arXiv cs.CLPaper
AfterDeleteBench: Auditing Post-Deletion Information Persistence in LLM Agents
This is a real problem for anyone building agents with persistent memory. The vulnerability isn't a flaw in any one model, it's structural: when you delete a fact, its descendants (summaries, embeddings, composed outputs) still carry the signal. If you're shipping an agent that handles sensitive data, you need to think about cascade deletion, not just removal from the original record. The paper gives you the test to audit your own system.