Accept Changes, Reject Attacks: Robust Memory Management for LLM Agents
LLM agents that persist long-term memory face a dual risk: poisoned records can steer future actions while overly aggressive filtering discards legitimate context — this work proposes a robust management layer addressing both failure modes.
Summary written by editorial AI · Source link below
arXiv:2610.41056v1 Announce Type: new Abstract: Since large language model (LLM) agents rely on long-term memory, what they store now shapes what they do later. A memory manager decides how each input changes this memory and which records the agent acts on. It can fail in two ways: keeping a fact that has since changed (outdated memory), or storing a planted passage or injected instruction (memory poisoning). Most prior work studies them separately, yet they call for opposite write decisions, s
Editorial Analysis
Enterprises building agentic AI systems with persistent memory must solve the memory-poisoning problem before deployment, or risk adversaries influencing downstream decisions through stored context.
Include adversarial memory-poisoning scenarios in your LLM agent red-team exercises and validate that your memory management layer rejects malicious inputs without discarding valid updates.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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