Chunyu Li
Author directory2026
A Survey on Long-Term Memory Security in LLM Agents: Attacks, Defenses, and Governance Across the Memory Lifecycle
Zehao Lin | Xixuan Hao | Renyu Fu | Shaobo Cui | Kai Chen | Chunyu Li | Zhiyu li | Feiyu Xiong
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Zehao Lin | Xixuan Hao | Renyu Fu | Shaobo Cui | Kai Chen | Chunyu Li | Zhiyu li | Feiyu Xiong
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
The emergence of writable, cross-session persistent memory in LLM agents introduces a qualitatively different threat landscape from conventional input-centric security concerns, characterized by three properties: persistence, statefulness, and propagation. To systematically characterize this landscape, we propose a Memory Lifecycle Framework that organizes attacks, defenses, and their cross-phase dependencies along two axes: six lifecycle phases (Write, Store, Retrieve, Execute, Share Propagate, Forget Rollback) and four security objectives (Integrity, Confidentiality, Availability, Governance). This analysis in turn exposes the need for formal security guarantees at the system level, motivating Verifiable Memory Governance (VMG), a framework of five architectural primitives that specifies what verifiable mechanisms a long-term-memory system must provide to maintain auditable, recoverable control over its memory state. Our analysis indicates that robust Long-Term Memory (LTM) security cannot be retrofitted at retrieval or execution time alone, but must be anchored in storage-time provenance, versioning, and policy-aware retention from the outset.