XRP Ledger Fixes Major Vulnerability Just in Time

A critical security flaw in the XRP Ledger's proposed Batch amendment could have allowed attackers to execute unauthorized transactions from victim accounts, all without needing private keys. The bug was caught on February 19 before the amendment went live on mainnet, meaning no funds were compromised. Cantina CEO Hari Mulackal called it a potential contender for "the largest security hack by dollar value" in blockchain history, with XRP's market cap sitting around $80 billion.

What Exactly Was the Bug?

The vulnerability lived inside the signature-validation process for batch transactions in Rippled version 3.1.0. A logic error in the validation loop created an early-exit condition that attackers could exploit.

Here's how it worked in practice:

• An attacker could craft a batch transaction containing inner operations (payments, account modifications) tied to a victim's account
• The transaction would use a forged signer entry
• The flawed validation would check a non-existent account, hit the early exit, and skip proper verification
• The unauthorized operations would then go through as if they were legitimate

If exploited after activation, attackers could have drained accounts down to their reserve balance, altered ledger states through transactions like AccountSet or TrustSet, and potentially even deleted accounts entirely.

How Was It Caught?

Security engineer Pranamya Keshkamat from Cantina, a blockchain-focused security firm, found the flaw alongside the firm's AI-powered auditing tool called Apex. The discovery came during a static analysis of the Rippled codebase.

Apex, described as an autonomous AI security auditor, flagged the logic error. Keshkamat and the team then submitted a responsible disclosure report. Ripple's engineering teams validated it quickly using a proof-of-concept and unit tests.

It's a concrete case of AI-assisted security delivering results, not just promising them.

How Fast Did the Response Come?

From discovery to public disclosure, the entire process took one week.

On February 19, the same day Cantina reported the flaw, UNL validators were advised to vote "No" on the Batch amendment. Several applied vetoes immediately.

By February 23, Ripple released an emergency patch with Rippled version 3.1.1. This update marks the Batch amendment as unsupported and introduces a temporary fix called fixBatchInnerSigs to block activation.

On February 26, XRPL Labs published the full vulnerability disclosure report publicly.

What Happens Next?

The Batch amendment isn't dead. The XRPL development team is working on a replacement called BatchV1_1. The updated version removes the early-exit conditions, adds authorization guards, and tightens signing checks across the board. There's no set release date yet, and the code is still under review.

Validators should upgrade to Rippled 3.1.1 immediately. Regular users don't need to take any action since the flaw never went live, but keeping an eye on official XRPL channels for BatchV1_1 updates is a good idea.

Why Does This Matter?

This is one of those near-misses that should make the entire industry pay attention. The bug existed in code that was actively being voted on for mainnet activation. If the timing had been different by even a few days, the outcome could have looked very different.

The fact that an AI tool played a central role in catching it is significant. Third-party audits have always been important in crypto, but AI-assisted audits are proving they can catch things that human reviewers might miss during routine code review.

XRPL Labs acknowledged that the incident will drive ongoing improvements to their code review processes. For an ecosystem handling tens of billions in value, that's not optional. It's survival.

Sources:

• Cointelegraph Reporting on the discovery by Cantina's security engineer and AI tool Apex, including quotes from CEO Hari Mulackal
• XRPL Labs Blog Official vulnerability disclosure report with technical details, remediation timeline, and validator recommendations