u.today
The XRPL ecosystem is counting down as a key amendment is set to activate in the next few days.
The fix amendment in XRPL 3.1.3, "fixCleanup3_1_3," has entered a 2-week activation period with an expected timeline of May 27, 2026, according to XRPScan data.
The peculiarity of this amendment is that it achieved 100% consensus as indicated by popular XRPL explorer XRPScan, a rare occurrence for most amendments, with most merely surpassing the required 80% mark.
The $XRP Ledger amendment system uses the consensus process to approve any changes that affect transaction processing on the $XRP Ledger. Fully functional transaction process changes are introduced as amendments; validators then vote on these changes.
If an amendment receives more than 80% support for two weeks, the amendment passes, and the change applies permanently to all subsequent ledger versions. Disabling a passed amendment requires a new amendment to do so.
The "fixCleanup3_1_3" was able to achieve 100% consensus as it required no manual voting, being a default yes fix amendment. It is a collection of fixes for NFTs, Permissioned Domains, Vaults, and the Lending Protocol.
The amendment follows as Ripple overhauls $XRP Ledger security through AI. Progress has been made in bug discovery by an AI-assisted red team, which was established with a focus on continuously analyzing the XRPL codebase and how features interact in real-world scenarios, not just in isolation.
As a result, the XRPL 3.1.3 version had only bug fixes and improvements and contained no new features that required voting.
Ripple expands XRPL security efforts
In March, Ripple stated it was overhauling how it secures the $XRP Ledger with AI at the center of the effort.
Alongside active testing, Ripple stated it was investing in modernizing and better aligning the XRPL codebase itself. Many classes of bugs in long-lived systems like xrpld stem not only from individual mistakes, but from structural issues such as limited type safety, inconsistent interaction patterns between features, insufficient invariant enforcement, and undocumented or unenforced assumptions.
Hence, addressing these issues remains critical as it makes the system more predictable, easier to reason about and resilient.