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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
3 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 (Former, graduated)

Jianjia Yu completed her PhD at Johns Hopkins University. Her research focuses on security and privacy in web and mobile ecosystems using program analysis and LLM techniques. Her work has received Distinguished Paper Awards at CCS '23 and S&P '25, and an 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.

Yichao Xu

Fourth-year PhD candidate in Computer Science at Johns Hopkins University, advised by Prof. Yinzhi Cao. Research focuses on program analysis for web application security, emphasizing precise detection of taint-style and business-logic vulnerabilities in real-world code. Co-author of CoBrA (ICSE 2026) and FAST (IEEE S&P 2023), which together uncovered nearly 300 zero-day vulnerabilities with 30 CVEs assigned, and a contributor to Joern’s PHP frontend and security queries.

Senapati Diwangkara

Final-year CS PhD at JHU, advised by Prof. Yinzhi Cao. His research focuses on novel agentic and program analysis methods for the security and reliability of web infrastructure. His work has been featured at top systems and security conferences like OSDI ’25 and NDSS ’26, uncovering significant zero-day vulnerabilities.

Letao Zhao

Second-year master’s student in Security Informatics at Johns Hopkins University. Research focuses on software security, web security, and binary exploitation. Winner of PwC Hackaday (2023, 2024), HKCERT CTF (2023), and HKUST Firebird CTF (2024). Certified by OffSec as an Offensive Security Web Expert (OSWE) and Offensive Security Certified Professional (OSCP).

Ruijie Tao

Second-year master’s student in Computer Science at Johns Hopkins University, with a background in Applied Mathematics from UC Santa Barbara. Focuses on NLP, LLMs, and AI agents, with particular interests in agent harnesses and LLM applications.

Tianao Song

Second-year M.S. student in Computer Science at Johns Hopkins University. Research focuses on AI agent safety and alignment, with particular interests in agent guardrails, trustworthy agentic systems, and evaluation methods for improving the safety and reliability of AI agents.

Zhifei Yu

Second-year master’s student in Computer Science at Johns Hopkins University. Research focuses on trustworthy AI and AI agents, including AI security, deepfake detection, agentic RAG, agentic RL, and evaluation of large language model agents.

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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