提出句子级黑盒攻击方法，让无关文档骗过密集检索模型。

Retrieval-Augmented Generation (RAG) systems typically consist of a dense retrieval (DR) model for initial retrieval and a neural ranking model (NRM) for re-ranking.Existing robustness studies in RAG mainly focus on NRMs, while adversarial attacks on DR models are mostly limited to word-level perturbations.For low-ranked target documents that are irrelevant to the query, simple word-level attacks are insufficient to mislead DR models into substantially promoting their rankings.To solve these problems, we propose SentAttack, a sentence-level black-box adversarial attack method for DR models.SentAttack is designed as a two-stage method.In the first stage, SentAttack interacts with the black-box RAG system via iterative retrieval to collect ranked documents and ranking information for training a surrogate DR model.In the second stage, SentAttack uses the surrogate DR model to encode and cluster documents relevant to the target query, yielding multiple cluster centroids.These centroids are concatenated with the target document at the sentence level to form an initial set of adversarial candidates.SentAttack then optimizes these candidates using a query- and centroid-guided objective combined with gradient-guided beam search.Extensive experiments demonstrate that SentAttack outperforms existing adversarial attacks on DR models, with especially strong performance on low-ranked target documents.