受限于语义任务到范围匹配的智能体委托授权
Delegated Authorization for Agents Constrained to Semantic Task-to-Scope Matching
AI总结：
针对智能体委托授权权限过宽的风险，提出一种通过语义检查颁发最小范围令牌的授权模型，并构建ASTRA数据集以基准测试任务与范围的语义匹配。
摘要：
授权大语言模型驱动的智能体动态调用工具和访问受保护资源会带来重大风险，因为当前的委托授权方法授予了过于宽泛的权限，并允许智能体访问工具，使其能够在预期任务范围之外操作。我们引入并评估了一种委托授权模型，使授权服务器能够对受保护资源的访问请求进行语义检查，并颁发仅限于智能体分配任务所需最小范围集合的访问令牌。鉴于目前缺乏以委托授权流程为中心的数据集，特别是针对给定任务同时包含语义上合适和不合适的范围请求，我们引入了ASTRA，这是一个用于对任务和范围之间的语义匹配进行基准测试的数据集和数据生成管道。我们的实验展示了基于模型的匹配的潜力和当前局限性，特别是随着任务完成所需范围数量的增加而面临的挑战。我们的结果强调，需要进一步研究语义匹配技术，以实现针对多智能体和工具增强应用的意图感知授权，包括细粒度控制，如基于任务的访问控制（TBAC）。
英文摘要：
Authorizing Large Language Model driven agents to dynamically invoke tools and access protected resources introduces significant risks, since current methods for delegating authorization grant overly broad permissions and give access to tools allowing agents to operate beyond the intended task scope. We introduce and assess a delegated authorization model enabling authorization servers to semantically inspect access requests to protected resources, and issue access tokens constrained to the minimal set of scopes necessary for the agents' assigned tasks. Given the unavailability of datasets centered on delegated authorization flows, particularly including both semantically appropriate and inappropriate scope requests for a given task, we introduce ASTRA, a dataset and data generation pipeline for benchmarking semantic matching between tasks and scopes. Our experiments show both the potential and current limitations of model-based matching, particularly as the number of scopes needed for task completion increases. Our results highlight the need for further research into semantic matching techniques enabling intent-aware authorization for multi-agent and tool-augmented applications, including fine-grained control, such as Task-Based Access Control (TBAC).
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。
