HarnessAudit-Bench
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.
- Made by
- UC Santa Barbara, with UC Berkeley, UW-Madison, Stanford and Microsoft Research
- 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.
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:
- Claude Fable 5.2
-
Gemini 4 Carbon
- GPT-6 Astra
- Claude Opus 5.5
- and 16 more
- 1
Gemini 3.1 Pro 0.41
- 2 Claude Sonnet 4.6 0.32
- 3 GPT-5.4 0.32
- 4 GLM-5V-Turbo 0.31
- 5 Kimi K2.6 0.30
- 6 Claude Opus 4.6 0.29
- 7
Qwen3.5-Plus 0.29
Source: Official HarnessAudit-Bench leaderboard
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 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 | | 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.
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.
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
- 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%