Deployment-Time Memorization in Foundation-Model Agents

A new study examines how AI agents with persistent memory create privacy-utility tradeoffs in real-world deployments, finding that aggressive summarization can reduce data extraction risks by up to 76% while preserving personalization. The research reveals that even when information is "deleted," derived copies often remain recoverable in memory systems.