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This paper surveys 21 proposals for user-level permissions in AI agent systems, highlighting the necessity for tailored permissions policies that reflect individual user needs rather than a one-size-fits-all approach. It constructs a taxonomy that categorizes how these systems specify, derive, and enforce permissions, and evaluates five commercial agents against these frameworks. The findings reveal significant gaps in current implementations, emphasizing the need for more nuanced security measures in the deployment of AI agents.
User-level permissions in AI agents are not just a feature; they are essential for mitigating risks like unauthorized transactions and data leaks.
As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail. Prompt injection attacks, as well as hallucination, can cause agents to leak private information to third parties. As autonomous systems, agents also present the more active danger of performing sensitive tasks, such as bank transactions, without the user's intent or authorization. Recognizing this challenge, the agentic security community has developed numerous proposals for secure agentic systems. Much of this work has focused on product-level approaches, where agentic system developers determine and apply the same security policies and permissions to all users. Yet different users have different needs and preferences, necessitating support for user-level permissions policies in agentic AI systems. To understand how user-level permissions are handled in AI agent systems, we survey 21 proposals for agent permissions systems. From this review, we construct a taxonomy of how different systems specify user-level permissions policies, both at the user interface and internally; derive internal policies from user input; and enforce those policies at run-time. We then analyze five prominent commercial agents and compare their permissions handling to agentic permissions systems in the literature. We identify several high-level themes across the literature and commerical agents, as well as multiple gaps where future work is needed.
