HarmonyGuard: Toward Safety and Utility in Web Agents via Adaptive
  Policy Enhancement and Dual-Objective Optimization
Authors: name: Yurun Chen, name: Xavier Hu, name: Yuhan Liu, name: Keting Yin, name: Juncheng Li, name: Zhuosheng Zhang, name: Shengyu Zhang

Large language models enable agents to autonomously perform tasks in open web
environments. However, as hidden threats within the web evolve, web agents face
the challenge of balancing task performance with emerging risks during
long-sequence operations. Although this challenge is critical, current research
remains limited to single-objective optimization or single-turn scenarios,
lacking the capability for collaborative optimization of both safety and
utility in web environments. To address this gap, we propose HarmonyGuard, a
multi-agent collaborative framework that leverages policy enhancement and
objective optimization to jointly improve both utility and safety. HarmonyGuard
features a multi-agent architecture characterized by two fundamental
capabilities: (1) Adaptive Policy Enhancement: We introduce the Policy Agent
within HarmonyGuard, which automatically extracts and maintains structured
security policies from unstructured external documents, while continuously
updating policies in response to evolving threats. (2) Dual-Objective
Optimization: Based on the dual objectives of safety and utility, the Utility
Agent integrated within HarmonyGuard performs the Markovian real-time reasoning
to evaluate the objectives and utilizes metacognitive capabilities for their
optimization. Extensive evaluations on multiple benchmarks show that
HarmonyGuard improves policy compliance by up to 38% and task completion by up
to 20% over existing baselines, while achieving over 90% policy compliance
across all tasks. Our project is available here:
https://github.com/YurunChen/HarmonyGuard.