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262 lines
19 KiB
Markdown
# Awesome-LVLM-Attack [](https://github.com/sindresorhus/awesome)
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A **continual** collection of papers related to Attacks on Large-Vision-Language-Models (LVLMs).
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Large vision-language models (LVLMs) have achieved significant success and demonstrated promising capabilities in various multimodal downstream tasks.
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Despite their remarkable capabilities, the increased complexity and deployment of LVLMs have also exposed them to various security threats and vulnerabilities, making the study of attacks on these models a critical area of research.
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Here, we've summarized existing LVLM Attack methods in our survey paper👍.
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[A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends](https://arxiv.org/abs/2407.07403)
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> If you find some important work missed, it would be super helpful to let me know (`dzliu@stu.pku.edu.cn`). Thanks!
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> If you find our survey useful for your research, please consider citing:
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```
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@article{liu2024attack,
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title={A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends},
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author={Liu, Daizong and Yang, Mingyu and Qu, Xiaoye and Zhou, Pan and Hu, Wei and Cheng, Yu},
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journal={arXiv preprint arXiv:2407.07403},
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year={2024}
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}
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```
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**Table of Contents**
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- [Adversarial Attacks](#Adversarial-Attack)
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- [Jailbreack Attacks](#Jailbreack-Attack)
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- [Prompt Injection](#Prompt-Injection)
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- [Data Poisoning](#Data-Poisoning)
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---
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## Adversarial-Attack
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* **On the Adversarial Robustness of Multi-Modal Foundation Models** |
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* Christian Schlarmann, Matthias Hein
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* University of Tubingen
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* [ICCVworkshop2023] https://openaccess.thecvf.com/content/ICCV2023W/AROW/papers/Schlarmann_On_the_Adversarial_Robustness_of_Multi-Modal_Foundation_Models_ICCVW_2023_paper.pdf
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* **On Evaluating Adversarial Robustness of Large Vision-Language Models** | [Github](https://github.com/yunqing-me/AttackVLM)
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* Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Cheung, Min Lin
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* Singapore University of Technology and Design, Sea AI Lab, Tsinghua University, Renmin University of China
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* [NeurIPs2023] https://arxiv.org/abs/2305.16934
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* **VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models** | [Github](https://github.com/ericyinyzy/VLAttack)
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* Ziyi Yin, Muchao Ye, Tianrong Zhang, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma
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* The Pennsylvania State University, Zhejiang University, Xi’an Jiaotong University, Dalian University of Technology, Stony Brook University
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* [NeurIPs2023] [https://arxiv.org/abs/2312.03777](https://arxiv.org/abs/2310.04655)
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* **Adversarial Illusions in Multi-Modal Embeddings** | [Github](https://github.com/ebagdasa/adversarial_illusions)
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* Tingwei Zhang, Rishi Jha, Eugene Bagdasaryan, Vitaly Shmatikov
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* Cornell University, Cornell Tech
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* [Arxiv2023] https://arxiv.org/abs/2308.11804
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* **Image Hijacks: Adversarial Images can Control Generative Models at Runtime** | [Github](https://github.com/euanong/image-hijacks)
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* Luke Bailey, Euan Ong, Stuart Russell, Scott Emmons
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* UC Berkeley, Harvard University, University of Cambridge
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* [Arxiv2023] https://arxiv.org/abs/2309.00236
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* **How Robust is Google's Bard to Adversarial Image Attacks?** | [Github](https://github.com/thu-ml/Attack-Bard)
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* Yinpeng Dong, Huanran Chen, Jiawei Chen, Zhengwei Fang, Xiao Yang, Yichi Zhang, Yu Tian, Hang Su, Jun Zhu
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* Tsinghua University, RealAI
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* [Arxiv2023] https://arxiv.org/abs/2309.11751
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* **Misusing Tools in Large Language Models With Visual Adversarial Examples** |
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* Xiaohan Fu, Zihan Wang, Shuheng Li, Rajesh K. Gupta, Niloofar Mireshghallah, Taylor Berg-Kirkpatrick, Earlence Fernandes
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* University of California San Diego, University of Washington
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* [Arxiv2023] https://arxiv.org/abs/2310.03185
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* **How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs** | [Github](https://github.com/UCSC-VLAA/vllm-safety-benchmark)
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* Haoqin Tu, Chenhang Cui, Zijun Wang, Yiyang Zhou, Bingchen Zhao, Junlin Han, Wangchunshu Zhou, Huaxiu Yao, Cihang Xie
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* UC Santa Cruz, UNC-Chapel Hill, University of Edinburgh, University of Oxford, AIWaves Inc.
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* [Arxiv2023] https://arxiv.org/abs/2311.16101
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* **On the Robustness of Large Multimodal Models Against Image Adversarial Attacks** |
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* Xuanming Cui, Alejandro Aparcedo, Young Kyun Jang, Ser-Nam Lim
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* University of Central Florida
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* [Arxiv2023] https://arxiv.org/abs/2312.03777
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* **InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models** |
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* Xunguang Wang, Zhenlan Ji, Pingchuan Ma, Zongjie Li, Shuai Wang
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* The Hong Kong University of Science and Technology
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* [Arxiv2023] https://arxiv.org/abs/2312.01886
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* **OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization** |
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* Dongchen Han, Xiaojun Jia, Yang Bai, Jindong Gu, Yang Liu, Xiaochun Cao
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* Sun Yat-sen University, Nanyang Technological University, Tsinghua University, University of Oxford
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* [Arxiv2023] https://arxiv.org/abs/2312.04403
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* **An Image Is Worth 1000 Lies: Transferability of Adversarial Images across Prompts on Vision-Language Models** | [Github](https://github.com/Haochen-Luo/CroPA)
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* Haochen Luo, Jindong Gu, Fengyuan Liu, Philip Torr
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* University of Oxford
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* [ICLR2024] https://arxiv.org/abs/2403.09766
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* **Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images** | [Github](https://github.com/KuofengGao/Verbose_Images)
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* Kuofeng Gao, Yang Bai, Jindong Gu, Shu-Tao Xia, Philip Torr, Zhifeng Li, Wei Liu
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* Tsinghua University, Tencent Technology (Beijing), University of Oxford, Tencent Data Platform, Peng Cheng Laboratory
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* [ICLR2024] https://arxiv.org/abs/2401.11170
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* **Adversarial Robustness for Visual Grounding of Multimodal Large Language Models** |
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* Kuofeng Gao, Yang Bai, Jiawang Bai, Yong Yang, Shu-Tao Xia
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* Tsinghua University, Tencent Security Platform, Peng Cheng Laboratory
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* [ICLRworkshop2024] https://arxiv.org/abs/2405.09981
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* **Transferable Multimodal Attack on Vision-Language Pre-training Models** |
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* Haodi Wang, Kai Dong, Zhilei Zhu, Haotong Qin, Aishan Liu, Xiaolin Fang, Jiakai Wang, Xianglong Liu
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* Southeast University, Data Space Research Institute of Hefei Comprehensive National Science Centre, Beihang University, Southeast University, Zhongguancun Laboratory
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* [S&P2024] https://www.computer.org/csdl/proceedings-article/sp/2024/313000a102/1Ub239H4xyg
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* **On the Safety Concerns of Deploying LLMs/VLMs in Robotics: Highlighting the Risks and Vulnerabilities** |
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* Xiyang Wu, Ruiqi Xian, Tianrui Guan, Jing Liang, Souradip Chakraborty, Fuxiao Liu, Brian Sadler, Dinesh Manocha, Amrit Singh Bedi
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* University of Maryland, Army Research Laboratory, University of Central Florida
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* [Arxiv2024] https://arxiv.org/abs/2402.10340
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* **The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative** | [Github](https://github.com/ChengshuaiZhao0/The-Wolf-Within)
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* Zhen Tan, Chengshuai Zhao, Raha Moraffah, Yifan Li, Yu Kong, Tianlong Chen, Huan Liu
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* Arizona State University, Michigan State University, Harvard University
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* [Arxiv2024] https://arxiv.org/abs/2402.14859
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* **Stop Reasoning! When Multimodal LLMs with Chain-of-Thought Reasoning Meets Adversarial Images** |
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* Zefeng Wang, Zhen Han, Shuo Chen, Fan Xue, Zifeng Ding, Xun Xiao, Volker Tresp, Philip Torr, Jindong Gu
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* Technical University of Munich, Ludwig Maximilian University of Munich, Huawei Munich Research Center, University of Oxford
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* [Arxiv2024] https://arxiv.org/abs/2402.14899
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* **AVIBench: Towards Evaluating the Robustness of Large Vision-Language Model on Adversarial Visual-Instructions** |
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* Hao Zhang, Wenqi Shao, Hong Liu, Yongqiang Ma, Ping Luo, Yu Qiao, Kaipeng Zhang
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* Xi’an Jiaotong University, Shanghai Artificial Intelligence Laboratory, Osaka University
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* [Arxiv2024] https://arxiv.org/abs/2403.09346
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* **Efficiently Adversarial Examples Generation for Visual-Language Models under Targeted Transfer Scenarios using Diffusion Models** |
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* Qi Guo, Shanmin Pang, Xiaojun Jia, Qing Guo
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* Xi’an Jiaotong University, Nanyang Technological University, Center for Frontier AI Research
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* [Arxiv2024] https://arxiv.org/abs/2404.10335
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* **Adversarial Attacks on Multimodal Agents** |
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* Chen Henry Wu, Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried, Aditi Raghunathan
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* Carnegie Mellon University
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* [Arxiv2024] https://arxiv.org/abs/2406.12814
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* **Refusing Safe Prompts for Multi-modal Large Language Models** |
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* Zedian Shao, Hongbin Liu, Yuepeng Hu, Neil Zhenqiang Gong
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* Duke University
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* [Arxiv2024] https://arxiv.org//abs/2407.09050
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## Jailbreak-Attack
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* **Are aligned neural networks adversarially aligned?** |
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* Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramer, Ludwig Schmidt
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* Google DeepMind, Stanford, University of Washington, ETH Zurich
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* [NeurIPs2023] https://arxiv.org/abs/2306.15447
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* **FigStep: Jailbreaking Large Vision-language Models via Typographic Visual Prompts** | [Github](https://github.com/ThuCCSLab/FigStep)
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* Yichen Gong, Delong Ran, Jinyuan Liu, Conglei Wang, Tianshuo Cong, Anyu Wang, Sisi Duan, Xiaoyun Wang
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* Tsinghua University, Shandong University, Carnegie Mellon University
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* [Arxiv2023] https://arxiv.org/abs/2311.05608
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* **Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts** |
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* Yuanwei Wu, Xiang Li, Yixin Liu, Pan Zhou, Lichao Sun
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* Huazhong University of Science and Technology, Lehigh University
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* [Arxiv2023] https://arxiv.org/abs/2311.09127
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* **MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models** | [Github](https://github.com/isXinLiu/MM-SafetyBench)
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* Xin Liu, Yichen Zhu, Jindong Gu, Yunshi Lan, Chao Yang, Yu Qiao
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* Shanghai AI Laboratory, East China Normal University, Midea Group, University of Oxford
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* [Arxiv2023] https://arxiv.org/abs/2311.17600
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* **Visual Adversarial Examples Jailbreak Aligned Large Language Models** | [Github](https://github.com/Unispac/Visual-Adversarial-Examples-Jailbreak-Large-Language-Models)
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* Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Peter Henderson, Mengdi Wang, Prateek Mittal
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* Princeton University, Stanford University
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* [AAAI2024] https://arxiv.org/abs/2306.13213
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* **Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models** |
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* Erfan Shayegani, Yue Dong, Nael Abu-Ghazaleh
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* University of California
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* [ICLR2024] https://arxiv.org/abs/2307.14539
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* **Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast** | [Github](https://github.com/sail-sg/Agent-Smith)
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* Xiangming Gu, Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Ye Wang, Jing Jiang, Min Lin
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* Sea AI Lab, National University of Singapore, Singapore Management University
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* [ICML2024] https://arxiv.org/abs/2402.08567
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* **Learning To See But Forgetting To Follow: Visual Instruction Tuning Makes LLMs More Prone To Jailbreak Attacks** | [Github](https://github.com/gpantaz/vl_jailbreak)
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* Georgios Pantazopoulos, Amit Parekh, Malvina Nikandrou, Alessandro Suglia
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* Heriot-Watt University
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* [COLINGworkshop2024] https://arxiv.org/abs/2405.04403
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* **Jailbreaking Attack against Multimodal Large Language Model** |
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* Zhenxing Niu, Haodong Ren, Xinbo Gao, Gang Hua, Rong Jin
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* Xidian University, Wormpex AI Research, Meta
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* [Arxiv2024] https://arxiv.org/abs/2402.02309
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* **ImgTrojan: Jailbreaking Vision-Language Models with ONE Image** | [Github](https://github.com/xijia-tao/ImgTrojan)
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* Xijia Tao, Shuai Zhong, Lei Li, Qi Liu, Lingpeng Kong
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* The University of Hong Kong
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* [Arxiv2024] https://arxiv.org/abs/2403.02910
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* **Images are Achilles' Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models** | [Github](https://github.com/AoiDragon/HADES)
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* Yifan Li, Hangyu Guo, Kun Zhou, Wayne Xin Zhao, Ji-Rong Wen
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* Renmin University of China, Beijing Key Laboratory of Big Data Management and Analysis Methods
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* [Arxiv2024] https://arxiv.org/abs/2403.09792
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* **Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?** |
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* Shuo Chen, Zhen Han, Bailan He, Zifeng Ding, Wenqian Yu, Philip Torr, Volker Tresp, Jindong Gu
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* LMU Munich, University of Oxford, Siemens AG, Munich Center for Machine Learning, Wuhan University
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* [Arxiv2024] https://arxiv.org/abs/2404.03411
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* **JailBreakV-28K: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks** | [Github](https://github.com/EddyLuo1232/JailBreakV_28K)
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* Weidi Luo, Siyuan Ma, Xiaogeng Liu, Xiaoyu Guo, Chaowei Xiao
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* The Ohio State University, University of Wisconsin-Madison
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* [Arxiv2024] https://arxiv.org/abs/2404.03027
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* **Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security** |
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* Yihe Fan, Yuxin Cao, Ziyu Zhao, Ziyao Liu, Shaofeng Li
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* TongJi University, Tsinghua University, Beijing University of Technology, Nanyang Technological University, Peng Cheng Laboratory
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* [Arxiv2024] https://arxiv.org/abs/2404.05264
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* **White-box Multimodal Jailbreaks Against Large Vision-Language Models** |
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* Ruofan Wang, Xingjun Ma, Hanxu Zhou, Chuanjun Ji, Guangnan Ye, Yu-Gang Jiang
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* Fudan University, Shanghai Jiao Tong University, DataGrand Tech
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* [Arxiv2024] https://arxiv.org/abs/2405.17894
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* **From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking** |
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* Siyuan Wang, Zhuohan Long, Zhihao Fan, Zhongyu Wei
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* University of Southern California, Fudan University, Alibaba Inc.
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* [Arxiv2024] https://arxiv.org/abs/2406.14859
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* **Unveiling the Safety of GPT-4o: An Empirical Study using Jailbreak Attacks** |
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* Zonghao Ying, Aishan Liu, Xianglong Liu, Dacheng Tao
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* Beihang University, Nanyang Technological University
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* [Arxiv2024] https://arxiv.org/abs/2406.06302
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* **Visual-RolePlay: Universal Jailbreak Attack on MultiModal Large Language Models via Role-playing Image Character** |
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* Siyuan Ma, Weidi Luo, Yu Wang, Xiaogeng Liu
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* University of Wisconsin–Madison, The Ohio State University, Peking University
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* [Arxiv2024] https://arxiv.org/abs/2405.20773
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* **Arondight: Red Teaming Large Vision Language Models with Auto-generated Multi-modal Jailbreak Prompts** |
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* Yi Liu, Chengjun Cai, Xiaoli Zhang, Xingliang Yuan, Cong Wang
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* Stanford, Harvard, Anthropic, Constellation, MIT, UC Berkeley
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* [Arxiv2024] https://arxiv.org/abs/2407.15050
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* **When Do Universal Image Jailbreaks Transfer Between Vision-Language Models?** |
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* 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
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* City University of Hong Kong, University of Science and Technology, The University of Melbourne
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* [Arxiv2024] https://arxiv.org/abs/2407.15211
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## Prompt-Injection
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* **Abusing Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs** |
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* Eugene Bagdasaryan, Tsung-Yin Hsieh, Ben Nassi, Vitaly Shmatikov
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* Cornell Tech
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* [Arxiv2023] https://arxiv.org/abs/2307.10490
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* **Can Language Models be Instructed to Protect Personal Information?** |
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* Yang Chen, Ethan Mendes, Sauvik Das, Wei Xu, Alan Ritter
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* Georgia Institute of Technology, Carnegie Mellon University
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* [Arxiv2023] https://arxiv.org/abs/2310.02224
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* **FigStep: Jailbreaking Large Vision-language Models via Typographic Visual Prompts** | [Github](https://github.com/ThuCCSLab/FigStep)
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* Yichen Gong, Delong Ran, Jinyuan Liu, Conglei Wang, Tianshuo Cong, Anyu Wang, Sisi Duan, Xiaoyun Wang
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* Tsinghua University, Shandong University, Carnegie Mellon University
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* [Arxiv2023] https://arxiv.org/abs/2311.05608
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* **MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models** | [Github](https://github.com/isXinLiu/MM-SafetyBench)
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* Xin Liu, Yichen Zhu, Jindong Gu, Yunshi Lan, Chao Yang, Yu Qiao
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* Shanghai AI Laboratory, East China Normal University, Midea Group, University of Oxford
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* [Arxiv2023] https://arxiv.org/abs/2311.17600
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* **MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance** | [Github](https://github.com/pipilurj/MLLM-protector)
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* Renjie Pi, Tianyang Han, Yueqi Xie, Rui Pan, Qing Lian, Hanze Dong, Jipeng Zhang, Tong Zhang
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* The Hong Kong University of Science and Technology, University of Illinois at Urbana-Champaign, The Hong Kong Polytechnic University
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* [Arxiv2024] https://arxiv.org/abs/2401.02906
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* **Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks** | [Github](https://github.com/mqraitem/Self-Gen-Typo-Attack)
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* Maan Qraitem, Nazia Tasnim, Piotr Teterwak, Kate Saenko, Bryan A. Plummer
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* Boston University
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* [Arxiv2024] https://arxiv.org/abs/2402.00626
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* **Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors** |
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* Jiachen Sun, Changsheng Wang, Jiongxiao Wang, Yiwei Zhang, Chaowei Xiao
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* University of Michigan Ann arbor, University of Wisconsin Madison, University of Science and Technology of China
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* [Arxiv2024] https://arxiv.org/abs/2405.10529
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* **Empirical Analysis of Large Vision-Language Models against Goal Hijacking via Visual Prompt Injection** | #
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* Subaru Kimura, Ryota Tanaka, Shumpei Miyawaki, Jun Suzuki, Keisuke Sakaguchi
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* Tohoku University, NTT Corporation
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* [Arxiv2024] https://arxiv.org/abs/2408.03554
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## Data-Poisoning
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* **Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models** | [Github](https://github.com/umd-huang-lab/VLM-Poisoning)
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* Yuancheng Xu, Jiarui Yao, Manli Shu, Yanchao Sun, Zichu Wu, Ning Yu, Tom Goldstein, Furong Huang
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* University of Maryland, College Park, JPMorgan AI Research, University of Waterloo, Salesforce Research
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* [Arxiv2024] https://arxiv.org/abs/2402.06659
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* **PoisonedRAG: Knowledge Poisoning Attacks to Retrieval-Augmented Generation of Large Language Models** | [Github](https://github.com/sleeepeer/PoisonedRAG)
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* Wei Zou, Runpeng Geng, Binghui Wang, Jinyuan Jia
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* Pennsylvania State University, Wuhan University, Illinois Institute of Technology
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* [Arxiv2024] https://arxiv.org/abs/2402.07867
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* **Test-Time Backdoor Attacks on Multimodal Large Language Models** | [Github](https://github.com/sail-sg/AnyDoor)
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* Dong Lu, Tianyu Pang, Chao Du, Qian Liu, Xianjun Yang, Min Lin
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* Southern University of Science and Technology, Sea AI Lab, University of California
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* [Arxiv2024] https://arxiv.org/abs/2402.08577
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* **VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models** |
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* Jiawei Liang, Siyuan Liang, Man Luo, Aishan Liu, Dongchen Han, Ee-Chien Chang, Xiaochun Cao
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* Sun Yat-sen University
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* [Arxiv2024] https://arxiv.org/abs/2402.13851
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* **Physical Backdoor Attack can Jeopardize Driving with Vision-Large-Language Models** |
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* Zhenyang Ni, Rui Ye, Yuxi Wei, Zhen Xiang, Yanfeng Wang, Siheng Chen
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* Shanghai Jiao Tong University, University of Illinois Urbana-Champaign, Shanghai AI Laboratory, Multi-Agent Governance & Intelligence Crew
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* [Arxiv2024] https://arxiv.org/abs/2404.12916
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* **Revisiting Backdoor Attacks against Large Vision-Language Models** |
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* Siyuan Liang, Jiawei Liang, Tianyu Pang, Chao Du, Aishan Liu, Ee-Chien Chang, Xiaochun Cao
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* National University of Singapore, Sun Yat-sen University, Beihang University
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* [Arxiv2024] https://arxiv.org/abs/2406.18844
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