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Forensic Trajectory Signatures for Agent Memory Poisoning Detection

A research paper on detecting memory poisoning attacks in large language models.

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Forensic Trajectory Signatures for Agent Memory Poisoning Detection

By Jun Wen LeongarXiv
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The authors discover a behavioral invariant in LLM agents under persistent memory poisoning, which can be used to detect attacks. A simple rule and a Random Forest classifier are proposed to exploit this invariant, achieving high accuracy.

The signature is overdetermined and generalizes to frontier models without retraining.

Abstract

The authors discover a behavioral invariant in LLM agents under persistent memory poisoning, which can be used to detect attacks. A simple rule and a Random Forest classifier are proposed to exploit this invariant, achieving high accuracy. The signature is overdetermined and generalizes to frontier models without retraining.

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memory poisoning detectionllm securityforensic analysisbehavioral invariantrandom forest classifierAI AgentsLarge Language ModelsAgent MemorySemantic Interoperability
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