• I got my PHD from Department of Computer Science at Rutgers University on May 2025, advised by Prof. Shiqing Ma.

• I work on AI for security and Agent/LLm security. I build AI-driven pipelines to automate security workflows spanning auditing, anomaly detection, and forensics. My current focus is the security of AI agent/LLM systems, encompassing policy enforcement (e.g., OPA), MCP gateway controls, automation and continuous assessment. I also investigate AI model security, with emphasis on backdoor attacks and interpretability as tools to diagnose and mitigate unsafe model behaviors.

• I am currently a research scientist at IBM Research. Previously, I was an research intern at IBM Research, and a research intern at Bell Labs. Before my PHD, I earned my B.E. from Central South University in 2020.

News

• [2025-02] I joined IBM Research as a research scientist.
• [2024-08] One paper was accepted by ACSAC 2024.
• [2024-05] I joined IBM Research as a research intern.
• [2024-04] One paper was accepted by ASPLOS 2024.
• [2023-06] I joined Bell Lab as a Machine Learning and AI intern.
• [2023-05] Two papers were accepted to Usenix Security 2023.
• [2022-09] Two papers were accepted to NeurIPS 2022.
• [2022-01] I am a member of Usenix Security 2022 Artifact Evaluation Committee.
• [2021-09] One paper was accepted to Usenix Security 2021.

Publication

• Madeline: Continuous and Low-cost Monitoring with Graph-free Representations to Combat Cyber Threats
  Wenjia Song, Hailun Ding, Na Meng, Peng Gao, Danfeng (Daphne) Yao (ACSAC 2024)

• Merlin: Multi-tier Optimization of eBPF Code for Performance and Compactness
  Jinsong Mao, Hailun Ding, Juan Zhai, Shiqing Ma
  Architectural Support for Programming Languages and Operating Systems 2024 (ASPLOS 2024)

• AirTag: Towards Automated Attack Investigation by Unsupervised Learning with Log Texts
  Hailun Ding, Juan Zhai, Yuhong Nan, Shiqing Ma
  USENIX Security Symposium 2023 (Usenix security 2023)

• The Case for Learned Provenance Graph Storage Systems
  Hailun Ding, Juan Zhai, Dong Deng, Shiqing Ma
  USENIX Security Symposium 2023 (Usenix security 2023)

• Rethinking the Reverse-engineering of Trojan Triggers
  Zhenting Wang, Kai Mei, Hailun Ding, Juan Zhai, Shiqing Ma
  Proceedings of Neural Information Processing Systems 2022 (NeurIPS 2022)

• Training with More Confidence: Mitigating Injected and Natural Backdoors During Training
  Zhenting Wang, Hailun Ding, Juan Zhai, Shiqing Ma
  Proceedings of Neural Information Processing Systems 2022 (NeurIPS 2022)

• ELISE: A Storage Efficient Logging System Powered by Redundancy Reduction and Representation Learning
  Hailun Ding, Shenao Yan, Juan Zhai, Shiqing Ma
  USENIX Security Symposium 2021 (Usenix security 2021)

• Procedural Learning With Robust Visual Features via Low Rank Prior
  Haifeng Li, Li Chen, Hailun Ding, Qi Li, Bingyu Sun, Guohua Wu
  IEEE Access 2019

• A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome
  Li Chen, Hailun Ding, Qi Li, Jiawei Zhu, Jian Peng, Haifeng Li
  Arxiv 2019

• Understanding the Importance of Single Directions via Representative Substitution
  Li Chen, Hailun Ding, Qi Li, Zhuo Li, Jian Peng, Haifeng Li
  AAAI 2019 Workshop on Network Interpretability for Deep Learning**

Service

• Program Committee, AAAI 2025, 2026
• Reviewer, TIFS
• Reviewer, CODASPY 2024
• Reviewer, ICLR 2024, 2025
• Reviewer, NeurIPS Workshop on Backdoors in Deep Learning: The Good, the Bad, and the Ugly (BUGS) 2023
• Reviewer, NeurIPS 2023, 2025
• Reviewer, International Conference on Machine Learning (ICML), 2022, 2024
• Artifact Evaluation Committee, USENIX Security Symposium, 2022 (Fall and Winter cycles)

Award

• National Scholarship (2017, 2019, 0.2% of Chinese undergraduate students)
• First Level Scholarship of Central South University (2017, 2018, 2019, Top 3%)
• Diamond Scholarship (2017, 15 person in 30,000 undergraduate students)
• First Prize in The Hack, Hack Shanghai (2018)