RATION: Towards Quantitative Risk-Based Access Control for AI Agents
AI agents increasingly interact with external environments to complete complex user tasks. These interactions can lead to unexpected behavior and expose agents to security threats such as indirect prompt injection. Current agent security systems largely protect agents by restricting their actions, which can result in a substantial security-utility trade-off for complex, open-ended tasks. To address this, we propose a quantitative risk-based approach to access control for AI agents that regulates measurable risks accumulated during task execution. As a concrete realization of this approach, we introduce RATION, a security framework that tracks measurable risks associated with agent actions...
| Authors | Yuntao Du, Harsh Chaudhari, Yuetian Chen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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