Cited by 3Adversarial Robustness in Machine LearningSecurity and Verification in ComputingCryptographic Implementations and Security

Memory Injection Attacks on LLM Agents via Query-Only Interaction

Dong, Shen, Xu, Shaochen, He, Pengfei, Li, Yige, Tang, Jiliang, Liu, Tianming, Liu, Hui, Xiang, Zhen·2025·Advances in Neural Information Processing Systems 38

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Crossref · 10.52202/085713-1554

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  • Modern memory modules automatically ingest and persist content without provenance verification.

    “This is realistic because modern memory modules automatically ingest and persist content without provenance verification (Dong et al., 2025).”

    From MemMorph: Tool Hijacking in LLM Agents via Memory Poisoning (2026) · cites this paper as Dong et al., 2025

  • Long-term memory can turn untrusted external content into persistent influence over an LLM agent's future decisions, creating the threat of indirect memory poisoning.

    “Long-term memory can turn untrusted external content into persistent influence over an LLM agent's future decisions, creating the threat of indirect memory poisoning.”

    From Transferable End-to-End Optimization for Indirect Long-Term Memory Poisoning in LLM Agents (2026) · cites this paper as [6]

  • A memory produced from the poisoning content may still be hidden by benign memories during retrieval.

    “A memory produced from the poisoning content may still be hidden by benign memories during retrieval [2,6].”

    From Transferable End-to-End Optimization for Indirect Long-Term Memory Poisoning in LLM Agents (2026) · cites this paper as [6]

  • MINJA shows that adversarial records can be implanted through normal user interactions.

    “MINJA (Dong et al., 2025) further shows that adversarial records can be implanted through normal user interactions.”

    From MemMorph: Tool Hijacking in LLM Agents via Memory Poisoning (2026) · cites this paper as Dong et al., 2025

  • MINJA achieves an average RSR@5 of 87.6% but only 48.2% AUR.

    “MINJA achieves an average RSR@5 of 87.6% but only 48.2% AUR”

    From Transferable End-to-End Optimization for Indirect Long-Term Memory Poisoning in LLM Agents (2026) · cites this paper as [6]

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Cited by 3

Authors
Dong, S., Xu, S., He, P., Li, Y., Tang, J., Liu, T., Liu, H., & Xiang, Z.
Affiliations
  • Michigan State University (US)
  • University of Georgia (US)
  • Singapore Management University (SG)
Venue
Advances in Neural Information Processing Systems 38
Published
2025
Pages
52119-52153
ISSN
1049-5258 (ISSN-L)
OpenAlex
W7196948743
Cited by
3
References
0
Citations by year
2026: 3
FWCI
2.86
Type
conference paper
Language
en
Open access
closed
Locations
neural information processing systems (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Security and Verification in Computing
  • Physical Sciences › Computer Science › Artificial Intelligence › Cryptographic Implementations and Security
Keywords
LLM agents, LLM security, memory manipulation

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