代理记忆中毒检测：基于取证轨迹签名的行为不变性方法

Computer Science > Cryptography and Security

arXiv:2606.30566v1 (cs) [Submitted on 29 Jun 2026]

Title:Forensic Trajectory Signatures for Agent Memory Poisoning Detection

Authors:Jun Wen Leong View a PDF of the paper titled Forensic Trajectory Signatures for Agent Memory Poisoning Detection, by Jun Wen Leong View PDF HTML (experimental)

Abstract:We discover a behavioral invariant in LLM agents under persistent memory poisoning: in architectures where routing information is retrieved through observable memory-tool invocations, successful attacks require calling memory_recall_fact before email_send_email, a transition that non-exfiltrating sessions rarely exhibit. Under the evaluated architecture, this invariant follows from the attack's information-retrieval dependency rather than being merely an empirical correlation, and suppressing it breaks the attack. A simple rule exploiting this invariant alone achieves AUC = 0.9563. A Random Forest classifier over 19 trajectory features refines it to AUC = 0.9904 (BCa 95% CI [0.987, 0.993], N=10,000 resamples), demonstrating that the attack imprints on multiple independent behavioral channels. The signature is overdetermined: removing all recall-related features (half the feature set) leaves AUC unchanged at 0.990, confirming that memory poisoning induces a distributed trajectory signature rather than a single observable anomaly. Cross-model hold-out on 9 models (7B-120B parameters) confirms AUC = 1.000 on 6/9 hold-out splits, with all three exceptions mechanistically explained. The invariant generalizes to frontier models (GPT-4.1, GPT-4o) without retraining. A strictly prefix-only variant achieves AUC = 0.934, suggesting that real-time blocking is feasible with moderate degradation. The boundary is forensically useful: prompt-injection attacks that bypass memory produce a distinct trajectory (score = 0.541), enabling incident responders to distinguish memory-channel attacks from prompt-injection attacks using tool-call logs alone.

Comments:	11 pages, 4 figures. Companion note to arXiv:2605.08442
Subjects:	Cryptography and Security (cs.CR); Machine Learning (cs.LG)
Cite as:	arXiv:2606.30566 [cs.CR]
	(or arXiv:2606.30566v1 [cs.CR] for this version)
	https://doi.org/10.48550/arXiv.2606.30566 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Jun Wen Leong [view email]
[v1] Mon, 29 Jun 2026 17:09:03 UTC (63 KB)
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