Appropriate harmlessness (Anthropic)
Only in system cards
Models answer borderline and harmful prompts. Classifiers sort each response into helpful answer, policy violation, appropriate refusal or unnecessary refusal.
In-house Anthropic grading scheme run on internal harm datasets, WildChat and XSTest. Reference responses decide whether a refusal was necessary.
Made by Anthropic Released Feb 2025 Refusing harmful requests Tasks kept private Last in a system card Feb 2025
Last model added Feb 2025
- Level
- Everyday Most adults, without training
- 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
System card reports it, from 1 lab
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for Appropriate harmlessness (Anthropic) yet.
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.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 24 Feb 2025 | Claude 3.7 Sonnet system card | Appropriate harmlessness on internal out-of-distribution prompts, standard thinking, four categories with no overall: helpful answers 35.6%, appropriate refusals 51.3%, unnecessary refusals 12.5%, policy violations 0.6% (Figure 7 right). Rates on internal datasets, WildChat and XSTest are a chart without printed values (Figure 7 left). Text: unnecessary refusals down 45% standard and 31% extended versus Claude 3.5 Sonnet (new) (2) | Safety |
More in Safety and alignment
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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%