TitanCA: Lessons from Orchestrating LLM Agents to Discover 100+ CVEs
Orchestrated LLM agents discovered 100+ real CVEs, outperforming conventional static analysis — signalling that AI-augmented vuln discovery is moving from theory to practical tooling for security teams.
Summary written by editorial AI · Source link below
arXiv:2604.17860v3 Announce Type: replace 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
Editorial Analysis
AI-driven vulnerability discovery at this scale could reshape how enterprises manage code-level risk, compressing the window between vulnerability introduction and detection.
Assess readiness to integrate LLM-agent-based vulnerability scanning into your SDLC, starting with contained pilot projects to validate efficacy against your technology stack.
AI agents are now discovering software vulnerabilities at scale — a capability that will change both offensive and defensive economics in software security.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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