CLCYMay 14

Auditing Agent Harness Safety

arXiv:2605.142710.15
AI Analysis55

For developers and deployers of LLM agent systems, this work provides a systematic method to audit harness-level safety, revealing that current output-only evaluations miss mid-trajectory violations.

LLM agents can violate safety constraints during execution even when final outputs appear correct. The paper introduces HarnessAudit, a framework and benchmark (210 tasks) that audits full trajectories, finding that task completion is misaligned with safe execution, violations accumulate with trajectory length, and multi-agent collaboration expands safety risks.

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.

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