The XRP Ledger has been running without interruption since 2012. Over that stretch, it has processed more than 100 million ledgers, completed over 3 billion transactions, and moved billions of dollars in value. That is a long operational track record by any standard.
But longevity brings structural weight. The XRPL codebase carries over a decade of engineering decisions, early-phase assumptions, and design patterns that predate modern tooling. None of that is unusual for long-running production systems. It does mean, though, that security cannot be treated as a solved problem.
Red Team Already Surfacing Real Bugs
Ripple has now established a dedicated, AI-assisted red team focused on continuously analyzing the XRPL codebase. The team does not test features in isolation. It examines how those features interact in real-world conditions, particularly at the boundaries where legacy logic meets newer functionality.
That boundary work has already produced results. The red team has uncovered more than 10 bugs so far. Only low-severity issues have been publicly disclosed, and all identified vulnerabilities are being prioritized for fixes.
Reece Merrick noted on X, writing that the @RippleXDev team is
“taking AI to the front line of XRPL security,” using AI to “scan code, simulate attacks, and hunt bugs before they happen.”
Alongside the red team, Ripple is running fuzzing and automated adversarial testing guided by explicit threat models. The goal is to simulate attacker behavior at scale, pulling vulnerabilities to the surface earlier than conventional testing methods allow.
Rewiring the Development Lifecycle
AI is being woven into the XRPL development process at multiple points. That includes adversarial code scanning on a regular basis, AI-assisted reviews on every pull request, and threat modeling for both new features and existing feature interactions.
The team is also using AI to generate edge cases and stress scenarios that would be difficult to construct manually. It moves the detection window earlier, before issues reach production rather than after.
Ayo Akinyele, writing in a Ripple insights post published March 26, framed it directly: the shift moves security from reactive debugging to proactive, systematic vulnerability discovery. The bar for reliability on a global financial network, the post noted, is uncompromising.
Beyond testing, Ripple is also modernizing the codebase itself. Many bug classes in long-lived systems like xrpld come from structural problems, not just individual coding errors. Limited type safety, inconsistent feature interaction patterns, and unenforced assumptions all contribute. Addressing those structural gaps is part of the current effort.
Amendments Face a Higher Approval Bar
Changes to the XRPL through amendments will now go through a stricter evaluation process before activation. Significant amendments will require multiple independent security audits. The bug bounty program is being expanded to pull in deeper external testing. Ripple is also running attackathons where new features get tested in adversarial environments before going live.
Security readiness criteria will be defined clearly, giving amendments explicit thresholds for testing, review, and risk assessment. Those standards will be published in collaboration with the XRPL Foundation, setting a transparent benchmark for what clears the bar.
The broader security effort also pulls in partners across the ecosystem: XRPL Commons, the XRPL Foundation, independent researchers, validator operators, and external security firms. No single team owns the responsibility.
The next XRPL release, according to Ripple, will be dedicated entirely to bug fixes and improvements, with no new features included.
