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.
@article{arxiv.2604.17860,
title = {TitanCA: Lessons from Orchestrating LLM Agents to Discover 100+ CVEs},
author = {Ting Zhang and Yikun Li and Chengran Yang and Ratnadira Widyasari and Yue Liu and Ngoc Tan Bui and Phuc Thanh Nguyen and Yan Naing Tun and Ivana Clairine Irsan and Huu Hung Nguyen and Huihui Huang and Jinfeng Jiang and Lwin Khin Shar and Eng Lieh Ouh and David Lo and Hong Jin Kang and Yide Yin and Wen Bin Leow},
journal= {arXiv preprint arXiv:2604.17860},
year = {2026}
}