Benchmark Almanac

Gray Swan Shade knowledge elicitation through chat

Claude Fable 5.1 1.00% Only in system cards

An automated attacker tries to elicit useful answers to 100 harmful and 100 dual-use cyber queries from a safeguarded model. Score is attack success rate.

External red teaming of deployed cyber safeguards. Gray Swan built the query set independently of Anthropic.

Made by Gray Swan Released Sep 2026 Jailbreaks and prompt injection Tasks kept private Last in a system card Sep 2026 Last model added Sep 2026
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
1
System card reports it, from 1 lab

Claude Fable 5.1 leads with 1.00%

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 Claude Fable 5.1 1.00%
#ModelScoreSettingDateSource
1 Claude Fable 5.1 1.00% Sep 2026 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% Claude Fable 5.1 0% 20% 40% 60% 80% 100% Claude Fable 5.1

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.

One system card reports it

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
1 Sep 2026 Claude Fable 5.1 system card 1 Gray Swan Shade attacker, knowledge elicitation through chat: useful information elicited for 2/200 queries (2 dual-use, 0 harmful), lower is better (3.5.2) 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%