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Related Articles from SNS
Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation
arXiv:2505.12574v5 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems improve the factual grounding of large language models (LLMs) but remain vulnerable to retrieval poisoning, where adversaries seed the corpus with manipulated content. Prior work largely evaluates this threat under a simplified single-attacker assumption. In practice, however, high-value or high-visibility queries attract multiple adversaries with conflicting objectives.