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Large language models have become increasingly prominent, also signaling a shift towards multimodality as the next frontier in artificial intelligence, where their embeddings are harnessed as prompts to generate textual content.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Mai Nguyen, Jason Yosinski, and Jeff Clune. 2015 · 2015
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Adversarial patch
Tom Brown, Dandelion Mane, Aurko Roy, Martin Abadi, and Justin Gilmer. 2017 · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2017 · 2017
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Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok. 2018 · 2018
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Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. 2019 · 2019
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Object detection with deep learning: A review
Zhong-Qiu Zhao, Peng Zheng, Shou-tao Xu, and Xindong Wu. 2019 · 2019
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Adversarial attacks on deep-learning models in natural language processing: A survey
Wei Emma Zhang, Quan Z Sheng, Ahoud Alhazmi, and Chenliang Li. 2020 · 2020
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Towards defending against adversarial examples via attack-invariant features
Dawei Zhou, Tongliang Liu, Bo Han, Nannan Wang, Chunlei Peng, and Xinbo Gao. 2021 · 2021
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Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. 2022 · 2022
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Diffusion models for adversarial purification
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Anima Anandkumar. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Densepure: Understanding diffusion models towards adversarial robustness
Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, and Dawn Song. 2022 · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Cited alongside, same era.
Image hijacks: Adversarial images can control generative models at runtime
Luke Bailey, Euan Ong, Stuart Russell, and Scott Emmons. 2023 · 2023
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Dynamic adversarial attacks on autonomous driving systems
Amirhosein Chahe, Chenan Wang, Abhishek Jeyapratap, Kaidi Xu, and Lifeng Zhou. 2023 · 2023
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Jailbreaking black box large language models in twenty queries
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong. 2023 · 2023
Cited alongside, same era.
Jailbreak in pieces: Compositional adversarial attacks on multi-modal language models
Erfan Shayegani, Yue Dong, and Nael Abu-Ghazaleh. 2023 · 2023
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Defending against physical adversarial patch attacks on infrared human detection
Lukas Strack, Futa Waseda, Huy H Nguyen, Yinqiang Zheng, and Isao Echizen. 2023 · 2023
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Better diffusion models further improve adversarial training
Zekai Wang, Tianyu Pang, Chao Du, Min Lin, Weiwei Liu, and Shuicheng Yan. 2023 · 2023
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{ \{ DiffSmooth } \} : Certifiably robust learning via diffusion models and local smoothing
Jiawei Zhang, Zhongzhu Chen, Huan Zhang, Chaowei Xiao, and Bo Li. 2023 · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
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Xi Chen, Xiao Wang, Lucas Beyer, Alexander Kolesnikov, Jialin Wu, Paul Voigtlaender, Basil Mustafa, Sebastian Goodman, Ibrahim Alabdulmohsin, Piotr Padlewski, et al. 2023 · 2023
Cited alongside, same era.
Aligning language models with preferences through f-divergence minimization
Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Nahyeon Ryu, and Marc Dymetman. 2023 · 2023
Cited alongside, same era.
Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection
Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. 2023 · 2023
Cited alongside, same era.
Pretraining language models with human preferences
Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Vinayak Bhalerao, Christopher Buckley, Jason Phang, Samuel R Bowman, and Ethan Perez. 2023 · 2023
Cited alongside, same era.
Robust evaluation of diffusion-based adversarial purification
Minjong Lee and Dongwoo Kim. 2023 · 2023
Cited alongside, same era.
Visual adversarial examples jailbreak large language models
Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Mengdi Wang, and Prateek Mittal. 2023 · 2023
Cited alongside, same era.
Smoothllm: Defending large language models against jailbreaking attacks
Alexander Robey, Eric Wong, Hamed Hassani, and George J Pappas. 2023 · 2023
Cited alongside, same era.
Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. 2023a
Cited in the paper.
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson. 2023 · 2023
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024 · 2024
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Optimization-based prompt injection attack to llm-as-a-judge
Jiawen Shi, Zenghui Yuan, Yinuo Liu, Yue Huang, Pan Zhou, Lichao Sun, and Neil Zhenqiang Gong. 2024 · 2024
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Chong Xiang, Tong Wu, Sihui Dai, Jonathan Petit, Suman Jana, and Prateek Mittal. 2024 · 2024
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Vision-language models for vision tasks: A survey
Jingyi Zhang, Jiaxing Huang, Sheng Jin, and Shijian Lu. 2024 · 2024
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{ \{ PatchCleanser } \} : Certifiably robust defense against adversarial patches for any image classifier
Chong Xiang, Saeed Mahloujifar, and Prateek Mittal. 2022 · 2082
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