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Chaowei Xiao

Email: cxiao34@wisc.edu

I am Chaowei Xiao, an assistant professor at the University of Wisconsin, Madison (Starting from 2023).

My research aims to study from both model and system perspectives to secure AI systems with both practical robustness and provable guarantee as well as exploring their roles in real-world application (e.g., autonomous driving, agents, IoT, and healthcare ).


I obtained my Ph.D. from the University of Michigan, Ann Arbor, and my bachelor's degree from Tsinghua University. Before joining UW-Madison, I spent two years at NVIDIA (2020-2022) as a full-time research scientist and a wonderful year at ASU (2022-2023).

[Google Scholar]

Award

Recent Invited talks

  • [12/2024] Invited talk at SFU@NeurIPS
  • [12/2024] Invited talk at LLM and Agent Safety Competition@NeurIPS.
  • [10/2024] Keynote at CCS Workshop on Large AI Systems and Models with Privacy and Safety Analysis.
  • [10/2024] Invited talk at Trillion Parameter Consortium (TPC) on LLM safety and security
  • [10/2024] Invited talk at NSF Workshop on Large Language Models for Network Security
  • [06/2024] Invited talk at CVPR Workshop of Adversarial Machine Learning on Computer Vision: Robustness of Foundation Models
  • [06/2024] Talk at NAACL tutorial on Combating Security and Privacy Issues in the Era of Large Language Models
  • [05/2024] Invited talk at ICLR Secure and Trustworthy Large Language Models
  • [12/2023] Invited Talk at NeurIPS TDW workshop

Recent News

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  • [1/2025] We have 9 papers at ICLR and 3 papers at NAACL on Model Safety and Security. Congratulations to all authors.
  • [12/2024] We got the Fall Research Competition Award at UW-Madison. Thank you UW-Madison and OVCR.
  • [11/2024] Our group recently received funding and donations. Thank you Amazon and Apple.
  • [11/2024] Our lab will have a winter break this Dec. Lab members will enjoy some well-deserved vacation time, with their families and loved ones.
  • [09/2024] We have four papers at NeurIPS regular track.
  • [09/2024]Our study on safety of RLFH Alignment is accepted to S&P (Oakland) 2025
  • [07/2024] MultiModal jailbreak benchmark is accepted to COLM. It is from the interns in my group.
  • [07/2024] 4/4 papers are accepted to ECCV on the topic of trustworthy VLM and driving. Two of them are from interns in my group.
  • [06/2024] Senior Area Chair for NeurIPS Benchmark track
  • [06/2024] I am currently serving as AC for the NeurIPS regular track
  • [05/2024] Our jailbreak paper is accepted to USENIX Security. Congratulations, Zhiyuan!
  • [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.
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  • [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.
  • [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.

Preprints

  • [New][Agent 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

Selected Publications ([Full List])

(* represents equal contribution)

[Google Scholar]

  • [LLM Security] AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs

    Xiaogeng Liu*, Peiran Li*, G. Edward Suh, Yevgeniy Vorobeychik, Zhuoqing Mao, Somesh Jha, Patrick McDaniel, Huan Sun, Bo Li, Chaowei Xiao

    ICLR 2024
  • [LLM Security]Can Watermarks be Used to Detect LLM IP Infringement For Free?

    Zhengyue Zhao, Xiaogeng Liu, Somesh Jha, Patrick McDaniel, Bo Li, Chaowei Xiao

    ICLR 2024
  • [Agent Security] EIA: Environmental Injection Attack on Generalist Web Agent for Privacy Leakage.

    Zeyi Liao, Lingbo Mo, Chejian Xu, Mintong Kang, Jiawei Zhang, Chaowei Xiao, Yuan Tian, Bo Li, Huan Sun

    ICLR 2024
  • [LLM for Science]LeanAgent: Lifelong Learning for Formal Theorem Proving.

    Adarsh Kumarappan, Mo Tiwari, Peiyang Song, Robert Joseph George, Chaowei Xiao, Anima Anandkumar.

    ICLR 2024
  • [LLM Safety (Hallucination)]HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection.

    Xuefeng Du, Chaowei Xiao, Yixuan Li

    NeurIPS 2024 (Oral)
  • [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

    NeurIPS 2024
  • [LLM For Driving] Dolphins: Multimodal Language Model for Driving.

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

    ECCV 2024
  • [ Embodied Agent] Voyager: An Open-Ended Embodied Agent with Large Language Models.

    Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar

    TMLR 2024
  • [LLM Fingerprint]Instructional fingerprinting of large language models.

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

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

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

    ICLR 2024.
  • [LLM Security]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]

  • [Diffusion & Security] Diffusion Models for Adversarial Purification

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

    ICML 2022[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]

  • [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]

  • [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] 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]