The Flow has not summarised this story yet — read it at arXiv Computation and Language.
arXiv:2607. 23838v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) lets a large language model answer questions using documents retrieved from an external knowledge base at query time.
arXiv:2608. 02678v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) systems are vulnerable to corpus poisoning: an attacker who inserts a crafted document into the retrieval corpus can steer the underlying large language model (LLM) toward an attacker-chosen wrong answer.
arXiv:2608.29307v1 Announce Type: cross Abstract: Language models increasingly answer questions by consulting retrieved documents rather than memory alone, a design now common in search assistants an...
arXiv:2609.00470v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) grounds large language models in external corpora, but implicit trust in retrieved documents creates a critical at...
arXiv:2607. 26339v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems ground large language models (LLMs) in external corpora, but this reliance exposes them to corpus poisoning: maliciously injected passages that manipulate retrieved evidence.
The paper introduces MONOLOGUE, a single‑document poisoning framework that uses logic hijacking to subtly alter Retrieval‑Augmented Generation (RAG) outputs without overt contradictions. Instead of directly refuting facts, MONOLOGUE frames updates as authoritative historical corrections, embedding counterfactuals into a grounded narrative. The method refines payloads offline through local multi‑dimensional gated optimization, combining text quality audits with surrogate behavioral verification, and demonstrates superior attack efficacy across multiple benchmarks, retrieval backends, and open‑weight generators.