| I am a PhD candidate in computer science at Purdue University, advised by Prof. Ninghui Li. I study security and privacy risks in machine learning and LLM agents, from both adversarial and defensive perspectives. My current research focuses on three directions: 🚨 AI Security & Safety Uncovering threats from LLM misuse 🔍 Machine Learning Privacy Assessing information leakage in ML models 🛡️ Data Privacy Identifying and protecting data privacy My research has been recognized and supported by the Ross Fellowship (2023–2027), Presidential Doctoral Excellence Award (2023–2027), and Herbold Scholarship (2023–2024). | Selected Publications - Automated Profile Inference with Language Model Agents
Yuntao Du, Zitao Li, Bolin Ding, Yaliang Li, Hanshen Xiao, Jingren Zhou, Ninghui Li
ACL 2026 (Findings) [pdf] [code]
AI Security & Safety First study showing that LLM agents enable automated doxing at web scale.
- Cascading and Proxy Membership Inference Attacks
Yuntao Du, Jiacheng Li, Yuetian Chen, Kaiyuan Zhang, Zhizhen Yuan, Hanshen Xiao, Bruno Ribeiro, Ninghui Li
NDSS 2026 [pdf] [code] [blog]
Machine Learning Privacy Formulates and categorizes MIAs and first exploits membership dependencies. - Imitative Membership Inference Attack
Yuntao Du, Yuetian Chen, Hanshen Xiao, Bruno Ribeiro, Ninghui Li
USENIX Security 2026 [pdf] [code] [blog]
Machine Learning Privacy A new shadow training paradigm for MIAs with significantly reduced computation. - Privacy Leakage from a Thousand Words: Millipixel Location Recovery from Dot Maps
Yuntao Du*, Tanishq Pauskar*†, Hao Wang, Jing Su, Ninghui Li
ACM CCS 2026 [pdf] [code]
Data Privacy First to achieve 1-meter attack accuracy in location recovery from national-scale dot maps.
Our findings have been adopted by map visualization platforms (e.g., QGIS) to warn users of these risks, and we are working with the U.S. CDC to revise its cartographic guidelines. - Systematic Assessment of Tabular Data Synthesis
Yuntao Du, Ninghui Li
ACM CCS 2025 [pdf] [code] [blog]
Data Privacy Proposes a unified evaluation framework for tabular data synthesis algorithms. See the full publication list → | Selected Awards & Honors - National Winner, Innovation Bowl (Purdue team lead), 2026 Coverage: [Purdue CS][Radiance][Machine Brief]
- NDSS Fellowship, Internet Society (1 of 24 worldwide), 2026
- Ross Fellowship, Purdue University, 2023
- Herbold Scholarship, Purdue University (1 of 7), 2023
- Presidential Doctoral Excellence Award, Purdue University (1 of 150), 2023
- Excellent Master's Dissertation, China (1 of 43), 2023
- Provincial Outstanding Graduate, Zhejiang, China, 2023
- National Scholarship, China (0.1%), 2021-2022
| Service - Program Committee: USENIX Security (2027), VLDB (2027), AsiaCCS (2027), NeurIPS (2026), ICLR (2025-2027), AISTATS (2025-2026), WWW (2026), CODASPY (2026), WSDM (2026), CIKM (2024-2026), SIGIR (2023-2026), IJCNN (2026-2027), TRL (2024-2025), AAAI (2023-2027), SIGIR-AP (2023-2024)
- Poster Program Committee: IEEE S&P (2026)
- Journal Reviewers: CSUR, TDSC, TOPS, VLDBJ, TKDE, TORS, TBD
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