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arXiv AI

ClawMMU: Protection-Coherent Virtual Memory for Stateful LLM Agents

ClawMMU is a protection layer for large language model agents that tags every memory fragment with its owner, scope, origins, and authority. It enforces that information can only increase in authority but never self‑elevate, checking these labels whenever content enters the model’s context, is stored, retrieved, or used to drive tool calls. By basing checks on provenance rather than content, ClawMMU turns memory attacks into classical protection faults that the agent harness can block even if the model is misled.

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SAFE-Mem: Secure Agentic Memory with Fine-Grained Authority and Governance-Aware Retrieval

SAFE‑Mem is a fine‑grained memory governance framework for large language model agents that annotates each memory claim with governance labels—kind, authority, and scope—to capture provenance and permitted use. It uses governance‑aware retrieval to match queries with claim permissions, blocking unauthorized influence while preserving legitimate information, and builds a governance‑enabled memory graph for provenance tracking and efficient retrieval. Experiments on four LLM backbones show that SAFE‑Mem achieves the lowest attack success rate among defenses that retain legitimate memory and improves answer accuracy by up to 15%.

By Haoming Li, Dingyi Kang, Xu Hu, Jinxin Yang, Zhu Xu, Chenru Wang, Yi Ge, Miao Yin, Guanpeng Li, Feng Chen, Bingzhe Li
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Sep 24

Safeguarding LLM Agents against Long-Horizon Threats via Shadow Memory

The paper introduces ShadowMem, a defensive framework that protects large language model agents from long-horizon threats by maintaining a dedicated safety-focused memory. Inspired by the shadow stack concept, ShadowMem stores safety-critical context throughout an agent’s execution and uses this shadow memory to evaluate the risk of upcoming actions before they are carried out. Experiments show that ShadowMem outperforms existing defenses in detection accuracy, detects most attacks early, and adds minimal overhead to agent performance.

By Yuhui Wang, Tanqiu Jiang, Jiacheng Liang, Charles Fleming, Ting Wang
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arXiv AI
Jun 12

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.

By Zehao Lin, Xixuan Hao, Renyu Fu, Shaobo Cui, Kai Chen, Chunyu Li, Zhiyu Li, Feiyu Xiong
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