arXiv AI
Aug 28

TriShieldRAG: 3 Rings, One Blind Spot in Layered Defenses for Retrieval-Augmented Generation

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.

By Susil Kumar Mohanty, Rohit Patel, Kosuru Yuvaraj, Jeenal Chaudhary, Disha Singhania
arXiv Computation and Language
1d ago

Language Models Show Conformity Bias in RAG: When the Majority Endorses a Poisoned Claim

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.

By Mehrdad Ghassabi, Audrina Ebrahimi, Zahra Zojaji, Amin Milani Fard
arXiv Computation and Language
Sep 18

Towards Safer RAG: Only Agents Capable of System 2 Thinking may Access Untrusted Documents

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.

By Mehrdad Ghassabi, Audrina Ebrahimi, Sadra Hakim, Hamidreza Baradaran Kashani