A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory To address these challenges, we introduce A-MemGuard (Agent-Memory Guard), the first proactive defense framework for LLM agent memory The core idea of our work is the insight that memory itself must become both self-checking and self-correcting
GitHub - TangciuYueng AMemGuard This is the official repository for the paper A-MemGuard: A Proactive Defense Framework for the LLM-based Agent Memory Our work introduces a novel defense mechanism against memory attacks in LLM agents
A-MEMGUARD: A PROACTIVE DEFENSE FRAMEWORK FOR LLM-BASED AGENT MEMORY ogressively lowers the threshold for sim-ilar attacks in the future To address these challenges, we introduce A-MemGuard (Agent-Memor Guard), the first proactive defense framework for LLM agent memory The core idea of our work is the insight th
A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory This work introduces A-MemGuard (Agent-Memory Guard), the first proactive defense framework for LLM agent memory, which shifts LLM memory security from static filtering to a proactive, experience-driven model where defenses strengthen over time
A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory In this paper, we introduced A-MemGuard, the first proactive defense framework designed to se-cure LLM agent memory The synergy of consensus-based validation and a dual-memory structure enables agents to detect contextual anomalies and learn from experience
A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory A-MemGuard is a proactive defense framework that significantly enhances the security of Large Language Model agents' memory against contextual attacks by employing a consensus-based validation mechanism and a dual-memory structure to detect anomalies and learn from past errors