On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Xinwei, Liu, Hangcheng, Bai, Li, Wang, Hao, Ye, Qingqing, Zhang, Tianwei, Hu, Haibo |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability
by: Wang, Hao, et al.
Published: (2024)
by: Wang, Hao, et al.
Published: (2024)
Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion
by: Xie, Chunlong, et al.
Published: (2025)
by: Xie, Chunlong, et al.
Published: (2025)
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
by: Yin, Ziyi, et al.
Published: (2023)
by: Yin, Ziyi, et al.
Published: (2023)
Crafting Adversarial Inputs for Large Vision-Language Models Using Black-Box Optimization
by: Guan, Jiwei, et al.
Published: (2026)
by: Guan, Jiwei, et al.
Published: (2026)
Jailbreak Vision Language Models via Bi-Modal Adversarial Prompt
by: Ying, Zonghao, et al.
Published: (2024)
by: Ying, Zonghao, et al.
Published: (2024)
DAVSP: Safety Alignment for Large Vision-Language Models via Deep Aligned Visual Safety Prompt
by: Zhang, Yitong, et al.
Published: (2025)
by: Zhang, Yitong, et al.
Published: (2025)
Image-Based Geolocation Using Large Vision-Language Models
by: Liu, Yi, et al.
Published: (2024)
by: Liu, Yi, et al.
Published: (2024)
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models
by: Fang, Hao, et al.
Published: (2024)
by: Fang, Hao, et al.
Published: (2024)
Hidden Tail: Adversarial Image Causing Stealthy Resource Consumption in Vision-Language Models
by: Zhang, Rui, et al.
Published: (2025)
by: Zhang, Rui, et al.
Published: (2025)
Revisiting Data Auditing in Large Vision-Language Models
by: Zhu, Hongyu, et al.
Published: (2025)
by: Zhu, Hongyu, et al.
Published: (2025)
Test-Time Attention Purification for Backdoored Large Vision Language Models
by: Zhang, Zhifang, et al.
Published: (2026)
by: Zhang, Zhifang, et al.
Published: (2026)
Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models
by: Wu, Zongyu, et al.
Published: (2025)
by: Wu, Zongyu, et al.
Published: (2025)
Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images
by: Gao, Kuofeng, et al.
Published: (2024)
by: Gao, Kuofeng, et al.
Published: (2024)
Rethinking Robust Adversarial Concept Erasure in Diffusion Models
by: Yin, Qinghong, et al.
Published: (2025)
by: Yin, Qinghong, et al.
Published: (2025)
A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations
by: Ye, Mang, et al.
Published: (2025)
by: Ye, Mang, et al.
Published: (2025)
Improving Adversarial Robustness via Feature Pattern Consistency Constraint
by: Hu, Jiacong, et al.
Published: (2024)
by: Hu, Jiacong, et al.
Published: (2024)
Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
by: Zhang, Yimeng, et al.
Published: (2024)
by: Zhang, Yimeng, et al.
Published: (2024)
JailbreakZoo: Survey, Landscapes, and Horizons in Jailbreaking Large Language and Vision-Language Models
by: Jin, Haibo, et al.
Published: (2024)
by: Jin, Haibo, et al.
Published: (2024)
Adversarial Robustness of Vision in Open Foundation Models
by: Fox, Jonathon, et al.
Published: (2025)
by: Fox, Jonathon, et al.
Published: (2025)
CoreMark: Toward Robust and Universal Text Watermarking Technique
by: Meng, Jiale, et al.
Published: (2025)
by: Meng, Jiale, et al.
Published: (2025)
Model X-ray:Detecting Backdoored Models via Decision Boundary
by: Su, Yanghao, et al.
Published: (2024)
by: Su, Yanghao, et al.
Published: (2024)
Improving Adversarial Transferability of Vision-Language Pre-training Models through Collaborative Multimodal Interaction
by: Fu, Jiyuan, et al.
Published: (2024)
by: Fu, Jiyuan, et al.
Published: (2024)
Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization
by: Hao, Shuyang, et al.
Published: (2025)
by: Hao, Shuyang, et al.
Published: (2025)
Class-feature Watermark: A Resilient Black-box Watermark Against Model Extraction Attacks
by: Xiao, Yaxin, et al.
Published: (2025)
by: Xiao, Yaxin, et al.
Published: (2025)
Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey
by: Zhang, Chiyu, et al.
Published: (2024)
by: Zhang, Chiyu, et al.
Published: (2024)
A Cross-Modal Prompt Injection Attack against Large Vision-Language Models with Image-Only Perturbation
by: Yang, Hao, et al.
Published: (2026)
by: Yang, Hao, et al.
Published: (2026)
R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning
by: Sheng, Lijun, et al.
Published: (2025)
by: Sheng, Lijun, et al.
Published: (2025)
Robust Watermarks Leak: Channel-Aware Feature Extraction Enables Adversarial Watermark Manipulation
by: Ba, Zhongjie, et al.
Published: (2025)
by: Ba, Zhongjie, et al.
Published: (2025)
Robust Anti-Backdoor Instruction Tuning in LVLMs
by: Xun, Yuan, et al.
Published: (2025)
by: Xun, Yuan, et al.
Published: (2025)
PromptSmooth: Certifying Robustness of Medical Vision-Language Models via Prompt Learning
by: Hussein, Noor, et al.
Published: (2024)
by: Hussein, Noor, et al.
Published: (2024)
Boosting Adversarial Transferability with Spatial Adversarial Alignment
by: Chen, Zhaoyu, et al.
Published: (2025)
by: Chen, Zhaoyu, et al.
Published: (2025)
EigenShield: Causal Subspace Filtering via Random Matrix Theory for Adversarially Robust Vision-Language Models
by: Darabi, Nastaran, et al.
Published: (2025)
by: Darabi, Nastaran, et al.
Published: (2025)
DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision Transformers
by: Yehezkel, Oryan, et al.
Published: (2024)
by: Yehezkel, Oryan, et al.
Published: (2024)
Robustness of Vision Foundation Models to Common Perturbations
by: Liu, Hongbin, et al.
Published: (2026)
by: Liu, Hongbin, et al.
Published: (2026)
NWaaS: Nonintrusive Watermarking as a Service for X-to-Image DNN
by: An, Haonan, et al.
Published: (2025)
by: An, Haonan, et al.
Published: (2025)
DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models
by: Sun, Ye, et al.
Published: (2026)
by: Sun, Ye, et al.
Published: (2026)
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?
by: Lorenz, Peter, et al.
Published: (2021)
by: Lorenz, Peter, et al.
Published: (2021)
On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective
by: Alter, Tal, et al.
Published: (2024)
by: Alter, Tal, et al.
Published: (2024)
Distilling Adversarial Robustness Using Heterogeneous Teachers
by: Deng, Jieren, et al.
Published: (2024)
by: Deng, Jieren, et al.
Published: (2024)
ViT-EnsembleAttack: Augmenting Ensemble Models for Stronger Adversarial Transferability in Vision Transformers
by: Cao, Hanwen, et al.
Published: (2025)
by: Cao, Hanwen, et al.
Published: (2025)
Similar Items
-
Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability
by: Wang, Hao, et al.
Published: (2024) -
Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion
by: Xie, Chunlong, et al.
Published: (2025) -
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
by: Yin, Ziyi, et al.
Published: (2023) -
Crafting Adversarial Inputs for Large Vision-Language Models Using Black-Box Optimization
by: Guan, Jiwei, et al.
Published: (2026) -
Jailbreak Vision Language Models via Bi-Modal Adversarial Prompt
by: Ying, Zonghao, et al.
Published: (2024)