Jingxuan He

The paper introduces a contextual security framework for LLM agents, defining security properties and reformulating various attacks and defenses based on the context of execution.

The paper introduces SecPI, a fine-tuning pipeline that teaches reasoning language models (RLMs) to autonomously internalize structured security reasoning, significantly improving secure code generation without requiring explicit security prompts at inference.

The paper introduces ExploitGym, a large-scale benchmark, demonstrating that advanced AI agents can successfully turn theoretical software vulnerabilities into working exploits, highlighting growing cybersecurity risks.

The paper introduces CyberGym-E2E, a large-scale, end-to-end benchmark designed to comprehensively evaluate AI agents' capabilities across the entire lifecycle of real-world software vulnerability discovery, proof-of-concept generation, and patch creation.