Updated 7/21/2026

Language Model Security Database

Explore primary-source AI security research, evaluated models, attack techniques, and defensive evidence.

959 research findings · 1077 evaluated models

Findings

959

Models evaluated

1077

Paper-evaluated models

Any selected model can match.

Refine by category

Findings match every selected category.

Attack Type2 selected

Attack Context

Architecture Components

Attack Surface

Impact1 selected

Advanced filters

Published fromPublished throughSource or venue

arXiv is the only currently indexed source.

Evidence status

Filtered research findings

1 entry

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

Context-Robust RAG Poisoning

Updated 2/22/2026

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

Affects: Llama 3 8B, Gemini Pro

Source: arXiv

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.