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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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41 entries

Matches every word across titles, descriptions, sources, affected systems, and models.

RAG Poisoning Jailbreak

Published Analyzed Source: arXiv

Large Language Models (LLMs) utilizing Retrieval Augmented Generation (RAG) are vulnerable to a novel attack vector, termed "RAG Poisoning," where malicious content is injected into the external knowledge base accessed by the LLM via prompt manipulation. This allows attackers to elicit undesirable or malicious outputs from the LLM, bypassing its safety filters. The attack exploits the LLM's reliance on the retrieved information during response generation.

Pandora: Jailbreak gpts by retrieval augmented generation poisoning
Evaluated models: GPT-3.5 Turbo, GPT-4, Mistral 7B
RAG
Poisoning
Jailbreak
Application layer

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.