arXiv (Computer Science - Cryptography and Security) • 1h
VulnGym: Reinforcement Learning-Driven Adversary Simulation for Patching Optimization
Tencent Research has introduced VulnGym, a framework designed to evolve vulnerability management from static CVSS-based prioritization to adversary-aware defense. By utilizing Reinforcement Learning (RL) trained on real-world APT behavioral profiles, VulnGym simulates sophisticated lateral movement within a dynamic network graph. The system integrates a CVE layer and a configurable patching policy engine, allowing security teams to stress-test defensive postures against evolving threat actors. This methodology enables organizations to identify and remediate specific attack paths rather than exhaustively patching high-score CVEs, effectively reducing the time-to-compromise and optimizing resource allocation in complex network topologies.