AI coding agents struggle with least-privilege authorization, new paper finds

A new research paper introduces permission-boundary inference, a method for AI coding agents to determine the necessary file-level access policies for tasks. The study found that current frontier models struggle with this, often granting excessive permissions while omitting crucial ones. To address this, the paper proposes a decomposition technique that first generates a broad policy and then refines it for tightness, significantly improving success rates and reducing security risks. AI

IMPACT This research highlights a critical security gap in current AI coding agents, suggesting new methods are needed to ensure safe deployment and prevent unauthorized access.

RANK_REASON Academic paper introducing a new method and benchmark for evaluating AI capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• AI coding agents
• AuthBench
• least-privilege authorization
• permission-boundary inference
• Sufficiency-Tightness Decomposition

• paper
• safety

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