HazardAuditor: From Executable Threats to Safer Computer-Use Agents

2026-09-14

AI Summary

本文提出HazardAuditor框架，通过标准化不同代理的行为并优化安全决策的学习过程来提高计算机使用代理的安全性。

Abstract

Computer-use agents increasingly interact with browsers, terminals, file systems, and external services, introducing safety risks that emerge through runtime behavior rather than generated content alone. Existing guard models target static prompts and responses and are poorly suited to agent execution; existing executable safety platforms produce evaluation verdicts rather than the normalized supervision a guard model needs to learn across heterogeneous agent frameworks. We introduce HazardAuditor, an execution-grounded framework that closes both gaps. Its infrastructure runs heterogeneous agents (Claude Code, Codex, Hermes, and OpenClaw) in controlled environments and normalizes their interactions into a canonical event representation for cross-framework supervision. We further observe that token-level post-training objectives create a structural mismatch for generative guards, causing longer rationales to dominate gradient updates. Guard Policy Optimization (GuardPO) addresses this by converting deterministic safety outcomes into sequence-level advantages and normalizing rationale and verdict regions, making the safety decision the effective unit of optimization. Across multiple benchmarks and heterogeneous computer-use systems, HazardAuditor improves accuracy by up to 16.5 percentage points over the strongest prior guard. Code, models, and evaluation artifacts will be available at https://yunhao-feng.github.io/HazardAuditor/.

Problem

• Computer-use agents
• Safety risks
• Runtime behavior
• Guard models
• Heterogeneous agent frameworks

Innovation

• execution-grounded framework
• cross-framework supervision
• Guard Policy Optimization (GuardPO)
• safety decision