Research · curated 31 Aug 2026
Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems
First reported arxiv.org
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Why it matters
Knowledge poisoning lets adversaries insert malicious documents into RAG retrieval to induce targeted misinformation, and this work offers defenders a pre-generation detection method plus an attack generator to test their own pipelines.
Researchers at Tampere University present Trustworthy RAG, an Evaluation Agent middleware that detects knowledge poisoning and misinformation in Retrieval-Augmented Generation systems by combining Natural Language Inference factual verification, a five-signal poison detector, and a Trust Index scoring formula. On TruthfulQA with Llama 3.3 70B the agent reaches 91% accuracy and 100% recall on instruction injection, though subtle in-place entity swaps remain hard to detect; the authors release the approach, an attack generator, and experimental artifacts at github.com/GPT-Laboratory/TrustworthyRAG.