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@@ -116,15 +116,15 @@ Here, we've summarized existing LVLM Attack methods in our survey paper👍.
* Zedian Shao, Hongbin Liu, Yuepeng Hu, Neil Zhenqiang Gong
* Duke University
* [Arxiv2024] https://arxiv.org//abs/2407.09050
* **Manipulation Facing Threats: Evaluating Physical Vulnerabilities in End-to-End Vision Language Action Models** | #
* **Manipulation Facing Threats: Evaluating Physical Vulnerabilities in End-to-End Vision Language Action Models** |
* Hao Cheng, Erjia Xiao, Chengyuan Yu, Zhao Yao, Jiahang Cao, Qiang Zhang, Jiaxu Wang, Mengshu Sun, Kaidi Xu, Jindong Gu, Renjing Xu
* The Hong Kong University of Science and Technology, University of Oxford, Hohai University, Hunan University, Drexel University, Beijing University of Technology
* [Arxiv2024] https://arxiv.org/abs/2409.13174
* **AnyAttack: Towards Large-scale Self-supervised Generation of Targeted Adversarial Examples for Vision-Language Models** | [Github](https://github.com/jiamingzhang94/AnyAttack) #
* **AnyAttack: Towards Large-scale Self-supervised Generation of Targeted Adversarial Examples for Vision-Language Models** | [Github](https://github.com/jiamingzhang94/AnyAttack)
* Jiaming Zhang, Junhong Ye, Xingjun Ma, Yige Li, Yunfan Yang, Jitao Sang, Dit-Yan Yeung
* Hong Kong University of Science and Technology, Beijing Jiaotong University, Fudan University, Singapore Management University
* [Arxiv2024] https://arxiv.org//abs/2410.05346
* **Doubly-Universal Adversarial Perturbations: Deceiving Vision-Language Models Across Both Images and Text with a Single Perturbation** | #
* **Doubly-Universal Adversarial Perturbations: Deceiving Vision-Language Models Across Both Images and Text with a Single Perturbation** |
* Hee-Seon Kim, Minbeom Kim, Changick Kim
* Korea Advanced Institute of Science and Technology
* [Arxiv2024] https://arxiv.org/abs/2412.08108
@@ -132,27 +132,27 @@ Here, we've summarized existing LVLM Attack methods in our survey paper👍.
* Xuanming Cui, Alejandro Aparcedo, Young Kyun Jang, Ser-Nam Lim
* University of Central Florida
* [CVPR2024] https://openaccess.thecvf.com/content/CVPR2024/papers/Cui_On_the_Robustness_of_Large_Multimodal_Models_Against_Image_Adversarial_CVPR_2024_paper.pdf
* **Exploring the Transferability of Visual Prompting for Multimodal Large Language Models** | [Github](https://github.com/zycheiheihei/Transferable-Visual-Prompting) #
* **Exploring the Transferability of Visual Prompting for Multimodal Large Language Models** | [Github](https://github.com/zycheiheihei/Transferable-Visual-Prompting)
* Yichi Zhang, Yinpeng Dong, Siyuan Zhan, Tianzan Min, Hang Su, Jun Zhu
* Tsinghua University, RealAI, Pazhou Laboratory (Huangpu)
* [CVPR2024] https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Exploring_the_Transferability_of_Visual_Prompting_for_Multimodal_Large_Language_CVPR_2024_paper.pdf
* **Break the Visual Perception: Adversarial Attacks Targeting Encoded Visual Tokens of Large Vision-Language Models** | #
* **Break the Visual Perception: Adversarial Attacks Targeting Encoded Visual Tokens of Large Vision-Language Models** |
* Yubo Wang, Chaohu Liu, Yanqiu Qu, Haoyu Cao, Deqiang Jiang, Linli Xu
* University of Science and Technology of China, Tencent YouTu Lab
* [ACMMM2024] https://arxiv.org/abs/2410.06699
* **Pandora's Box: Towards Building Universal Attackers against Real-World Large Vision-Language Models** | #
* **Pandora's Box: Towards Building Universal Attackers against Real-World Large Vision-Language Models** |
* Daizong Liu, Mingyu Yang, Xiaoye Qu, Pan Zhou, Xiang Fang, Keke Tang, Yao Wan, Lichao Sun
* Peking University, Huazhong University of Science and Technology, Nanyang Technological University, Guangzhou University, Lehigh University
* [NeurIPS2024] https://openreview.net/forum?id=gDpWYpocE1
* **Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs** | #
* **Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs** |
* Linhao Huang, Xue Jiang, Zhiqiang Wang, Wentao Mo, Xi Xiao, Bo Han, Yongjie Yin, Feng Zheng
* Tsinghua University, Southern University of Science and Technology, Hong Kong Baptist University, Hong Kong University of Science and Technology, China Electronics Corporation
* [Arxiv2025] https://arxiv.org/abs/2501.01042
* **Effective Black-Box Multi-Faceted Attacks Breach Vision Large Language Model Guardrails** | #
* **Effective Black-Box Multi-Faceted Attacks Breach Vision Large Language Model Guardrails** |
* Yijun Yang, Lichao Wang, Xiao Yang, Lanqing Hong, Jun Zhu
* Tsinghua University, Huawei Noah’s Ark Lab
* [Arxiv2025] https://arxiv.org/abs/2502.05772
* **Universal Adversarial Attack on Aligned Multimodal LLMs** | #
* **Universal Adversarial Attack on Aligned Multimodal LLMs** |
* Temurbek Rahmatullaev, Polina Druzhinina, Matvey Mikhalchuk, Andrey Kuznetsov, Anton Razzhigaev
* n/a
* [Arxiv2025] https://arxiv.org/abs/2502.07987
@@ -238,34 +238,38 @@ Here, we've summarized existing LVLM Attack methods in our survey paper👍.
* Rylan Schaeffer, Dan Valentine, Luke Bailey, James Chua, Cristóbal Eyzaguirre, Zane Durante, Joe Benton, Brando Miranda, Henry Sleight, John Hughes, Rajashree Agrawal, Mrinank Sharma, Scott Emmons, Sanmi Koyejo, Ethan Perez
* City University of Hong Kong, University of Science and Technology, The University of Melbourne
* [Arxiv2024] https://arxiv.org/abs/2407.15211
* **MMJ-Bench: A Comprehensive Study on Jailbreak Attacks and Defenses for Vision Language Models** | #
* **MMJ-Bench: A Comprehensive Study on Jailbreak Attacks and Defenses for Vision Language Models** |
* Fenghua Weng, Yue Xu, Chengyan Fu, Wenjie Wang
* ShanghaiTech University
* [Arxiv2024] https://arxiv.org/abs/2408.08464
* **UniGuard: Towards Universal Safety Guardrails for Jailbreak Attacks on Multimodal Large Language Models** | #
* **UniGuard: Towards Universal Safety Guardrails for Jailbreak Attacks on Multimodal Large Language Models** |
* Sejoon Oh, Yiqiao Jin, Megha Sharma, Donghyun Kim, Eric Ma, Gaurav Verma, Srijan Kumar
* Netflix, Georgia Institute of Technology
* [Arxiv2024] https://arxiv.org/abs/2411.01703
* **Zer0-Jack: A Memory-efficient Gradient-based Jailbreaking Method for Black-box Multi-modal Large Language Models** | #
* **Zer0-Jack: A Memory-efficient Gradient-based Jailbreaking Method for Black-box Multi-modal Large Language Models** |
* Tiejin Chen, Kaishen Wang, Hua Wei
* Arizona State University
* [Arxiv2024] https://arxiv.org/abs/2411.07559
* **Jailbreak Large Visual Language Models Through Multi-Modal Linkage** | [Github](https://github.com/wangyu-ovo/MML) #
* **Jailbreak Large Visual Language Models Through Multi-Modal Linkage** | [Github](https://github.com/wangyu-ovo/MML)
* Yu Wang, Xiaofei Zhou, Yichen Wang, Geyuan Zhang, Tianxing He
* Chinese Academy of Sciences, University of Chinese Academy of Sciences, Tsinghua University, Shanghai Qi Zhi Institute, University of Chicago
* [Arxiv2024] https://arxiv.org/abs/2412.00473
* **Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models** | #
* **Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models** |
* Ma Teng, Jia Xiaojun, Duan Ranjie, Li Xinfeng, Huang Yihao, Chu Zhixuan, Liu Yang, Ren Wenqi
* Sun Yat-Sen University, Nanyang Technological University, Alibaba Group, Zhejiang University
* [Arxiv2024] https://arxiv.org/abs/2412.05934
* **Retention Score: Quantifying Jailbreak Risks for Vision Language Models** | #
* **Retention Score: Quantifying Jailbreak Risks for Vision Language Models** |
* Zaitang Li, Pin-Yu Chen, Tsung-Yi Ho
* The Chinese University of Hong Kong, IBM Research
* [AAAI2025] https://arxiv.org/abs/2412.17544
* **Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency** | #
* **Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency** |
* Shiji Zhao, Ranjie Duan, Fengxiang Wang, Chi Chen, Caixin Kang, Jialing Tao, YueFeng Chen, Hui Xue, Xingxing Wei
* Beihang University
* Beihang University
* [Arxiv2025] https://arxiv.org/abs/2501.04931
* **Distraction is All You Need for Multimodal Large Language Model Jailbreaking** | #
* Zuopeng Yang, Jiluan Fan, Anli Yan, Erdun Gao, Xin Lin, Tao Li, Kanghua mo, Changyu Dong
* Guangzhou University, The University of Adelaide, Shanghai Jiao Tong University
* [Arxiv2025] https://arxiv.org/abs/2502.10794
## Prompt-Injection
* **Abusing Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs** |
@@ -296,23 +300,23 @@ Here, we've summarized existing LVLM Attack methods in our survey paper👍.
* Jiachen Sun, Changsheng Wang, Jiongxiao Wang, Yiwei Zhang, Chaowei Xiao
* University of Michigan Ann arbor, University of Wisconsin Madison, University of Science and Technology of China
* [Arxiv2024] https://arxiv.org/abs/2405.10529
* **Empirical Analysis of Large Vision-Language Models against Goal Hijacking via Visual Prompt Injection** | #
* **Empirical Analysis of Large Vision-Language Models against Goal Hijacking via Visual Prompt Injection** |
* Subaru Kimura, Ryota Tanaka, Shumpei Miyawaki, Jun Suzuki, Keisuke Sakaguchi
* Tohoku University, NTT Corporation
* [Arxiv2024] https://arxiv.org/abs/2408.03554
* **Manipulation Facing Threats: Evaluating Physical Vulnerabilities in End-to-End Vision Language Action Models** | #
* **Manipulation Facing Threats: Evaluating Physical Vulnerabilities in End-to-End Vision Language Action Models** |
* Hao Cheng, Erjia Xiao, Chengyuan Yu, Zhao Yao, Jiahang Cao, Qiang Zhang, Jiaxu Wang, Mengshu Sun, Kaidi Xu, Jindong Gu, Renjing Xu
* The Hong Kong University of Science and Technology, University of Oxford, Hohai University, Hunan University, Drexel University, Beijing University of Technology
* [Arxiv2024] https://arxiv.org/abs/2409.13174
* **Exploring the Transferability of Visual Prompting for Multimodal Large Language Models** | [Github](https://github.com/zycheiheihei/Transferable-Visual-Prompting) #
* **Exploring the Transferability of Visual Prompting for Multimodal Large Language Models** | [Github](https://github.com/zycheiheihei/Transferable-Visual-Prompting)
* Yichi Zhang, Yinpeng Dong, Siyuan Zhan, Tianzan Min, Hang Su, Jun Zhu
* Tsinghua University, RealAI, Pazhou Laboratory (Huangpu)
* [CVPR2024] https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Exploring_the_Transferability_of_Visual_Prompting_for_Multimodal_Large_Language_CVPR_2024_paper.pdf
* **Pandora's Box: Towards Building Universal Attackers against Real-World Large Vision-Language Models** | #
* **Pandora's Box: Towards Building Universal Attackers against Real-World Large Vision-Language Models** |
* Daizong Liu, Mingyu Yang, Xiaoye Qu, Pan Zhou, Xiang Fang, Keke Tang, Yao Wan, Lichao Sun
* Peking University, Huazhong University of Science and Technology, Nanyang Technological University, Guangzhou University, Lehigh University
* [NeurIPS2024] https://openreview.net/forum?id=gDpWYpocE1
* **Effective Black-Box Multi-Faceted Attacks Breach Vision Large Language Model Guardrails** | #
* **Effective Black-Box Multi-Faceted Attacks Breach Vision Large Language Model Guardrails** |
* Yijun Yang, Lichao Wang, Xiao Yang, Lanqing Hong, Jun Zhu
* Tsinghua University, Huawei Noah’s Ark Lab
* [Arxiv2025] https://arxiv.org/abs/2502.05772
@@ -342,11 +346,11 @@ Here, we've summarized existing LVLM Attack methods in our survey paper👍.
* Siyuan Liang, Jiawei Liang, Tianyu Pang, Chao Du, Aishan Liu, Ee-Chien Chang, Xiaochun Cao
* National University of Singapore, Sun Yat-sen University, Beihang University
* [Arxiv2024] https://arxiv.org/abs/2406.18844
* **Membership Inference Attacks against Large Vision-Language Models** | [Github](https://github.com/LIONS-EPFL/VL-MIA) #
* **Membership Inference Attacks against Large Vision-Language Models** | [Github](https://github.com/LIONS-EPFL/VL-MIA)
* Zhan Li, Yongtao Wu, Yihang Chen, Francesco Tonin, Elias Abad Rocamora, Volkan Cevher
* University of California
* [NeurIPS2024] https://arxiv.org/abs/2411.02902
* **Membership Inference Attacks Against Vision-Language Models** | [Github](https://github.com/YukeHu/vlm_mia) #
* **Membership Inference Attacks Against Vision-Language Models** | [Github](https://github.com/YukeHu/vlm_mia)
* Yuke Hu, Zheng Li, Zhihao Liu, Yang Zhang, Zhan Qin, Kui Ren, Chun Chen
* Zhejiang University, Shandong University, CISPA Helmholtz Center for Information Security
* [USENIX'25] https://arxiv.org/abs/2501.18624