Benchmark Almanac

Agent harness misuse evaluation

Gemini 4 Argon 91.4% Only in system cards

Agents in a sandboxed Antigravity harness get malicious and benign tool-use requests on illegal acts, physical harm and scaled abuse. Reports safe action rate.

In-house Google DeepMind evaluation. The tasks are not public.

Made by Google DeepMind Released Oct 2026 Refusing harmful requests Tasks kept private Last in a system card Oct 2026 Last model added Oct 2026
Level
Everyday Most adults, without training
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.
1
Model scored in lab cards
1
System card reports it, from 1 lab

Gemini 4 Argon leads with 91.4%

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 Gemini 4 Argon 91.4%
#ModelScoreSettingDateSource
1 Gemini 4 Argon 91.4% Oct 2026 Model card

The best score passed 85% in Oct 2026

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% Near the ceiling (85%) Gemini 4 Argon 0% 20% 40% 60% 80% 100% Near the ceiling (85%) Gemini 4 Argon

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
13 Oct 2026 Gemini 4 Argon system card 91.4 Agent Harnesses, safe action rate 91.40%; helpfulness 68.07%, without out-of-model safeguards (Figure 4.8) 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%