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@@ -255,6 +255,14 @@ Here, we've summarized existing LVLM Attack methods in our survey paper👍.
* Jaehyun Kwak, Nam Cao, Boryeong Cho, Segyu Lee, Sumyeong Ahn, Se-Young Yun
* KAIST, KENTECH
* [Arxiv2026] https://arxiv.org/abs/2602.04356
* **PA-Attack: Guiding Gray-Box Attacks on LVLM Vision Encoders with Prototypes and Attention** | #
* Hefei Mei, Zirui Wang, Chang Xu, Jianyuan Guo, Minjing Dong
* City University of Hong Kong, The University of Sydney
* [CVPR2026] https://arxiv.org/abs/2602.19418
* **Multi-Paradigm Collaborative Adversarial Attack Against Multi-Modal Large Language Models** | #
* Yuanbo Li, Tianyang Xu, Cong Hu, Tao Zhou, Xiao-Jun Wu, Josef Kittler
* Jiangnan University, University of Surrey
* [CVPR2026] https://arxiv.org/abs/2603.04846
## Jailbreak-Attack
* **Are aligned neural networks adversarially aligned?** |
@@ -563,6 +571,10 @@ Here, we've summarized existing LVLM Attack methods in our survey paper👍.
* In Chong Choi, Jiacheng Zhang, Feng Liu, Yiliao Song
* The University of Melbourne, The University of Adelaide
* [Arxiv2026] https://arxiv.org/abs/2602.14399
* **Image-based Prompt Injection: Hijacking Multimodal LLMs through Visually Embedded Adversarial Instructions** | #
* Neha Nagaraja, Lan Zhang, Zhilong Wang, Bo Zhang, Pawan Patil
* Northern Arizona University, Bytedance
* [Arxiv2026] https://arxiv.org/abs/2603.03637
## Data-Poisoning
* **Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models** | [Github](https://github.com/umd-huang-lab/VLM-Poisoning)
@@ -735,6 +747,10 @@ Here, we've summarized existing LVLM Attack methods in our survey paper👍.
* Yuxuan Zhou, Yang Bai, Kuofeng Gao, Tao Dai, Shu-Tao Xia
* Tsinghua University, Shenzhen University, ByteDance
* [Arxiv2025] https://arxiv.org/abs/2511.07315
* **SlowBA: An efficiency backdoor attack towards VLM-based GUI agents** | #
* Junxian Li, Tu Lan, Haozhen Tan, Yan Meng, Haojin Zhu
* Shanghai Jiao Tong University
* [Arxiv2026] https://arxiv.org/abs/2603.08316
## Benchmarks
* **Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark Study** | [Github](https://github.com/oneonlee/Meme-Safety-Bench) #