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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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1 entry

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

Semantic Hierarchical VLM Transfer

Published Analyzed Source: arXiv

Large Vision-Language Models (VLMs) are vulnerable to a transferable targeted adversarial attack known as SGHA-Attack (Semantic-Guided Hierarchical Alignment). This vulnerability arises from the susceptibility of visual encoders (specifically Vision Transformers) to intermediate-layer feature manipulation optimized on a surrogate model (e.g., CLIP). An attacker can craft adversarial images by injecting imperceptible perturbations that enforce semantic consistency with a target text prompt…

SGHA-Attack: Semantic-Guided Hierarchical Alignment for Transferable Targeted Attacks on Vision-Language Models
Evaluated models: UniDiffuser, BLIP-2 ViT-g/14, InstructBLIP Vicuna 13B+4 more
  • UniDiffuser
  • BLIP-2 ViT-g/14
  • InstructBLIP Vicuna 13B
  • MiniGPT-4 Vicuna 13B
  • LLaVA 1.5 13B
  • LLaVA NeXT Qwen 1.5 72B Chat
  • GPT-4o
Model layer
Jailbreak
Hallucination
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.

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.