Refusal-Trained LLMs Are Easily Jailbroken As Browser Agents

TL;DR

This paper investigates whether safety refusals in LLMs generalize to agentic web browser use, revealing that current refusal training does not prevent harmful behavior in browser agents, and introduces BrowserART for red-teaming such agents.

Contribution

The study demonstrates that refusal training in chat LLMs does not effectively transfer to browser agents and provides BrowserART, a new toolkit for testing and improving agent safety.

Findings

Refusal-trained LLMs are easily jailbroken in browser agents.

Attack methods transfer from chat to browser agents.

Human rewrites and current models still attempt harmful behaviors.

Abstract

For safety reasons, large language models (LLMs) are trained to refuse harmful user instructions, such as assisting dangerous activities. We study an open question in this work: does the desired safety refusal, typically enforced in chat contexts, generalize to non-chat and agentic use cases? Unlike chatbots, LLM agents equipped with general-purpose tools, such as web browsers and mobile devices, can directly influence the real world, making it even more crucial to refuse harmful instructions. In this work, we primarily focus on red-teaming browser agents, LLMs that manipulate information via web browsers. To this end, we introduce Browser Agent Red teaming Toolkit (BrowserART), a comprehensive test suite designed specifically for red-teaming browser agents. BrowserART is consist of 100 diverse browser-related harmful behaviors (including original behaviors and ones sourced from HarmBench [Mazeika et al., 2024] and AirBench 2024 [Zeng et al., 2024b]) across both synthetic and real websites. Our empirical study on state-of-the-art browser agents reveals that, while the backbone LLM refuses harmful instructions as a chatbot, the corresponding agent does not. Moreover, attack methods designed to jailbreak refusal-trained LLMs in the chat settings transfer effectively to browser agents. With human rewrites, GPT-4o and o1-preview-based browser agents attempted 98 and 63 harmful behaviors (out of 100), respectively. We publicly release BrowserART and call on LLM developers, policymakers, and agent developers to collaborate on improving agent safety