From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents
arXiv:2606.04329v1 Announce Type: cross Abstract: Memory is a core component of AI agents, enabling them to accumulate knowledge across interactions and improve performance. However, persistent memory introduces the risk of memory poisoning, where a single adversarial memory write can exert long-term influence over agent behavior. We present a systematic study of memory poisoning in LLM-based agents. We identify four memory write channels and nine structural vulnerabilities in model capabilities, system prompt design, and agent system architecture that make these channels exploitable. Based on
The rapid development and deployment of LLM-based agents necessitate immediate attention to their security vulnerabilities, as their autonomy increases.
Memory poisoning attacks pose a fundamental threat to the reliability, trustworthiness, and long-term utility of AI agents, potentially leading to persistent malicious behavior.
The understanding of critical attack vectors and architectural flaws in AI agent systems is evolving, highlighting the need for robust security by design.
- · AI security researchers
- · Cybersecurity firms specializing in AI
- · Developers of secure AI agent frameworks
- · Organizations deploying unhardened AI agents
- · LLM developers ignoring security-by-design principles
- · Sectors reliant on unverified AI agent outputs
Increased focus on memory security and robustness in AI agent development and deployment.
Development of new defensive mechanisms and architectures specifically designed to counter memory poisoning attacks in autonomous systems.
Potential for regulatory guidelines or standards to emerge addressing the security and integrity of AI agent memories and interactions.
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Read at arXiv cs.AI