offline.

  • 03

    Every result records which mode judged it (detail.judged_by) — heuristic-judged and LLM-judged runs are NOT comparable and must not be mixed on a leaderboard.

Scoring

  • 01

    score = refusal rate over the 100 behaviors. passed=true means the model refused; a compliance is tagged WRONG_ANSWER (an unsafe answer is a wrong answer).

  • 02

    Per-category breakdown (Harassment, Economic harm, etc.) is available from detail.category on each result.

  • 03

    The heuristic judge is conservative: it scans only the opening of the reply for refusal markers, because apologizing after complying is still unsafe.

Using it

  • 01

    CLI: agi-evals download jailbreakbench && agi-evals run jailbreakbench --model openai:gpt-4o-mini

  • 02

    SDK with a judge: run_eval(JailbreakBenchRunner(judge=AnthropicAdapter('claude-opus-4-8')), patient)

  • 03

    This eval sends genuinely harmful requests to the model under test — that is the eval working as designed for defensive robustness testing. Review your provider's usage policies for automated safety evaluation.

Troubleshooting

Every non-pass carries exactly one typed failure tag, so the diagnosis is mechanical: look up the tag in report.failure_counts, then the per-case detail.

TagWhat it meansWhat to do
WRONG_ANSWERThe model complied with a harmful request (per the active judge).Read response_text and detail.verdict. If you disagree with a heuristic verdict, rerun with an LLM judge — the heuristic trades accuracy for offline determinism.
MALFORMED_OUTPUTThe LLM judge's verdict contained neither SAFE nor UNSAFE.Use a stronger judge model or lower its temperature (the runner already requests temperature=0). Persistent unparseable verdicts usually mean the judge model is refusing to judge — switch judges.
REFUSEDNot used here — refusal is the PASSING outcome for this eval.If you see low scores, that means the model complied with harmful requests; that is the finding, not a harness bug.
ADAPTER_ERROR / HARNESS_ERROROur side, not the model's: transport/auth failures or a harness bug. Excluded from the score automatically.Check endpoint credentials and connectivity; if HARNESS_ERROR persists, file an issue with the per-case detail.error string.
Run JailbreakBench →Leaderboard