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

Ido Levy, Ben wiesel, Sami Marreed, Alon Oved, Avi Yaeli, Segev Shlomov

Keywords: LLM Agent, Web Agent, Trust, Benchmark

TL;DR: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents for Enterprise Scenarios

Abstract:

Recent advancements in Web agents have introduced novel architectures and benchmarks showcasing progress in autonomous web navigation and interaction. However, most existing benchmarks prioritize effectiveness and accuracy, overlooking factors like safety and trustworthiness—both essential for deploying web agents in enterprise settings.
We present ST-WebAgentBench, a benchmark designed to evaluate web agents' safety and trustworthiness across six critical dimensions, essential for reliability in enterprise applications. This benchmark is grounded in a detailed framework that defines safe and trustworthy (ST) agent behavior. Our work extends WebArena with safety templates and evaluation functions to rigorously assess safety policy compliance. We introduce the Completion Under Policy to measure task success while adhering to policies, alongside the Risk Ratio, which quantifies policy violations across dimensions, providing actionable insights to address safety gaps.
Our evaluation reveals that current SOTA agents struggle with policy adherence and cannot yet be relied upon for critical business applications. We open-source this benchmark and invite the community to contribute, with the goal of fostering a new generation of safer, more trustworthy AI agents. All code, data, environment reproduction resources, and video demonstrations are available at [blinded URL].

Primary Area: datasets and benchmarks

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Submission Number: 6718