DPMF: A Dynamic Risk-Aware Permission Framework for Securing AI Agents Using Third-Party APIs

AI agents are moving from systems that only generate information to systems that can plan tasks, use tools, and perform operations through external APIs. This shift creates a security problem that traditional permission models do not fully address: an agent may hold broad privileges even when a particular task needs only a small part of that access. A valid token therefore does not automatically mean that every action produced by the agent is appropriate. This paper proposes DPMF (Dynamic Risk-Aware Permission Framework), an external authorization layer for AI agents that interact with third-party services. DPMF evaluates each proposed API action using contextual signals such as user-intent alignment, data sensitivity, permission severity, source trust, action reversibility, and behavioral deviation. The resulting risk level drives one of four decisions: Allow, Restrict, Human Approval, or Deny. The framework also separates the agent's ability to propose an action from the security layer's authority to approve it. The paper presents the framework architecture, threat model, risk-scoring approach, dynamic permission algorithm, case study, and an experimental methodology for comparing DPMF with static authorization. Because the framework has not yet been experimentally implemented, no numerical performance claims are presented as established results. Instead, measurable evaluation criteria and expected outcomes are defined.