arXiv:2606.29030v1 Announce Type: new Abstract: AI agents extend conventional large language model (LLM) applications by integrating language understanding with task execution, external tool use, and memory mechanisms. While memory allows agents to retain prior interactions and provide more personalized and context-aware responses, it also introduces a new vulnerability: information stored in memory can influence future outputs even when the current query is clean. In this paper, we investigate memory manipulation in LLM-based agents for multiple-choice question answering. We first design and
The rapid development and deployment of LLM agents make understanding their vulnerabilities critical for safe and effective integration. This research emerges as agents transition from theoretical constructs to practical applications.
Memory manipulation in LLM agents poses a significant security risk, potentially leading to biased or manipulated outputs in critical applications. This highlights the need for robust security measures in autonomous AI systems.
The understanding of AI agent vulnerabilities expands beyond traditional prompt injection, now including persistent memory manipulation as a distinct attack vector. Developers must now consider memory-specific security protocols.
- · AI security researchers
- · Developers of secure AI agent frameworks
- · Cybersecurity firms specializing in AI
- · Users of unhardened LLM agents
- · Organizations relying on agent integrity
- · Developers neglecting memory security
Identification of a new attack surface for LLM agents through memory manipulation.
Increased focus on designing secure memory architectures and verification methods for AI agents.
Development of a specialized 'AI agent forensics' industry to detect and mitigate memory-based attacks.
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Read at arXiv cs.AI