InjecMEM: Memory Injection Attack on LLM Agent Memory Systems
Score
76首次针对记忆系统提出注入攻击范式，揭示现有智能体安全研究未覆盖的新风险。
方法设计严谨，实验覆盖多系统多模型，并有迁移性与鲁棒性分析。
为记忆系统安全加固提供了明确攻击样例和可复现评测框架，可直接用于防御研究。
记忆子系统在Agent应用中刚刚普及，此刻提出安全威胁具有很强现实紧迫性。
来源于arXiv预印本，尚未经同行评审，但方法和实验细节完整可检验。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
It is the first systematic attack paradigm targeting memory injection in LLM agents, filling a critical gap in agent memory security research.
AI commentary
「What stands out most is how the paper avoids hype and targets a real blind spot—memory systems that are rapidly becoming the default in deployed agents. For teams building personalized agents, this is a threat model they need to understand early, especially those relying on vector stores and RAG for long-term memory; retrieval trust boundaries may need a fundamental rethink.」
Quick overview
Memory is becoming a default subsystem in deployed LLM agents to provide persistent personalization and continuity, yet it may introduce new security vulnerabilities. This paper proposes InjecMEM, a novel memory injection attack paradigm that requires only a single interaction (no read/edit access to memory store) to steer later responses of related queries toward a pre-specified output. Guided by the retrieval-then-generate mechanism of memory systems, the injection consists of a retriever-agnostic anchor and an adversarial command. The anchor contains high-recall topical cues so that downstream retrieval consistently associates the record with the target topic; the command is a short sequence optimized via gradient-based coordinate search to remain effective under uncertain fused contexts, variable placements, and long prompts. Evaluated across multiple memory systems and backbone models, InjecMEM achieves reliable topic-conditioned retrieval and targeted generation, remains effective under memory drift, and leaves non-target queries unaffected. The results underscore the need to harden memory systems and provide a reproducible framework for studying agent memory security.
- Attack requires only a single interaction with no read/edit access to the memory store, creating a broad attack surface.
- Combines retriever-agnostic anchors with adversarially optimized commands via gradient-based coordinate search, enabling cross-model transfer.
- Validated across multiple memory systems and backbones, achieving reliable targeted generation while leaving non-target queries unaffected and remaining robust to memory drift.
Full text
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