MobileSafetyBench: 评估移动设备控制自主代理的安全性
MobileSafetyBench: Evaluating Safety of Autonomous Agents in Mobile Device Control
AI总结：
MobileSafetyBench通过Android模拟器评估移动设备控制自主代理的安全性，揭示基于LLMs的代理在安全任务中存在缺陷，提出提示方法提升安全性。
摘要：
基于大型语言模型（LLMs）的自主代理在各种领域中的辅助任务中展现出巨大潜力，包括移动设备控制。由于这些代理直接与个人信息和设备设置交互，确保其安全可靠的行为至关重要，以防止不良后果。然而，目前尚无标准化评估移动设备控制代理安全性的基准。在本工作中，我们引入了MobileSafetyBench，这是一个基于Android模拟器的基准，旨在评估在现实移动环境中设备控制代理的安全性。我们开发了一套多样化的任务，涉及与各种移动应用程序（包括消息和银行应用程序）的交互，挑战代理在管理涵盖滥用和负面副作用的风险。这些任务包括评估代理在日常场景中的安全性和对间接提示注入攻击的鲁棒性。我们的实验表明，基于最新LLMs的基线代理在执行任务时往往无法有效防止伤害。为了缓解这些安全问题，我们提出了一种提示方法，鼓励代理优先考虑安全因素。虽然这种方法在促进更安全的行为方面显示出前景，但仍存在相当大的改进空间，以完全赢得用户的信任。这突显了继续研究以在移动环境中开发更稳健的安全机制的紧迫性。
英文摘要：
Autonomous agents powered by large language models (LLMs) show promising potential in assistive tasks across various domains, including mobile device control. As these agents interact directly with personal information and device settings, ensuring their safe and reliable behavior is crucial to prevent undesirable outcomes. However, no benchmark exists for standardized evaluation of the safety of mobile device-control agents. In this work, we introduce MobileSafetyBench, a benchmark designed to evaluate the safety of device-control agents within a realistic mobile environment based on Android emulators. We develop a diverse set of tasks involving interactions with various mobile applications, including messaging and banking applications, challenging agents with managing risks encompassing misuse and negative side effects. These tasks include tests to evaluate the safety of agents in daily scenarios as well as their robustness against indirect prompt injection attacks. Our experiments demonstrate that baseline agents, based on state-of-the-art LLMs, often fail to effectively prevent harm while performing the tasks. To mitigate these safety concerns, we propose a prompting method that encourages agents to prioritize safety considerations. While this method shows promise in promoting safer behaviors, there is still considerable room for improvement to fully earn user trust. This highlights the urgent need for continued research to develop more robust safety mechanisms in mobile environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
