Uncertainty-Aware, Risk-Adaptive Access Control for Agentic Systems using an LLM-Judged TBAC Model

• URL: http://arxiv.org/abs/2510.11414v1
• Date: Mon, 13 Oct 2025 13:52:33 GMT
• Title: Uncertainty-Aware, Risk-Adaptive Access Control for Agentic Systems using an LLM-Judged TBAC Model
• Authors: Charles Fleming, Ashish Kundu, Ramana Kompella,
• Abstract summary: This paper introduces an advanced security framework that extends the Task-Based Access Control (TBAC) model by using a Large Language Model (LLM) as an autonomous, risk-aware judge.<n>This model makes access control decisions not only based on an agent's intent but also by explicitly considering the inherent textbfrisk associated with target resources.
• Score: 11.50995963023462
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of autonomous AI agents within enterprise environments introduces a critical security challenge: managing access control for emergent, novel tasks for which no predefined policies exist. This paper introduces an advanced security framework that extends the Task-Based Access Control (TBAC) model by using a Large Language Model (LLM) as an autonomous, risk-aware judge. This model makes access control decisions not only based on an agent's intent but also by explicitly considering the inherent \textbf{risk associated with target resources} and the LLM's own \textbf{model uncertainty} in its decision-making process. When an agent proposes a novel task, the LLM judge synthesizes a just-in-time policy while also computing a composite risk score for the task and an uncertainty estimate for its own reasoning. High-risk or high-uncertainty requests trigger more stringent controls, such as requiring human approval. This dual consideration of external risk and internal confidence allows the model to enforce a more robust and adaptive version of the principle of least privilege, paving the way for safer and more trustworthy autonomous systems.

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