arXiv AI
Jul 10
Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT
arXiv:2607. 07922v1 Announce Type: cross Abstract: Vision Transformers (ViTs) remain vulnerable to localized adversarial attacks, e.
Localization then Neutralization: Gradient-guided Token Suppression against Visual Prompt Injection Attack
arXiv:2605. 25194v2 Announce Type: replace Abstract: Adversarial images pose a severe security threat to multimodal large language models through prompt injection.
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Hugging Face Trending Papers
Jun 2
Investigating Adversarial Robustness of Multi-modal Large Language Models
Multi-modal Large Language Models (MLLMs) achieve strong performance on vision-language tasks, but incorporating visual inputs through a vision encoder (e. g.
arXiv AI
Jun 15
MirrorCheck: Efficient Adversarial Defense for Vision-Language Models
arXiv:2406. 09250v5 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly susceptible to sophisticated adversarial attacks, including adaptive strategies specifically designed to bypass existing defenses.
arXiv AI
Jun 24
PHANTOM: A Large-Scale Dataset of Multimodal Adversarial Attacks for Vision-Language Models
arXiv:2606. 24388v1 Announce Type: new Abstract: We introduce a large-scale, open-source dataset of pre-generated adversarial attacks for vision-language models (VLMs).
arXiv Machine Learning
Jun 30
High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models
arXiv:2512. 21815v4 Announce Type: replace-cross Abstract: Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks.
arXiv AI
Jun 10
Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction
arXiv:2606. 10571v1 Announce Type: cross Abstract: Adversarial examples reveal vulnerabilities in Vision-Language Pre-training (VLP) models and provide insights for improving robustness.
arXiv AI
Aug 12
Covert Visual Prompt Injection against Commercial Multimodal Large Language Models
arXiv:2603. 29418v2 Announce Type: replace-cross Abstract: Although multimodal large language models (MLLMs) are increasingly deployed in real-world applications, their instruction-following behavior leaves them vulnerable to prompt injection attacks.
arXiv AI
Jun 11
Diffusion-based Cumulative Adversarial Purification for Vision Language Models
arXiv:2506. 03933v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have shown remarkable capabilities in multimodal understanding, yet their susceptibility to adversarial perturbations poses a significant threat to their reliability in real-world applications.
Hugging Face Trending Papers
Jul 21
Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model
While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their visual inputs remain vulnerable to adversarial attacks, posing significant security risks. Existing defense methods predominantly target single-task scenarios (e.
arXiv Machine Learning
Jun 3
BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks
arXiv:2606. 02947v1 Announce Type: new Abstract: Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks.
arXiv Machine Learning
Jul 17
GeoDetect: Geometric Adversarial Detection for VLPs
arXiv:2607. 14737v1 Announce Type: cross Abstract: Vision-language pre-trained models (VLPs) are widely used in real-world applications.
arXiv Machine Learning
Aug 3
Low-Cost Hard-Label Adversarial Attack with Theoretical Foundations
arXiv:2601. 14300v4 Announce Type: replace Abstract: Hard-label black-box attacks, relying solely on top-1 predictions, represent one of the most challenging yet practically threat models.