ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents

ICLR 2026 Conference Submission13776 Authors

Keywords: web agents, safety, trustworthiness, benchmark, policy compliance, enterprise workflows, Completion Under Policy, CuP, Risk Ratio, human-in-the-loop, policy hierarchy, robustness, error handling, evaluation, agentic systems, LLM-based agents, autonomous browsing

TL;DR: ST-WebAgentBench is a policy-aware benchmark with new metrics (CuP, Risk Ratio) that evaluates web agents’ safety and trustworthiness across 222 enterprise-style tasks, revealing large gaps between raw completion and policy-compliant success.

Abstract: Autonomous web agents solve complex browsing tasks, yet existing benchmarks measure only whether an agent finishes a task, ignoring whether it does so safely or in a way enterprises can trust. To integrate these agents into critical workflows, safety and trustworthiness (ST) are prerequisite conditions for adoption. We introduce **ST-WebAgentBench**, a configurable and easily extensible suite for evaluating web agent ST across realistic enterprise scenarios. Each of its 222 tasks is paired with ST policies, concise rules that encode constraints, and is scored along six orthogonal dimensions (e.g., user consent, robustness). Beyond raw task success, we propose the Completion Under Policy (CuP) metric, which credits only completions that respect all applicable policies, and the \textit{Risk Ratio}, which quantifies ST breaches across dimensions. Evaluating three open state-of-the-art agents reveals that their average CuP is less than two-thirds of their nominal completion rate, exposing critical safety gaps. By releasing code, evaluation templates, and a policy-authoring interface, ST-WebAgentBench provides an actionable first step toward deploying trustworthy web agents at scale.

Primary Area: datasets and benchmarks

Submission Number: 13776