Attack Type

Hallucination Research

Exploits causing models to generate false information

4 matching entries out of 53 in this category

Hallucination research results

4 entries

Semantic Hierarchical VLM Transfer

Published Feb 2, 2026Analyzed Feb 22, 2026Source: 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

Sound Induces Trimodal Collapse

Published Jan 20, 2026Analyzed Feb 22, 2026Source: arXiv

A vulnerability exists in trimodal audio-video-language models where an attacker can systematically degrade multimodal reasoning through untargeted, audio-only adversarial perturbations. By optimizing a shared perturbation $\delta$ applied to the audio channel, an attacker can manipulate internal representations—specifically targeting audio encoder embeddings and cross-modal attention mechanisms—without modifying visual or textual inputs. The attack exploits the model's reliance on the audio…

SoundBreak: A Systematic Study of Audio-Only Adversarial Attacks on Trimodal Models

Evaluated models: VideoLLAMA2, Qwen 2 7B Instruct, Whisper Large-v2+1 more

VideoLLAMA2
Qwen 2 7B Instruct
Whisper Large-v2
PANNs

Point Cloud Adversarial Attack

Published Jan 10, 2026Analyzed Feb 22, 2026Source: arXiv

Point-based 3D Vision-Language Models (VLMs), specifically PointLLM and GPT4Point, are vulnerable to white-box, gradient-based adversarial attacks. The vulnerability exists in the model's processing of 3D point cloud data, where an attacker can optimize imperceptible geometric perturbations ($\delta$) on the input point cloud ($x$) to manipulate the model's textual output. The paper identifies two specific attack vectors: 1. Vision Attack: Directly perturbs the high-dimensional visual token…

On the Adversarial Robustness of 3D Large Vision-Language Models

Evaluated models: Vicuna 7B

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

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