Benchmark Almanac

HarnessAudit-Bench

Gemini 3.1 Pro 0.41 leads since May 2026
Not in model cards

Agents run inside execution harnesses such as Claude Code, Codex and OpenClaw on 210 tasks across eight domains, from finance to software engineering, with safety rules built in. The audit reads the full trajectory through channels the agent cannot change. It scores whether the agent stays inside its permission and information-flow policies, completes checkpoints, and keeps both under prompt injection, vague goals and tool errors.

Tasks run in single-agent and multi-agent setups, and the paper compares 10 harness configurations and 3 multi-agent frameworks. Scores belong to a model and harness pair, not to a model alone. 52 Hugging Face upvotes and about 58 GitHub stars.

Released
May 2026
Kind
Refusing harmful requests
Level
Expert
Scored
Checked automatically
Answers
Agent task
Tasks
All public
Strongest model tested
GPT-5.4 #27 on the capability index
Last model added
April 2026
Scores refreshed
November 12th, 2026

In the authors’ words

LLM agents increasingly run inside execution harnesses that dispatch tools, allocate resources, and route messages between specialized components. However, a harness can return a correct, benign answer over a trajectory that accesses unauthorized resources or leaks context to the wrong agent. Output-level evaluation cannot see these failures, yet most safety benchmarks score only final outputs or terminal states, even though many violations occur mid-trajectory rather than at termination. The central question is whether the harness respects user intent, permission boundaries, and information-flow constraints throughout execution. To address this gap, we propose HarnessAudit, a framework that audits full execution trajectories across boundary compliance, execution fidelity, and system stability, with a focus on multi-agent harnesses where these risks are most pronounced. We further introduce HarnessAudit-Bench, a benchmark of 210 tasks across eight real-world domains, instantiated in both single-agent and multi-agent configurations with embedded safety constraints. Evaluating ten harness configurations across frontier models and three multi-agent frameworks, we find that: (i) task completion is misaligned with safe execution, and violations accumulate with trajectory length; (ii) safety risks vary across domains, task types, and agent roles; (iii) most violations concentrate in resource access and inter-agent information transfer; and (iv) multi-agent collaboration expands the safety risk surface, while harness design sets the upper bound of safe deployment.

Abstract of Auditing Agent Harness Safety (arXiv:2605.14271, CC0)
7
Models scored on its own leaderboard

Gemini 3.1 Pro leads with 0.41 among models released by April 2026

Best score per model from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best.

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

  1. 1 Gemini 3.1 Pro 0.41
  2. 2 Claude Sonnet 4.6 0.32
  3. 3 GPT-5.4 0.32
  4. 4 GLM-5V-Turbo 0.31
  5. 5 Kimi K2.6 0.30
  6. 6 Claude Opus 4.6 0.29
  7. 7 Qwen3.5-Plus 0.29

Source: Official HarnessAudit-Bench leaderboard

#ModelScoreSettingDateSource
1 Gemini 3.1 Pro 0.41 OpenClaw Feb 2026 Official leaderboard
2 Claude Sonnet 4.6 0.32 Claude Code Feb 2026 Official leaderboard
3 GPT-5.4 0.32 OpenClaw Mar 2026 Official leaderboard
4 GLM-5V-Turbo 0.31 OpenClaw Apr 2026 Official leaderboard
5 Kimi K2.6 0.30 OpenClaw Apr 2026 Official leaderboard
6 Claude Opus 4.6 0.29 Claude Code Feb 2026 Official leaderboard
7 Qwen3.5-Plus 0.29 OpenClaw Feb 2026 Official leaderboard

Number one over time

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

Google DeepMind
Own leaderboard Not tested since May 2026

Source: Official HarnessAudit-Bench leaderboard

Scores over time

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

On the benchmark’s own leaderboard
0.20 0.25 0.30 0.35 0.40 0.45 2026 0.20 0.25 0.30 0.35 0.40 0.45 2026

Source: Official HarnessAudit-Bench leaderboard

No lab model card reports it yet

We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.

More in Safety and alignment

All 465 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%
  • 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. Gemini 4 Carbon 0%
  • DistressBench In use Models reply as a chatbot to 718 simulated users in psychological crisis, in single-turn and multi-turn chats. Three judge models score replies against clinician-written rubrics. Muse Spark 1.2 88.4%
  • Lakera B3 (Backbone Breaker Benchmark) In use Crowdsourced prompt-injection attacks hit backbone LLMs inside agent threat snapshots at three defense levels. The score is attack resistance, 100 minus the board's vulnerability percentage. Claude Opus 4.5 89%
  • IHEval In use Models face conflicts between system, user and tool instructions. Reports a composite score for following the higher-priority instruction. GPT-4o 70%