MIND: Lightweight and Effective Memory Injection Defense for LLM Agents via Intent-Aware Information Bottleneck
Read the original on arXiv AI →MIND is a lightweight defense framework that protects memory‑augmented LLM agents from memory injection attacks by using an intent‑aware Information Bottleneck to extract compact intent–behavior representations. The approach filters out task‑irrelevant and repetitive information while preserving attack signals, enabling a lightweight detector to identify malicious memories without repeated LLM auditing. Experiments show that MIND significantly lowers attack success rates—reducing mean ASR‑r and ASR‑a by 58.1% and 53.5% on ReAct‑StrategyQA—while maintaining task accuracy and inference efficiency.
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