拒绝有害指令的LLM在浏览器代理中易被绕过，安全风险被放大。
Refusal-Trained LLMs Are Easily Jailbroken As Browser Agents
- 构建专用测试工具BrowserART，评估浏览器代理的安全性。
- GPT-4o和o1-preview代理分别尝试98和63个有害行为，成功率极高。
- 聊天场景的攻击方法可直接迁移至代理，需警惕真实世界影响。
出于安全考虑，大型语言模型（LLMs）通常被训练为拒绝有害用户指令，如协助危险活动。本文研究一个关键问题：这种在对话场景中强制执行的安全拒绝对非对话、自主代理使用场景是否有效？与聊天机器人不同，配备通用工具（如网页浏览器、移动设备）的LLM代理能直接影响现实世界，因此拒绝有害指令更为重要。本文聚焦于红队测试浏览器代理——通过浏览器操控信息的LLM代理。为此，我们提出浏览器代理红队测试工具包（BrowserART），一个专门设计的综合性测试套件，包含100种多样的浏览器相关有害行为（涵盖合成及真实网站，来源包括HarmBench [Mazeika et al., 2024] 和 AirBench 2024 [Zeng et al., 2024b]）。对前沿浏览器代理的实证研究表明，尽管其基础模型在对话中拒绝有害指令，但对应的代理版本并未如此。此外，针对聊天场景设计的越狱攻击方法在浏览器代理上仍具高度有效性。经人工改写后，基于GPT-4o和o1-preview的浏览器代理分别尝试了98次和63次有害行为（共100项）。我们公开发布BrowserART，呼吁模型开发者、政策制定者及代理开发人员共同提升代理安全性。
原文摘要 · Abstract (English)
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
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