AINeutralarXiv – CS AI · 6h ago6/10
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MEMSAD: Gradient-Coupled Anomaly Detection for Memory Poisoning in Retrieval-Augmented Agents
Researchers present MEMSAD, a defense mechanism against memory poisoning attacks on retrieval-augmented LLM agents, using gradient-coupled anomaly detection to identify adversarial perturbations while maintaining retrieval performance. The work formalizes security vulnerabilities in persistent external memory systems and demonstrates that while composite defenses achieve perfect detection rates, synonym-based attacks remain undetectable by embedding-based approaches.