Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents

Persistent memory in LLM agents creates an attack surface that production safety classifiers do not observe: the payload enters via RAG retrieval and persists across sessions via tool-mediated memory. We evaluate six defenses across four architectural layers against delayed-trigger attacks on nine open-source models (5,040 runs, N=40 per condition). Five of six defenses fail: input-level filters never see the payload (it enters via RAG, not user input); retrieval-level classifiers observe it but

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ethics.ai (8 May 2026), “Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents,” evidence record 4700, https://ethics.ai/record/4700 (originally published by arXiv).

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