The XRPL ecosystem is on a countdown as important fixes are expected to be enabled in the next few days.
According to XRPScan data, fix fixCleanup3_1_3 for XRPL 3.1.3 is entering a two-week activation period with an expected schedule of May 27, 2026.
A feature of this modification is that it achieved 100% consensus, as demonstrated by the popular XRPL explorer XRPScan. This is rare for most fixes, and most fixes are only slightly above the required 80% mark.
of $XRP Ledger adjustment systems use a consensus process to approve changes that affect the processing of transactions on the books. $XRP ledger. A fully functional transaction process change is introduced as a fix. Verifiers then vote on these changes.
If the amendment receives 80% or more support over a two-week period, it will be passed and the changes will be permanently applied to all subsequent ledger versions. A new amendment would be required to override the passed amendment.
“fixCleanup3_1_3” was able to achieve 100% consensus as it does not require manual voting and is a default “yes” fix fix. This is a collection of fixes for NFTs, allowed domains, vaults, and lending protocols.
Corrections due to review of Ripple $XRP Ledger security powered by AI. AI-assisted red teaming continues to find bugs. The Red Team was established with a focus on continuous analysis of the XRPL codebase and how features interact not just in isolation but in real-world scenarios.
As a result, the XRPL 3.1.3 version contained only bug fixes and improvements, and no new features that required voting.
Ripple expands its commitment to XRPL security
Ripple announced in March that it was overhauling the way it secures its services. $XRP AI-centered ledger.
Alongside aggressive testing, Ripple said it is investing in modernizing and better tuning the XRPL codebase itself. Many types of bugs in long-lived systems like xrpld arise not only from individual mistakes but also from structural problems such as type safety limitations, inconsistent interaction patterns between features, poorly enforced invariants, and undocumented or unenforced assumptions.
Therefore, addressing these issues remains important as it makes systems more predictable, easier to reason about, and more resilient.
