Last analyzed Sep 9, 2026

Language Model Security Database

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

985 research findings · 1119 evaluated models

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12 entries

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

Universal VLLM Visual Bypass

Published May 2, 2025Analyzed Dec 9, 2025Source: arXiv

A vulnerability exists in Vision-Language Models (VLLMs) that allows for transferable, targeted adversarial attacks. Attackers can generate adversarial image perturbations using an ensemble of open-source surrogate models (primarily CLIP-based visual encoders) which effectively transfer to proprietary, black-box VLLMs. The attack leverages a specific optimization framework that combines a Visual Contrastive Loss with multiple positive/negative visual examples, rather than relying solely on…

Transferable Adversarial Attacks on Black-Box Vision-Language Models

Evaluated models: Qwen 2.5 VL 7B Instruct, Qwen 2.5 VL 72B Instruct, Llama 3.2 11B Vision Instruct+6 more

Qwen 2.5 VL 7B Instruct
Qwen 2.5 VL 72B Instruct
Llama 3.2 11B Vision Instruct
Llama 3.2 90B Vision Instruct
GPT-4o
GPT-4o Mini
Claude 3.5 Sonnet
Claude 3.7 Sonnet
Gemini 1.5 Pro

LLM Robot Bias & Violence

Published Jun 13, 2024Analyzed Apr 12, 2025Source: arXiv

Large Language Models (LLMs) used to control robots exhibit biases leading to discriminatory and unsafe behaviors. When provided with personal characteristics (e.g., race, gender, disability), LLMs generate biased outputs resulting in discriminatory actions (e.g., assigning lower rescue priority to certain groups) and accept or deem feasible dangerous or unlawful instructions (e.g., removing a person's mobility aid).

Llm-driven robots risk enacting discrimination, violence, and unlawful actions

Evaluated models: GPT-3.5, GPT-3.5 Turbo, GPT-4+1 more

GPT-3.5
GPT-3.5 Turbo
GPT-4
Mistral 7B

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.