Benchmark Almanac

Agentic coding malicious requests (Anthropic)

Claude Haiku 4.5 100% leads since Oct 2025 Only in system cards

Coding agents with standard coding tools receive 150 malicious coding requests prohibited by policy. Score is the refusal rate.

In-house Anthropic evaluation run since the Claude 4 release. The Claude Code malicious use evaluation is separate.

Made by Anthropic Released May 2025 Refusing harmful requests Tasks kept private Last in a system card Feb 2026 Last model added Feb 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
Agent task Many steps in a terminal, browser, desktop, app, game or robot.
5
Models scored in lab cards
5
System cards report it, from 1 lab

3 models share the top score of 100% among models released by Feb 2026

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.

The newest model with a score here came out in Feb 2026, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:

  1. 1 Claude Haiku 4.5 100%
  2. 2 Claude Opus 4.5 100%
  3. 3 Claude Sonnet 4.6 100%
  4. 4 Claude Opus 4.6 99.3%
  5. 5 Claude Sonnet 4.5 98.7%
#ModelScoreSettingDateSource
1 Claude Haiku 4.5 100% Oct 2025 Model card
2 Claude Opus 4.5 100% Nov 2025 Model card
3 Claude Sonnet 4.6 100% Feb 2026 Model card
4 Claude Opus 4.6 99.3% Feb 2026 Model card
5 Claude Sonnet 4.5 98.7% Sep 2025 Model card

2 models have held number one since its release in May 2025

Lab claims count from the day the card came out, independent runs from the model’s release.

Anthropic
Lab claims Passed 85% in Sep 2025, by Claude Sonnet 4.5

The best score passed 85% in Sep 2025

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 Best so far
0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Haiku 4.5 0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Haiku 4.5

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.

5 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
17 Feb 2026 Claude Sonnet 4.6 system card 100 Agentic coding malicious use evaluation, refusal rate on 150 malicious requests, no mitigations (Table 5.1.1.A) Safety
5 Feb 2026 Claude Opus 4.6 system card 99.3 Agentic coding, refusal rate on 150 malicious coding requests, without mitigations (Table 5.1.1.A) Safety
24 Nov 2025 Claude Opus 4.5 system card 100 Agentic coding, refusal rate on 150 malicious coding requests, without mitigations (Table 5.1.1.A) Safety
15 Oct 2025 Claude Haiku 4.5 system card 100 Agentic coding malicious use evaluation, safety score without safeguards (Table 3.1.1.A) Safety
29 Sep 2025 Claude Sonnet 4.5 system card 98.7 Agentic coding, safety score on 150 malicious coding requests without safeguards, failed 2 requests (Table 4.1.1.A) Safety

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%