arXiv CS AI#tech
When Poison Fails After Retrieval: Revisiting Corpus Poisoning under Chunking and Reranking Pipelinestranslating…
Factuality: 90/100USACornell University
arXiv:2606.11265v1 Announce Type: cross
Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate downstream model outputs through malicious knowledge injection. Existing studies mainly evaluate poisoning under simplified retrieval settings, overlooking practical RAG pipelines involving document chunking, dense retrieval, reranking, and grounded generation. In this paper, we revisit corpus poisoning under realistic multi-stage retrieval pi