CV

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Research Interests

AI safety and adversarial robustness of large language models: red-teaming of LLM and computer-use agents, indirect prompt injection, jailbreaking, and safety alignment.

Education

Simon Fraser University, Burnaby, Canada — Ph.D. in Computing Science, Sep 2022 – Present

  • Advised by Prof. Ke Wang. GPA: 4.21/4.33
  • Awards: Special Graduate Entrance Scholarship, Graduate Fellowship (×6)

Shenzhen University, Shenzhen, China — B.E. with Honors in Artificial Intelligence, Sep 2018 – Jun 2022

  • GPA: 3.96/4.5 (Rank: 4/103)
  • Awards: Outstanding Graduate, Outstanding Bachelor Thesis, Star of SZU Scholarship, MCM Meritorious Winner

Research Experience

RBC Borealis (Borealis AI), Vancouver, Canada — Machine Learning Researcher (Intern), Jan 2026 – May 2026

Project: Computer-Use Agent Safety

  • Introduced multi-step indirect prompt injection, a new attack class that decomposes an adversarial goal into innocuous sub-steps spread across a chain of web pages.
  • Built an automatic LLM-driven pipeline for adversarial goal decomposition.
  • Released the CUA safety benchmark StepJack, and evaluated six state-of-the-art computer-use agents.
  • Raised attack success rate on GPT-5.4-mini from 41.7% at single-step to 72.9% at three-step.
  • Research paper submitted, with patent filed and code open-sourced.

Publications & Preprints

  1. Zhuoxin Zhan, A. Rafiey, A. Ma, L. Pishdad, L. El Asri. StepJack: Benchmarking Computer-Use Agent Safety Against Multi-Step Indirect Prompt Injection. Preprint, arXiv 2026. paper · code
  2. Zhuoxin Zhan, K. Wang, P. Xiong, L. Li. Benign Prompts Can Jailbreak Large Language Models. Under submission, 2026.
  3. Zhuoxin Zhan, K. Wang, P. Xiong. Accelerating Adversarial Training on Under-Utilized GPU. IJCAI 2025. paper · code
  4. Zhuoxin Zhan, M. He, W. Pan, Z. Ming. TransRec++: Translation-Based Sequential Recommendation with Heterogeneous Feedback. Frontiers of Computer Science, 2022. paper
  5. Zhuoxin Zhan, L. Zhong, J. Lin, W. Pan, Z. Ming. Sequence-Aware Similarity Learning for Next-Item Recommendation. The Journal of Supercomputing, 2021.
  6. Y. Ni, Zhuoxin Zhan, W. Pan, Z. Ming. Asymmetric Pairwise Preference Learning for Heterogeneous One-Class Collaborative Filtering. ICONIP 2020.
  7. J. Lin, Zhuoxin Zhan, W. Pan, Z. Ming. Single-Behavior Sequential Recommendation. Book chapter, Intelligent Recommendation Technology, Tsinghua University Press, 2022 (in Chinese). book

Services

Conference Reviewer — NeurIPS '26, ICML '26, EMNLP '26, WWW '26, WSDM '25, CIKM '25/'24, KDD '24, BigData '24

Student Chair, Artificial Intelligence Club, Shenzhen University — Sep 2019 – Jul 2021

  • Collaborated with sponsors (Tencent, IBM, Baidu) to organize events such as IBM summer camps and Baidu seminars.

Technical Skills

  • Languages: Python, C/C++, Java, SQL
  • ML / AI: PyTorch, HuggingFace Transformers, vLLM, scikit-learn, NumPy, Pandas, Weights & Biases