Transferable End-to-End Optimization for Indirect Long-Term Memory Poisoning in LLM Agents
Transferable end-to-end attack poisons LLM agent long-term memory through untrusted content, enabling persistent influence over future decisions — a threat model enterprises with agentic AI must urgently address.
Summary written by editorial AI · Source link below
arXiv:2609.00523v1 Announce Type: new Abstract: Long-term memory can turn untrusted external content into persistent influence over an LLM agent's future decisions, creating the threat of indirect memory poisoning. A successful attack must survive a multi-stage pipeline comprising memory writing, retrieval, and utilization. Existing attacks largely rely on intra-stage optimization, optimizing individual stages in isolation while overlooking inter-stage coupling. Specifically, these stages impos
Editorial Analysis
Enterprises deploying LLM agents with persistent memory face a new class of supply-chain-like attacks where poisoned inputs today shape compromised decisions tomorrow.
Conduct a threat assessment of all LLM agents with long-term memory, focusing on external content ingestion paths as indirect poisoning vectors.
LLM agents with persistent memory can be subtly compromised through external content, creating a new category of hard-to-detect influence attacks that warrants board-level risk awareness.
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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