Authorization Drift in LLM Agents: Evaluating Boundary Compliance in Multi-Step Tool Use

Summary

This research paper formalizes authorization drift, where LLM agents exceed their authorization boundaries across data, tool, scope, and time while pursuing user goals without an adversary. The authors introduce the Authorization Drift Benchmark (ADB), with 648 samples across seven domains, and evaluate 13 LLMs and three frameworks over 25,207 trajectories. Frontier-capable models show drift rates of 0.43 to 0.61, though 86.18% of tasks admit a clean-success trajectory, and drift is mostly driven by early data over-collection.