X-AgentRed: Red-Teaming Personal Agents in Evolving Multi-Service Ecosystems· X-AgentRed：在多服务生态系统中对个人代理进行红队测试
Personal agents increasingly connect to services across the web and applications by using tools. As the effects of their actions accumulate and interact across services, unintended or harmful outcomes can arise when agents fulfill benign user requests. Proactively identifying these compositional failures requires systematically exploring diverse cross-service paths. Existing red-teaming methods often simply prompt LLMs to identify potentially harmful paths from task and environment descriptions or decompose harmful objectives specified by humans or LLMs into seemingly benign action combinations. However, generation is biased toward patterns learned during training, potentially favoring familiar tool combinations and leaving novel cross-service interactions underexplored. To address this, w
针对个人代理在多服务系统中的潜在风险，提出了一种新型红队测试方法。
- 核心方法
- X-AgentRed 通过系统性地探索多样的跨服务路径，主动识别并测试个人代理的组合性失败，减少因训练偏差导致的路径探索不足问题。
- 适合谁读
- 研究者和工程师
- 要解决的问题
- 个人代理在多服务生态系统中执行任务时，可能会因为不同服务之间的交互而产生意外或有害的结果，现有红队测试方法对此探索不足。
- 关键实验
- 在多个多服务生态系统中进行了实验，验证了 X-AgentRed 框架的有效性和鲁棒性。
- 主要贡献
- 提出了一种新的红队测试框架，能够更全面地探索个人代理在多服务生态系统中的潜在风险路径。
- 意义与局限
- 该方法提高了个人代理系统的安全性，减少了因跨服务交互产生的潜在风险。然而，其大规模应用的可行性和成本效益仍需进一步研究。