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.
TriShieldRAG introduces a three‑layered defense for Retrieval‑Augmented Generation: an Ingest Guard that screens documents, a Retrieval Scorer that re‑ranks based on trust, and a Cross‑LLM Consensus that validates evidence across three models. Against the original PoisonedRAG attack on the 2.68M‑passage Natural Questions corpus, the framework reduces attack success from about 79% to 1%. However, adaptive attacks that only alter document formatting can bypass the Ingest Guard and still achieve high success rates, revealing limits of layered defenses that rely on the same retrieved evidence.
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:2608.17153v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) improves large language models by grounding them in external evidence, but this exposes them to knowledge-pois...
Retrieval‑augmented generation (RAG) can expose language models to misleading evidence. In a study of 200 SciFact questions, the Gemma‑3‑12b model’s ability to detect poisoned claims dropped from 25% when a single poisoned document was present to 10% when six mutually agreeing poisoned documents dominated the context. The majority condition caused 34 of 38 questions to shift from detected to undetected, indicating a conformity‑like bias.
The paper examines how deliberative (System 2) reasoning affects a Retrieval-Augmented Generation (RAG) model’s vulnerability to knowledge‑poisoning attacks. Using two metrics—Cordon Rate and Leakage Rate—it evaluates six model configurations on 200 SciFact questions. Results show that enabling reasoning lowers both Cordon and Leakage Rates for DeepSeek‑V4‑Flash, indicating reduced behavioral impact from poisoned evidence, though overall attack success increases.
arXiv:2609.09243v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) grounds a language model in retrieved documents, which reduces hallucination but creates a new attack surface: i...
arXiv:2610.40742v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is vulnerable to knowledge poisoning, where adversaries manipulate retrieved evidence to influence model outputs...
arXiv:2610.33849v1 Announce Type: cross Abstract: Query-time filters that screen each retrieved document before it reaches the generator are the dominant published defense against retrieval-augmented...
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.
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. 04756v1 Announce Type: cross Abstract: In Retrieval-Augmented Generation (RAG), post-retrieval conflict resolution arbitrates among noisy or contradictory retrieved passages.