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arXiv · 2606.25721

Tracing Target Answers in Poisoned Retrieval Corpora via Token Influence Attribution

Yan-Lun Chen·Pin-Yu Chen·Chia-Mu Yu·Ying-Dar Lin·Yu-Sung Wu·Wei-Bin Lee
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Abstract

Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate model outputs through malicious retrieved documents. Existing detection methods typically rely on auxiliary classifiers or additional LLM-based verification, introducing substantial computational overhead. We present TRACE, a lightweight detection framework that identifies poisoning attacks by tracing answer-related tokens through token influence attribution. TRACE first discovers recurrent high-influence keywords across retrieved documents and then performs a secondary verification to confirm their influence on model predictions. Experiments on three QA benchmarks and six LLMs demonstrate strong detection performance while simultaneously uncovering attacker-specified target answers.

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Yan-Lun Chen, Pin-Yu Chen, Chia-Mu Yu, Ying-Dar Lin, Yu-Sung Wu, Wei-Bin Lee. 2026-06-24. Tracing Target Answers in Poisoned Retrieval Corpora via Token Influence Attribution. https://arxiv.org/abs/2606.25721

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