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This paper reveals that RAG poisoning can artificially boost model confidence, evading traditional detection methods, and proposes monitoring attention distribution to detect such attacks.
This paper proposes D-SCAN, a detection framework for RAG poisoning that monitors attention collapse dynamics in language model generations, showing effectiveness against adversarial attacks even when outputs appear benign.