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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42 entries

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

Adversarial VLM Jailbreak

Published Feb 17, 2025Analyzed Dec 9, 2025Source: arXiv

Vision-Language Models (VLMs), specifically the LLaVA-1.5 and LLaVA-1.6 series, are vulnerable to optimization-based white-box jailbreak attacks despite standard safety alignment measures like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). Attackers can craft adversarial perturbations in the image space (imperceptible noise) or latent space using Projected Gradient Descent (PGD) to manipulate the model's internal representations. These perturbations maximize the…

Adversary-Aware DPO: Enhancing Safety Alignment in Vision Language Models via Adversarial Training

Evaluated models: LLaVA 1.5 7B, LLaVA 1.6 7B

AD Black-Box Cascading Disruption

Published Jan 23, 2025Analyzed Dec 9, 2025Source: arXiv

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

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.