Memory Injection Attacks on LLM Agents via Query-Only Interaction
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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]
Reliance on memory introduces a security risk: adversaries can inject seemingly harmless records into an agent's memory to manipulate its future behavior.
“However, this reliance on memory introduces a critical security risk: an adversary can inject seemingly harmless records into an agent's memory to manipulate its future behavior.”
From A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory (2025) · cites this paper as Dong et al., 2025
The indirect memory injection attack poisons memory through seemingly normal user queries and can initiate a self-reinforcing error cycle where flawed memories are used as precedents for future flawed actions.
“This attack vector is particularly dangerous as it poisons the memory through seemingly normal user queries, which can initiate a self-reinforcing error cycle where flawed memories are used as precedents for future flawed actions.”
From A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory (2025) · cites this paper as Dong et al., 2025
Once triggered, memory injection can initiate a self-reinforcing error cycle: the corrupted outcome is stored as precedent, which amplifies the initial error and progressively lowers the threshold for similar attacks.
“Second, once triggered, the manipulation can initiate a self-reinforcing error cycle: the corrupted outcome is stored as precedent, which not only amplifies the initial error but also progressively lowers the threshold for similar attacks in the future.”
From A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory (2025) · cites this paper as Dong et al., 2025
Recent work has demonstrated memory poisoning attacks in various settings.
“Recent work has demonstrated memory poisoning attacks in various settings (Dong et al., 2025; Srivastava & He, 2025; Anonymous, 2025; Chen et al., 2024; Zou et al., 2026; Xu et al., 2026).”
From From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents (2026) · cites this paper as Dong et al., 2025
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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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