Ripple Introduces AI to Strengthen XRPL Security: Over 10 Vulnerabilities Discovered, Next Version Focused on Security Fixes

On March 28, Ripple Labs announced the introduction of an AI-driven security framework for the XRP Ledger, utilizing machine learning tools throughout the processes of code review, adversarial testing, and vulnerability discovery to address security challenges arising from the growth of institutional applications. It was disclosed that Ripple has formed an AI-assisted ‘Red Team’ that simulates attack behaviors through fuzzing and automated adversarial testing, currently identifying over 10 vulnerabilities that are being prioritized for repair. The company stated that this initiative will shift security mechanisms from ‘passive repair’ to ‘active discovery.’ On the development front, Ripple plans to modernize the XRPL code structure while enhancing protocol change standards, requiring critical updates to undergo multiple independent security audits and expanding the scope of bug bounties and community collaboration. Notably, the next version of XRPL will not introduce new features but will focus entirely on vulnerability fixes and system hardening, highlighting a significant increase in security priorities. This move comes as Ripple accelerates its expansion into institutional business, including stablecoins and real-world asset (RWA) application scenarios, raising the demands for the underlying ledger’s security.