MiniScope: Authorizing Agents with Least-Privilege Permissions

Abstract:AI agents are increasingly granted autonomous access to sensitive user data and third-party services, making effective permission management a critical security challenge. Existing permission models, however, typically rely on flat permission structures that fail to balance security with usability: fine-grained confirmation induces user fatigue, while coarse-grained or persistent approval leads to overprivileged agents. To address this tradeoff, we propose a task-centric, hierarchical permission model that treats an agent as a delegate operating within a task-specific role instead of requiring a separate permission decision for every tool call. Building on this model, we present MiniScope, an end-to-end permission system for agents that automates permission-hierarchy discovery and enforces contextual least privilege at runtime. Our evaluation shows that MiniScope reduces simulated permission confirmations by 43.4%-89.4% for cautious and typical personas relative to per-tool prompting and mitigates all privilege-escalation attacks with negligible impact on utility and runtime. Applied to real-world deployments, MiniScope further uncovers six overprivileged connector configurations in ChatGPT and Claude.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2512.11147 [cs.CR]

(or arXiv:2512.11147v2 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2512.11147 Focus to learn more arXiv-issued DOI via DataCite

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