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Team Jay'l Break

Team Jay'l Break

Our goal is to advance functional correctness, reliability, and security in AI-driven software development and testing through two complementary efforts: (i) building coding agents that generate high-quality and reliable software via advanced planning and adaptive guidance, and (ii) developing next-generation red-teaming systems that effectively detect and exploit vulnerabilities in generated software by combining Large Language Model reasoning, security domain knowledge, and program analysis.

By Johns Hopkins University
1 min read
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Team members

Zhengyu Liu - Team leader

Third-year PhD student in Computer Science at Johns Hopkins. Research focuses on web and software security via program analysis. Work received Distinguished Paper (S&P '25), Honorable Mention (USENIX '25), and Best Student Paper (ICICS '22). DEFCON speaker and member of CTF team TheHackersCrew.

Jianjia Yu

PhD candidate at Johns Hopkins. Research focuses on security and privacy in web and mobile ecosystems using program analysis and LLM techniques. Work received Distinguished Paper Awards at CCS '23 and S&P '25, and Honorable Mention at USENIX Security '25.

Rui Yang

First-year PhD student in Computer Science at Johns Hopkins, advised by Prof. Yinzhi Cao. Research focuses on AI agent security, web application security, and vulnerability analysis. Discovered and responsibly disclosed over 20 real-world CVEs.

Xiaogeng Liu

Third-year PhD student in Electrical and Computer Engineering at Johns Hopkins, advised by Prof. Chaowei Xiao. Research focuses on robustness of machine learning systems, emphasizing models' ability to maintain performance and resist attacks. Awarded NVIDIA Graduate Fellowship and Distinguished Paper Award at USENIX Security '24.

Zhengyue Zhao

PhD student at Johns Hopkins advised by Prof. Chaowei Xiao. Research focuses on trustworthy large language models, including safety alignment through structured reasoning, inference-time safety enhancement, and privacy-preserving techniques such as watermark-based model protection.

Faculty advisors

Ziyang Li

Assistant Professor of Computer Science at Johns Hopkins, affiliated with Data Science and AI Institute and Institute of Assured Autonomy. Research spans programming languages, machine learning, and security. Work recognized as multiple spotlight papers (NeurIPS 2025, ICML 2024, ICLR 2020). Received AWS AI Fellowship (2023) for contributions to trustworthy AI.

Yinzhi Cao

Associate Professor in Computer Science and Technical Director of Information Security Institute at Johns Hopkins. Research focuses on security and privacy of the web, smartphones, and machine learning systems using program analysis techniques.

Chaowei Xiao

Assistant Professor in Electrical and Computer Engineering at Johns Hopkins. Worked on AI security for over 10 years. Received Schmidt Science AI2050 Early Career Award, Argonne National Lab Impact Award, and multiple industry faculty awards.

About the Author
Johns Hopkins University

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