让AI工具调用更安全:按需授权,用短期证书控制权限范围。
Intent-Governed Tool Authorization for AI Agents
- 基于请求生成临时权限证书,动态缩小工具可用范围。
- 实测零意外执行风险,但仍有9.1%~27.3%未执行的越权请求。
- 适合关注AI安全、权限管控的开发者与系统设计者。
工具型AI代理通常使用静态权限凭证,其权限超出用户当前需求。本文提出意图驱动访问控制(IGAC),一种服务器端授权层,将可信请求转化为短期意图证书,缩小静态授权的工具清单,并在执行前验证拟用工具及操作效果。IGAC无法突破静态策略授权;权限受限还依赖证书真实性与效应边界合理性。我们在OpenPort治理框架上评估了可复用的IGAC路径,涵盖端点测试、176个基于运行时的合成任务、真实模型分类器与规划器原型、306次端到端模型-任务运行试验,以及36次基准化外部子集测试。确定性运行对比中,参考证书的IGAC将综合暴露或路径指标从1.0000降至0;端到端模型运行中,组合IGAC-OpenPort路径未发生任何已完成的不安全执行,尽管不安全接受授权仍为0.0909-0.2727,且所有残余案例均为未执行的草稿。基于追踪的归一化反事实分析可消除该残余授权,但付出显著性能代价。结果支持静态策略不扩张,指出证书精度是当前主要瓶颈。
原文摘要 · Abstract (English)
Tool-using AI agents commonly operate under integration credentials whose static permissions exceed a user's current request. We present Intent-Governed Access Control (IGAC), a server-side authorization layer that converts a trusted request into a short-lived intent certificate, narrows the statically authorized tool manifest, and checks proposed tool and payload effects before execution. IGAC cannot grant authority outside static policy; confinement to the request additionally depends on certificate fidelity and sound effect bounds. We evaluate a reusable IGAC path over an OpenPort governance substrate using endpoint tests, 176 runtime-backed synthetic tasks, real-model classifier and planner pilots, 306 end-to-end model-task runtime trials, and a 36-trial benchmark-shaped external subset. In the deterministic runtime comparison, reference-certificate IGAC reduces the archived composite exposure-or-path indicator from 1.0000 to 0. In the end-to-end model runs, the combined IGAC-OpenPort path records no completed unsafe executions, although unsafe accepted authority remains 0.0909-0.2727 and every residual case is a non-executed draft. A trace-backed normalizer counterfactual removes this residual authority at substantial utility cost. The results support static-policy non-expansion and identify certificate precision as the principal remaining bottleneck.
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