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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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.