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@@ -28,11 +28,51 @@ Here, we've summarized existing LVLM Attack methods in our survey paper👍.
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 ## Adversarial-Attack
-* **ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language** | [Github](https://github.com/daveredrum/ScanRefer)
-  * Dave Zhenyu Chen, Angel X. Chang, Matthias Nießner
-  * Technical University of Munich, Simon Fraser University
-  * [ECCV2020] https://arxiv.org/abs/1912.08830
-  * A dataset, two-stage approach, proposal-then-selection
+* **On the Adversarial Robustness of Multi-Modal Foundation Models** | 
+  * Christian Schlarmann, Matthias Hein
+  * University of Tubingen
+  * [ICCVworkshop2023] https://openaccess.thecvf.com/content/ICCV2023W/AROW/papers/Schlarmann_On_the_Adversarial_Robustness_of_Multi-Modal_Foundation_Models_ICCVW_2023_paper.pdf
+* **On Evaluating Adversarial Robustness of Large Vision-Language Models** | [Github](https://github.com/yunqing-me/AttackVLM)
+  * Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Cheung, Min Lin
+  * Singapore University of Technology and Design, Sea AI Lab, Tsinghua University, Renmin University of China
+  * [NeurIPs2023] https://arxiv.org/abs/2305.16934
+* **Adversarial Illusions in Multi-Modal Embeddings** | [Github](https://github.com/ebagdasa/adversarial_illusions)
+  * Tingwei Zhang, Rishi Jha, Eugene Bagdasaryan, Vitaly Shmatikov
+  * Cornell University, Cornell Tech
+  * [Arxiv2023] https://arxiv.org/abs/2308.11804
+* **Image Hijacks: Adversarial Images can Control Generative Models at Runtime** | [Github](https://github.com/euanong/image-hijacks)
+  * Luke Bailey, Euan Ong, Stuart Russell, Scott Emmons
+  * UC Berkeley, Harvard University, University of Cambridge
+  * [Arxiv2023] https://arxiv.org/abs/2309.00236
+* **How Robust is Google's Bard to Adversarial Image Attacks?** | [Github](https://github.com/thu-ml/Attack-Bard)
+  * Yinpeng Dong, Huanran Chen, Jiawei Chen, Zhengwei Fang, Xiao Yang, Yichi Zhang, Yu Tian, Hang Su, Jun Zhu
+  * Tsinghua University, RealAI
+  * [Arxiv2023] https://arxiv.org/abs/2309.11751
+* **Misusing Tools in Large Language Models With Visual Adversarial Examples** | 
+  * Xiaohan Fu, Zihan Wang, Shuheng Li, Rajesh K. Gupta, Niloofar Mireshghallah, Taylor Berg-Kirkpatrick, Earlence Fernandes
+  * University of California San Diego, University of Washington
+  * [Arxiv2023] https://arxiv.org/abs/2310.03185
+* **How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs** | [Github](https://github.com/UCSC-VLAA/vllm-safety-benchmark)
+  * Haoqin Tu, Chenhang Cui, Zijun Wang, Yiyang Zhou, Bingchen Zhao, Junlin Han, Wangchunshu Zhou, Huaxiu Yao, Cihang Xie
+  * UC Santa Cruz, UNC-Chapel Hill, University of Edinburgh, University of Oxford, AIWaves Inc.
+  * [Arxiv2023] https://arxiv.org/abs/2311.16101
+* **InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models** | 
+  * Xunguang Wang, Zhenlan Ji, Pingchuan Ma, Zongjie Li, Shuai Wang
+  * The Hong Kong University of Science and Technology
+  * [Arxiv2023] https://arxiv.org/abs/2312.01886
+* **An Image Is Worth 1000 Lies: Transferability of Adversarial Images across Prompts on Vision-Language Models** | [Github](https://github.com/Haochen-Luo/CroPA)
+  * Haochen Luo, Jindong Gu, Fengyuan Liu, Philip Torr
+  * University of Oxford
+  * [ICLR2024] https://arxiv.org/abs/2403.09766
+* **Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images** | [Github](https://github.com/KuofengGao/Verbose_Images)
+  * Kuofeng Gao, Yang Bai, Jindong Gu, Shu-Tao Xia, Philip Torr, Zhifeng Li, Wei Liu
+  * Tsinghua University, Tencent Technology (Beijing),  University of Oxford, Tencent Data Platform, Peng Cheng Laboratory
+  * [ICLR2024] https://arxiv.org/abs/2401.11170
+* **The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative** | [Github](https://github.com/ChengshuaiZhao0/The-Wolf-Within)
+  * Zhen Tan, Chengshuai Zhao, Raha Moraffah, Yifan Li, Yu Kong, Tianlong Chen, Huan Liu
+  * Arizona State University, Michigan State University, Harvard University
+  * [Arxiv2024] https://arxiv.org/abs/2402.14859
+
 
 
 ## Jailbreak-Attack