mlsanityData to 13 Oct 2026

Auditing Agent Harness Safety

arXiv 2605.14271 ↗First posted 14 May 2026cs.CL cs.CYcited by 9

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 28 of its 40 references that are in the map. First posted on arXiv 14 May 2026.

Builds on

28 of 40 references.

Closest ancestors · 12

Same direction, newest first

  1. 2026Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw6 Apr
  2. 2026ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis2 Apr
  3. 2026Agent Audit: A Security Analysis System for LLM Agent Applications24 Mar
  4. 2026GroupGuard: A Framework for Modeling and Defending Collusive Attacks in Multi-Agent Systems14 Mar
  5. 2026Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats12 Mar
  6. 2026AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systems12 Feb
  7. 2025TAMAS: Benchmarking Adversarial Risks in Multi-Agent LLM Systems7 Nov
  8. 2025OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents17 Jun
  9. 2024Agent-SafetyBench: Evaluating the Safety of LLM Agents19 Dec
  10. 2024AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents11 Oct
  11. 2024ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents9 Oct
  12. 2024AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents19 Jun

Other references · 16

  1. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,945
  2. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,365
  3. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,335
  4. 2026Kimi K2.5: Visual Agentic Intelligencecited by 472
  5. 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Taskscited by 313
  6. 2024PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Actioncited by 165
  7. 2026Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agentscited by 57
  8. 2026ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspacescited by 31
  9. 2026ClawBench: Can AI Agents Complete Everyday Online Tasks?cited by 29
  10. 2026ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watcherscited by 19
  11. 2026A Trajectory-Based Safety Audit of Clawdbot (OpenClaw)cited by 16
  12. 2026ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agentscited by 16
4 more
  1. 2026A Systematic Security Evaluation of OpenClaw and Its Variantscited by 13
  2. 2026Quantifying Trust: Financial Risk Management for Trustworthy AI Agentscited by 8
  3. 2026Emergent Risks in Generative Multi-Agent Systemscited by 5
  4. 2026Do Phone-Use Agents Respect Your Privacy?cited by 3

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 9.

9 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026From Failed Trajectories to Reliable LLM Agents: Diagnosing and Repairing Harness Flaws
  2. 2026Harness-Bench: Measuring Harness Effects across Models in Realistic Agent Workflows
  3. 2026Toward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement
  4. 2026Stop Comparing LLM Agents Without Disclosing the Harness
  5. 2026TRACES: Proactive Safety Auditing for Multi-Turn LLM Agents via Trajectory-State Modeling
  6. 2026Synthesizing Multi-Agent Harnesses for Vulnerability Discovery
  7. 2026Auditing Harness Tampering in Self-Improving Agents
  8. 2026AURA-Eval: Evaluation Framework for Acting Under Risk Awareness in LLM Agent Trajectories