Memory poisoning via lost-provenance documents in agent memory stores creates agent misconduct that safety systems misattribute to model failure; the paper defines Semantic Norm Drift, releases a benchmark, and proposes a new testing method plus a defense.
InThe Twelfth International Conference on Learning Representations.https://openre view.net/forum?id=GEcwtMk1uA [Srivastava and He(2025)] Saksham Sahai Srivastava and Haoyu He
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The Misattribution Gap: When Memory Poisoning Looks Like Model Failure in Agentic AI Systems
Memory poisoning via lost-provenance documents in agent memory stores creates agent misconduct that safety systems misattribute to model failure; the paper defines Semantic Norm Drift, releases a benchmark, and proposes a new testing method plus a defense.