The XRPL ecosystem is counting down as a key amendment is set to activate in the next few days.
Based on data from XRPScan, the XRPL 3.1.3 update, known as “fixCleanup3_1_3,” is now active and expected to be fully implemented around May 27, 2026. This follows a standard two-week activation period.
What makes this amendment unusual is that it received unanimous support – 100% of validators agreed – according to XRPScan, a popular tool for tracking the XRP Ledger. Most amendments only need to reach 80% approval to pass, so full consensus is very rare.
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The XRP Ledger updates itself through a voting process. Any changes to how transactions are handled are proposed as ‘amendments,’ and network validators then vote to approve or reject them. This ensures changes are made with broad agreement.
If an amendment gains over 80% support for a two-week period, it’s approved and becomes a permanent part of the ledger’s history. To undo an approved amendment, a new amendment must be created.
The “fixCleanup3_1_3” update was unanimously approved because it automatically passed without a vote. This update includes improvements for NFTs, restricted web addresses (permissioned domains), Vaults, and the Lending Protocol.
This update comes as Ripple improves the security of the XRP Ledger using artificial intelligence. An AI-powered security team has been successful in finding bugs by examining how the XRPL code works in practice, focusing on real-world interactions rather than testing features separately.
Therefore, the update to XRPL version 3.1.3 focused solely on fixing bugs and making improvements – it didn’t introduce any new features that needed a vote.
Ripple expands XRPL security efforts
Ripple announced in March that it’s improving the security of the XRP Ledger, and artificial intelligence will be a key part of this process.
In addition to ongoing testing, Ripple is working to improve the underlying code of the XRP Ledger (XRPL). Bugs in long-running systems like xrpld often aren’t just simple errors; they come from problems with how the code is structured. These include a lack of robust data checking, features that don’t work well together, missing safeguards to ensure data consistency, and hidden or unverified assumptions within the code.
Therefore, fixing these problems is still very important because it makes the system more reliable, understandable, and able to withstand challenges.
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2026-05-24 03:29