Security: A Next Frontier in AI Coding by Jingxuan He

Abstract

AI is reshaping software development, yet this rapid adoption risks introducing a new generation of security debt. In this talk, I will present my research program aimed at transforming AI from a source of vulnerabilities to a proactive tool for building secure software. I will begin by introducing a systematic framework for quantifying AI‑induced cybersecurity risks through two benchmarks: CyberGym, which evaluates AI agents’ offensive capabilities in vulnerability reproduction and discovery, and BaxBench, which measures LLMs’ propensity to introduce security flaws when generating code. Building on these findings, I will present a secure‑by‑design approach for AI‑generated code. This includes security‑centric fine‑tuning that embeds secure coding practices directly into models, as well as a decoding‑time constraining mechanism based on type systems to enforce safety guarantees. Finally, I will conclude by discussing my future research on building broader security and trust in AI‑driven software ecosystems.

About the Speaker

Jingxuan He is a Postdoctoral Researcher at the University of California, Berkeley, working with Prof. Dawn Song. He earned his PhD at ETH Zurich, where he was advised by Prof. Martin Vechev. His research lies at the intersection of security and AI, with a focus on quantifying AI’s impact on cybersecurity and developing proactive defenses to mitigate associated risks. His work has been recognized with an ACM CCS Distinguished Paper Award and an ETH Medal for Outstanding Doctoral Thesis, and is adopted by leading AI labs like Anthropic.