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

arXiv: https://arxiv.org/abs/2510.11414 · HTML: https://mlsanity.com/paper/2510.11414/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 2 of 2)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)

## Built on by (most cited first, 4 of 4)

- [SoK: Trust-Authorization Mismatch in LLM Agent Interactions](https://mlsanity.com/paper/2512.06914/) (2025)
- [ROPE: Routed Origin Policy Enforcement against Indirect Prompt Injection](https://mlsanity.com/paper/2608.27496/) (2026)
- [Towards an Agent Operating System - Lessons from Classical and Cloud OS](https://mlsanity.com/paper/2607.25076/) (2026)
- [Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study](https://mlsanity.com/paper/2609.07395/) (2026)

## Abstract (arXiv)

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 risk associated with target resources and the LLM's own 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.