arXiv AI June 12, 2026 By Zehao Lin, Xixuan Hao, Renyu Fu, Shaobo Cui, Kai Chen, Chunyu Li, Zhiyu Li, Feiyu Xiong

A Survey on Long-Term Memory Security in LLM Agents: Attacks, Defenses, and Governance Across the Memory Lifecycle

arXiv:2604. 16548v2 Announce Type: replace-cross Abstract: 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 3

Memory Provenance Laundering in LLM Agents: A Non-Amplification Firewall for Persistent Memory

arXiv:2607. 29167v1 Announce Type: cross Abstract: Long-term memory lets large language model(LLM) agents reuse prior preferences and work flows, but it also turns untrusted observations into persistent action context.

By Jinghan Xu, Yiyong Xiao, Wanru Shao, Hankai Liu, Xinjin Li

llms agents multimodal

arXiv AI

Jul 31

MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair

arXiv:2607. 27080v1 Announce Type: cross Abstract: Memory systems allow agents to retain and reuse information from past interactions, but they can also let malicious content persist.

By Xuanze Chen, Xukang Xie, Wentao Fu, Jiajun Zhou, Shanqing Yu, Qi Xuan

llms agents benchmarks

arXiv AI

4d ago

Trojan Hippo Bench: A Dynamic Benchmark for Persistent Memory Attacks and Defenses in LLM Agents

arXiv:2605.01970v4 Announce Type: replace-cross Abstract: Memory systems enable otherwise stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. Th...

By Debeshee Das, Julien Piet, Darya Kaviani, Luca Beurer-Kellner, Florian Tram\`er, David Wagner

llms rag agents multimodal benchmarks safety

arXiv AI

Jun 10

Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation

arXiv:2606. 10749v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory, and act on external environments.

By Yuchen Ling, Shengcheng Yu, Zhenyu Chen, Chunrong Fang

llms agents benchmarks

arXiv AI

Sep 3

Agent Memory Is a Surface for Endogenous Authorization Laundering

Agent Memory Is a Surface for Endogenous Authorization Laundering explores how long‑running LLM agents use persistent memory to track permissions, restrictions, and revocations. The paper shows that when memory misrepresents evolving authorization states, agents can grant themselves authority that the underlying history never permitted, a phenomenon the authors call endogenous authorization laundering. To study this, the authors introduce EAL‑Bench, evaluate several LLMs across domains, and find that memory writers can create false authority in up to 50.2% of cases, which executors then act upon in 98.6% of trials. Two safeguards—requiring stored permissions to be backed by valid source events and tracking permission changes through bounded event sourcing—reduce laundering but also reject more legitimate actions, highlighting a safety‑utility tradeoff.

By Tommaso Cerruti, Mika Okamoto, Ansel Kaplan Erol

llms agents safety

arXiv AI

Aug 5

When Memory Becomes Authority: Benchmarking Authority Collapse at the Memory Consolidation Boundary

arXiv:2608. 01679v2 Announce Type: replace Abstract: Persistent memory allows (self-evolving) LLM agents to adapt across tasks by consolidating heterogeneous interaction histories into reusable facts, preferences, observations, and rules.

By Qiuyang Zhan, Rui Zhang, Sheng Guo, Lepeng Zhao, Zhuotao Liu

llms agents benchmarks

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Related stories

Memory Provenance Laundering in LLM Agents: A Non-Amplification Firewall for Persistent Memory

MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair

Trojan Hippo Bench: A Dynamic Benchmark for Persistent Memory Attacks and Defenses in LLM Agents

Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation

Agent Memory Is a Surface for Endogenous Authorization Laundering

When Memory Becomes Authority: Benchmarking Authority Collapse at the Memory Consolidation Boundary