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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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Context-Robust RAG Poisoning

Published Analyzed Source: arXiv

Retrieval-Augmented Generation (RAG) systems are vulnerable to a robust corpus poisoning attack known as "Confundo." This vulnerability arises from the lack of pipeline awareness in standard RAG implementations, specifically regarding document ingestion (tokenization and chunking) and query variations. An attacker can exploit this by fine-tuning a Large Language Model (LLM) to function as a poison generator. Unlike traditional adversarial examples which are brittle, Confundo generates poison…

Confundo: Learning to Generate Robust Poison for Practical RAG Systems
Evaluated models: Llama 3 8B, Gemini Pro
Application layer
Poisoning
Hallucination
RAG

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.