CONTRA：可个性化代理的红队配置
CONTRA: Red-Teaming Configurations of Personalizable Agents
AI总结：
探讨代理配置与执行危险行为风险的关系，提出CONTRA算法，通过推理评估良性但危险的配置发现导致恶意行为的代理配置，构建数据集分析发现多数技能有恶意配置，CONTRA能成功识别部分危险配置。
摘要：
近期工具如OpenClaw将基于大语言模型的代理能力从简单对话系统扩展到完全自主代理。本文探索代理配置与无明确指令执行危险行为风险的相互作用，提出CONTRA算法，构建数据集分析发现当前代理在个性化方面安全性不足。
英文摘要：
Recent tools such as OpenClaw have extended the capabilities of LLM-based agents from simple dialog-based systems to fully autonomous agents. These systems allow personalization of the agent through modifiable internal files and the installation of skills. While this enables deployment in a wide range of settings and the automation of diverse tasks, greater capability and autonomy increases the risk of malicious actions being executed unintentionally. In this work, we explore the interplay between agent configuration and the risk of executing dangerous actions without explicit instruction. To this end, we propose CONfiguration Tree-search for Red-teaming Agents (CONTRA), an LLM-assisted tree-search algorithm that discovers agent configurations resulting in the execution of malicious actions. CONTRA works by reasoning about benign yet dangerous configurations and evaluating them in a simulated environment. We construct a dataset of the 473 most popular skills from a public repository, along with 2-5 corresponding malicious target actions per skill. In a large-scale analysis, we find that 75.1% of skills have at least one configuration resulting in the execution of a malicious action, most of which have not been detected as containing malicious content by existing scans. Overall, CONTRA successfully identifies a configuration leading to the execution of the target action in 39.2% of all tested cases. Our findings demonstrate that current agents provide insufficient safety with respect to personalization.
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。
