TitanCA: Lessons from Orchestrating LLM Agents to Discover 100+ CVEs

April 20, 2026 · Grace Period · + Add venue

Authors Ting Zhang, Yikun Li, Chengran Yang, Ratnadira Widyasari, Yue Liu, Ngoc Tan Bui, Phuc Thanh Nguyen, Yan Naing Tun, Ivana Clairine Irsan, Huu Hung Nguyen, Huihui Huang, Jinfeng Jiang, Lwin Khin Shar, Eng Lieh Ouh, David Lo, Hong Jin Kang, Yide Yin, Wen Bin Leow arXiv ID 2604.17860 Category cs.CR: Cryptography & Security Citations 0

Abstract

Software vulnerabilities remain one of the most persistent threats to modern digital infrastructure. While static application security testing (SAST) tools have long served as the first line of defense, they suffer from high false-positive rates. This article presents TitanCA, a collaborative project between Singapore Management University and GovTech Singapore that orchestrates multiple large language model (LLM)-powered agents into a unified vulnerability discovery pipeline. Applied in open-source software, TitanCA has discovered 203 confirmed zero-day vulnerabilities and yielded 118 CVEs. We describe the four-module architecture, i.e., matching, filtering, inspection, and adaptation, and share key lessons from building and deploying an LLM-based vulnerability discovery solution in practice.