Forgetting is Not Always Bad: A Neuro-Inspired Memory Repair Mechanism for Poisoned LLM Agents
Read the original on Hugging Face Trending Papers →Large language model agents are vulnerable to memory‑poisoning attacks, and existing defenses rely on external modules that are memory‑inefficient and computationally heavy. The paper introduces DREAM, a training‑free, agent‑intrinsic memory repair framework that uses weak reactivation to destabilize malicious memories, anomaly detection on activation patterns, and selective suppression of harmful memories while reinforcing benign ones. Experiments show DREAM cuts backdoor attack success by over 95 % and improves runtime and token efficiency compared to A‑MemGuard, while maintaining high task success on poisoned multi‑agent systems.
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