Agentic Security
Building and evaluating autonomous agents for realistic, long-horizon security workflows.
Researcher · Urbana, Illinois
PhD Student in Computer Science at the University of Illinois Urbana-Champaign · advised by Lingming Zhang.
I build and evaluate AI agents for cybersecurity.

My work connects the capabilities of AI agents with the evidence, tooling, and evaluation discipline required by real-world security practice.
Building and evaluating autonomous agents for realistic, long-horizon security workflows.
Connecting models, program analysis, and dynamic execution evidence in reproducible systems.
Discovering and validating vulnerabilities in production software, from services to operating-system kernels.
SEC-bench Pro, our benchmark for long-horizon software security tasks, is now available on arXiv.
Agentic Vulnerability Reasoning on COTS Binaries is now available on arXiv.
Recognized as a Microsoft Most Valuable Security Researcher for 2026 Q1.