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

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

Query-Agnostic Poisoning of Medical Multimodal RAG

Updated 7/20/2026

M3Att demonstrates a reproducible knowledge-poisoning issue in medical multimodal RAG: an attacker with limited corpus-distribution knowledge can insert paired image-text entries whose visually perturbed images are broadly retrieved and whose clinically plausible misinformation steers downstream generation. The paper evaluates both white-box and black-box retrieval optimization and reports degraded diagnostic and report-generation utility across multiple datasets, retrievers, and LVLMs. These…

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation

Affects: GPT-4o, GPT-5 Chat, Gemini 2.5 Flash +5 more

Source: arXiv

Synchronized Multimodal Steering

Updated 3/9/2026

Multimodal Large Language Model-based Recommender Systems (MLLM-RecSys) are vulnerable to Cross-Modal Interactive Data Poisoning. Attackers can manipulate the system by injecting compromised user-generated content (UGC) that contains synchronized, coupled perturbations across both textual and visual modalities. While MLLMs naturally filter out single-modality noise via cross-modal consensus, this vulnerability exploits the consensus mechanism itself. By leveraging cross-modal attention to…

VENOMREC: Cross-Modal Interactive Poisoning for Targeted Promotion in Multimodal LLM Recommender Systems

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.