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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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2 entries

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

RL Adversarial Function Call

Published Analyzed Source: arXiv

Large Language Models (LLMs) enabled with Function Calling (FC) capabilities are vulnerable to adversarial query rewriting and semantic manipulation. Standard FC models, typically trained via Supervised Fine-Tuning (SFT) on static datasets, fail to generalize against adversarial inputs that deviate from fixed distribution patterns. An attacker can exploit this by crafting queries that are semantically similar to valid requests but engineered to induce "bad cases," such as incorrect tool…

Exploring Weaknesses in Function Call Models via Reinforcement Learning: An Adversarial Data Augmentation Approach
Evaluated models: Qwen 2.5 7B Instruct, Qwen 3 0.6B, Qwen 3 4B+1 more
  • Qwen 2.5 7B Instruct
  • Qwen 3 0.6B
  • Qwen 3 4B
  • Qwen 3 8B
Model layer
Hallucination
Fine-tuning
AI agents

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.