A Survey on the Security of Long-Term Memory in LLM Agents: Toward Mnemonic Sovereignty
Factual evidence
What the source reports
A research survey identifies emerging security risks in LLM agents with persistent, long-term memory, including cross-session poisoning and unauthorized access.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 23 September 2026
- Collected by OneBench
- 21 Apr 2026, 07:28 UK
Stored source excerpt
arXiv:2604.16548v1 Announce Type: cross Abstract: Research on large language model (LLM) security is shifting from "will the model leak training data" to a more consequential…
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OneBench interpretation
Institutional assessment
So what
Persistent memory in LLM agents introduces a new attack surface for data poisoning and unauthorized access, demanding a re-evaluation of current model risk and data governance frameworks.
Do what
This shifts the security burden from static training data to dynamic, cross-session agent states, requiring your model risk and security teams to develop new controls for agent memory management.