AgentDrift: Unsafe Recommendation Drift Under Tool Corruption Hidden by Ranking Metrics in LLM Agents

This paper identifies a critical and previously hidden safety vulnerability in tool-augmented LLM agents, demonstrating that standard evaluation metrics can obscure unsafe recommendation drift under tool corruption, which is a paramount concern for the deployment of reliable AI systems.