RATION applies quantitative risk limits to AI agent access control
Original title: RATION: Towards Quantitative Risk-Based Access Control for AI Agents
Policies mix user-request constraints with risks calibrated from similar past tasks and enforce them during execution.
Researchers propose RATION, a risk-based access-control framework for AI agents that interact with external environments. It tracks measurable risks of agent actions and applies enforcement by risk type, aiming to avoid blanket action restrictions. Policies combine explicit constraints from the user request with implicit risks from historical executions of similar tasks. Experiments on four security benchmarks, including exposure to threats such as indirect prompt injection, are reported to preserve security and task utility.
Unconfirmed: Security and utility claims come from the authors' experiments.
- Enforcement depends on the type of risk accumulated during execution.
- Authors tested the framework across diverse models and environments.
- The design targets the security-utility trade-off of restricting agent actions.
From the original article
1 min read at arxiv.orgAI 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.
By Yuntao Du · arxiv.org
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