Benchmark Almanac

Gray Swan Jailbreak Arena

o3-mini 3.60% Only in system cards

Human attackers try to jailbreak models for violent, self-harm and malicious code outputs. Scored as attack success rate per category.

Run by Gray Swan for OpenAI. Harmful text and image outputs are judged with the OpenAI moderation API.

Made by Gray Swan AI Released Dec 2024 Jailbreaks and prompt injection Tasks kept private Last in a system card Jan 2025 Last model added Jan 2025
Level
Expert Specialists with years of training, often over hours of work
Scored
Graded by a model A language model scores each response, usually against a rubric.
Answers
Open-ended Free-form text, documents, designs or media judged as a whole.
1
Model scored in lab cards
2
System cards report it, from 1 lab

o3-mini leads with 3.60%

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

  1. 1 o3-mini 3.60%
#ModelScoreSettingDateSource
1 o3-mini 3.60% Jan 2025 Model card

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
0% 20% 40% 60% 80% 100% 2025 o3-mini 0% 20% 40% 60% 80% 100% 2025 o3-mini

Labs report it only in system cards

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.

2 system cards report it, from 1 lab

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
31 Jan 2025 o3-mini system card 3.6 Jailbreak Arena (Gray Swan), average user attack success rate, o3-mini-near-final-checkpoint (4.3.2) Robustness
5 Dec 2024 o1 system card Jailbreak Arena (Gray Swan), attack success rate 6% harmful text, 5% image-text, 5% malicious code, no overall score (4.4.2) Robustness

More in Safety and alignment

All 444 in Safety and alignment
  • FORTRESS In use Paired adversarial and benign national-security misuse prompts. Rubric-graded risk score on adversarial and over-refusal score on benign prompts. Claude Opus 5.5 5.78%
  • StrongREJECT In use Forbidden-prompt jailbreak benchmark with a rubric grader that scores how useful a non-refusal actually is. Inkling 98.6%
  • AgentDojo In use Agents do tool-use tasks while tools and data carry prompt injections. Attack success rate counts hijacked runs. Grok 4 Fast 0%
  • MASK In use Measures whether a model contradicts its own elicited beliefs under pressure to lie. Honesty separated from accuracy. Muse Spark Open 98.1%
  • Gray Swan IPI Arena benchmark In use Indirect prompt-injection attacks from public red-teaming competitions replayed on tool, coding and browser agents. Attack success rate at k attempts. GPT-6 Astra 8.50%
  • Agentic misalignment suite (Anthropic) In use Agents in simulated company settings face threats or goal conflicts that invite blackmail, leaking or sabotage. Score is the misaligned action rate. Claude Sonnet 4.5 0%