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

1. 1Identify potential tool corruption in LLM agents
2. 2Evaluate the impact of tool corruption on recommendation safety using paired-trajectory protocols
3. 3Decompose divergence into information-channel and decision-process components to understand the sources of unsafe drift
4. 4Develop and implement safety-focused evaluation metrics to complement ranking-quality metrics

ML researchers and engineers working on LLM agents benefit from understanding AgentDrift, as it highlights the importance of evaluating safety in high-stakes domains. This knowledge can inform the development of more robust and reliable LLM agents

💡 Ranking metrics can hide unsafe recommendation drift in LLM agents, emphasizing the need for safety-focused evaluation

🚨 AgentDrift: Unsafe recommendation drift in LLM agents due to tool corruption 🤖