Recent News

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• [03/2024] Five papers at NAACL on LLM security (4 main and 1 finding): two on the backdoor attack, one on backdoor defense, one on jailbreak attacks, and one on model fingerprint. Stay tuned on these exciting fields
• [03/2024] PreDa for personalized federated learning is accepted at CVPR 2024.
• [01/2024] Three papers at ICLR.
• [01/2024] Two papers at TMLR
• [12/2023] Invited Talk at NeurIPS TDW workshop
• [10/2023]Our paper MoleculeSTM has been accepted to Nature Machine Intelligence. MoleculeSTM aims to align the nature language and molecule representation into the same representation space.
• [10/2023] Three papers at EMNLP and one paper at NeurIPS. For our NeurIPS paper, we study a new threat of the instruction tuning of LLMs by injecting the Ads. This is the first work that views the LLMs as the generative model and aims to attack the generative property of LLMs.
• [10/2023] Our tutorial on Security and Privacy in the Era of Large Language Models is accepted to NAACL.
• [05/2023] One paper at ACL. Congratulations to zhuofeng and jiazhao. We propose an attention-based method to defend against NLP backdoor attacks
• [04/2023] Two papers at ICML. Congratulations to Jiachen and Zhiyuan. We propose the first benchmark for code copyright of code generation models.
• [02/2023] Two papers at CVPR. Congratulations to Yiming and Xiaogeng. Xiaogeng is an intern from my group at ASU.
• [02/2023] I will give a tutorial at CVPR 2023 on the topic of trustworthiness in the era of Foundation Models. Stay tuned!
• [01/2023] Impact Award from Argonne National Laboratory.
• [01/2023] One paper got accepted to USENIX Security 2023.
• [1/2023] Three papers are accepted to ICLR 2023 [a]: We explain why and how to use diffusion model to improve adversarial robustness and design DensePure which leverages the pretrained diffusion model and classifier to provide the state-of-the-art certified robustness. [b]:This is our first attemp on retrieval-based framework and AI for drug discovery. We will recently release more work in this research line. Stay tuned!
• [12/2022] Our team won the ACM Gordon Bell Special Prize for COVID-19 Research.
• [09/2022] One papers got accepted to USENIX Security 2023.
• [09/2022] Two papers got accepted to NeurIPS 2022.
• [09/2022] Our paper RobustTraj has been accepted to CORL for oral presentations. We explore to train a robust Trajectory Prediction Model against adversarial attacks.
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[08/2022] I have moved to Phoenix and joined ASU as an assistant professor.
[08/2022] I will be giving a talk in virtual seminar series on Challenges and Opportunities for Security & Privacy in Machine Learning.
[07/2022] One survey paper to discuss the challenge and opportunity of machine learning security got accepted to ACM Computing Survey 2022.
[07/2022] Two papers got accepted to ECCV 2022.
[05/2022] Two papers got accepted to ICML 2022. Thanks for all of my collaborators.
[3/2022] I will be giving a talk in AAAI 2022 1st International Workshop on Practical Deep Learning in the Wild.
[3/2022] I will be giving a talk in AAAI 2022 workshop on Adversarial Machine Learning and Beyond.
[2/2022] One paper is accepted to ICLR.

Current PhD Students:

• Jiongxiao Wang
• Xiaogeng Liu
• Shutong Wu
• Fangzhou Wu

Preprints

• [New][LLM System Security] A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems.

  Fangzhou Wu, Ning Zhang, Somesh Jha, Patrick McDaniel, Chaowei Xiao.

  https://arxiv.org/abs/2402.18649
• [New][LLM Security] Mitigating Fine-tuning Jailbreak Attack with Backdoor Enhanced Alignment.

  Jiongxiao Wang, Jiazhao Li, Yiquan Li, Xiangyu Qi, Muhao Chen, Junjie Hu, Yixuan Li, Bo Li, Chaowei Xiao

  https://arxiv.org/abs/2402.14968
• [LLM For Driving] Dolphins: Multimodal Language Model for Driving.

  Yingzi Ma, Yulong Cao, Jiachen Sun, Marco Pavone, Chaowei Xiao

  https://arxiv.org/abs/2312.00438

Publications (Selected)

(* represents equal contribution)

[Full List], [Google Scholar]

• [LLM Safety]Instructional fingerprinting of large language models.

  Jiashu Xu, Fei Wang, Mingyu Derek Ma, Pang Wei Koh, Chaowei Xiao, Muhao Chen.

  NAACL 2024.
• [LLM Safety]AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models.

  Xiaogeng Liu, Nan Xu, Muhao Chen, Chaowei Xiao.

  ICLR 2024.
• [LLM Safety]On the exploitability of instruction tuning.

  Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao*, Tom Goldstein*.

  NeurIPS 2023 [pdf] [code]

• [LLM Copyright]CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models

  Zhiyuan Yu, Yuhao Wu, Ning Zhang, Chenguang Wang, Yevgeniy Vorobeychik, Chaowei Xiao

  ICML 2023. [pdf] [code]

• [LLM]Shall we pretrain autoregressive language models with retrieval? a comprehensive study.

  Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro

  EMNLP 2023

• [Multi-Modal LLM]Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning

  Zhuolin Yang, Wei Ping, Zihan Liu, Vijay Korthikanti, Weili Nie, De-An Huang, Linxi Fan, Zhiding Yu, Shiyi Lan, Bo Li, Ming-Yu Liu, Yuke Zhu, Mohammad Shoeybi, Bryan Catanzaro, Chaowei Xiao*, Anima Anandkumar*

  EMNLP 2023

• [Diffusion & Security] Diffusion Models for Adversarial Purification

  Weili Nie, Brandon Guo, Yujia Huang,Chaowei Xiao, Arash Vahdat, Anima Anandkumar.

  ICML 2022[pdf] [code]

• [Diffusion Model & Security] A Critical Revisit of Adversarial Robustness in 3D Point Cloud Recognition with Diffusion-Driven Purification.

  Jiachen Sun, Jiongxiao Wang, Weili Nie, Zhiding Yu, Zhuoqing Mao, Chaowei Xiao

  ICML 2023. [pdf] [code]

• [Diffusion & Security] DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing.

  Jiawei Zhang, , Zhongzhu Chen, Huan Zhang, Chaowei Xiao, Bo Li.

  USENIX Security 2023. [pdf] [code]

• [Diffusion & Security] DensePure: Understanding Diffusion Models towards Adversarial Robustness.

  Chaowei Xiao*, Zhongzhu Chen*, Kun Jin*, Jiongxiao Wang*, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, Dawn Song

  ICLR 2023. [pdf] [code]

• [AI for Social Good] Retrieval-based Controllable Molecule Generation

  Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao , Richard Baraniuk, Anima Anandkumar

  ICLR 2023 (spotlight). [pdf] [code]

• [AI for Social Good] GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics.

  Maxim Zvyagin*, Alexander Brace*, Kyle Hippe*, Yuntian Deng*, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael Irvin, J. Gregory Pauloski, Logan Ward, Valerie Hayot, Murali Emani, Sam Foreman, Zhen Xie, Diangen Lin, Maulik Shukla, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Ian Foster, James J. Davis, Michael E. Papka, Thomas Brettin, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, Arvind Ramanathan.

  ACM Gordon Bell Special Covid Prize[pdf] [code]

• [ViT & Robustness] Understanding the robustness in vision transformers.

  Daquan Zhou, Zhiding Yu, Enze Xie,Chaowei Xiao, Anima Anandkumar, Jiashi Feng, Jose M Alvarez.

  ICML 2022[pdf] [code]

• [Adv Training & Robustness] AugMax: Adversarial Composition of RandomAugmentations for Robust Training.

  Haotao Wang,Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, Zhangyang Wang.

  NeurIPS 2021

• [Security] Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks.

  Yulong Cao*, Ningfei Wang*,Chaowei Xiao*, Dawei Yang*, Jin Fang, RuigangYang, Qi Alfred Chen, Mingyan Liu, Bo Li.

  IEEE Symposium on Security and Privacy (Oakland) 2021

• [Security] MeshAdv: Adversarial Meshes for Visual Recognition

  Chaowei Xiao*, Dawei Yang*, Bo Li, Jia Deng, Mingyan Liu

  In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019. (oral) [pdf]

• [Security] Spatially Transformed Adversarial Examples

  Chaowei Xiao*, Jun-Yan Zhu*, Bo Li, Warren He, Mingyan Liu and Dawn Song

  In International Conference on Learning Representations (ICLR), 2018 [pdf]

• [Security] Generating Adversarial Examples with Adversarial Networks

  Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu and Dawn Song

  In International Joint Conference on Artificial Intelligence (IJCAI), 2018. [pdf]

• [Security] Robust Physical-World Attacks on Machine Learning Models

  Kevin Eykholt*, Ivan Evtimov*, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno and Dawn Song

  In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 [pdf]