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Last analyzed 9/9/2026

Language Model Security Database

985 research findings · 1123 evaluated models

Filtered research findings

42 entries

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

Flowchart-based LVLM Jailbreak Attack

Published 2/1/2025
Analyzed 3/19/2025

FC-Attack leverages automatically generated flowcharts containing step-by-step descriptions derived or rephrased from harmful queries, combined with a benign textual prompt, to jailbreak Large Vision-Language Models (LVLMs). The vulnerability lies in the model's susceptibility to visual prompts containing harmful information within the flowcharts, thus bypassing safety alignment mechanisms.

FC-Attack: Jailbreaking Large Vision-Language Models via Auto-Generated Flowcharts
Evaluated models: Claude 3.5 Sonnet 20240620, Gemini 1.5 Flash, GPT-4o 2024-08-06 +4 more

Source: arXiv

Model layer
Application layer
Prompt layer
Vision

AD Black-Box Cascading Disruption

Published 1/1/2025
Analyzed 12/9/2025

Vision Language Models (VLMs) integrated into autonomous driving (AD) systems are vulnerable to a black-box adversarial attack method termed Cascading Adversarial Disruption (CAD). The vulnerability stems from the model's susceptibility to optimized visual perturbations that disrupt the decision-making reasoning chain (perception, prediction, and planning). Attackers can generate adversarial images or physical patches by aligning visual noise with deceptive textual semantics in the model's…

Black-box adversarial attack on vision language models for autonomous driving
Evaluated models: GPT-4, GPT-4o, InstructBLIP

Source: arXiv

Model layer
Injection
Vision
Multimodal

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.