Prevented is Not Protected: What Attack Success Rate Hides about RAG
Researchers argue that attack success rate obscures real defensive weaknesses in RAG systems and propose complementary metrics, urging teams to look beyond headline numbers when evaluating knowledge-poisoning defences.
Summary written by editorial AI · Source link below
arXiv:2610.40742v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is vulnerable to knowledge poisoning, where adversaries manipulate retrieved evidence to influence model outputs. Existing defenses are primarily evaluated using attack success rate (ASR), which obscures defense-induced failures and the mechanisms behind apparent attack prevention. To address this issue, this work introduces \textbf{Aligned RAG Evaluation}, a matched-query protocol that jointly examines clean a
Editorial Analysis
Enterprises relying on RAG pipelines may overestimate their resilience if they benchmark only on attack success rate; richer metrics reveal hidden failure modes in retrieval-augmented AI.
Adopt multi-dimensional evaluation metrics—beyond attack success rate—when assessing the robustness of RAG-based applications against knowledge poisoning.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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