Business · Cryptocurrency · published 2026-10-13 · via Bitcoin Haber

AI Tools Uncover Security Bugs in XRP Ledger and Zcash

AI-driven audits identified serious vulnerabilities in the XRP Ledger and Zcash networks, with Coin Bureau saying the discoveries helped protect more than $190 billion in assets. Veria Labs found an integer overflow in XRP Ledger's payment engine that had gone unnoticed since 2015 and could have minted 18.45 trillion XRP; it received a $250,000 bug bounty. Separately, Cantina AI's Apex tool detected a signature-verification flaw in a proposed XRP Ledger Batch amendment that put roughly $80 billion at risk, prompting validators to reject the change and deploy an emergency patch.

Expanded Detail

Veria Labs’ AI review traced an integer overflow in the XRP Ledger payment engine back to 2015. The bug could allegedly mint 18.45 trillion XRP per transaction—184 times the token’s supply—while XRP’s market value stood near $94 billion. The foundation said no unauthorized tokens appeared, and the team earned a $250,000 maximum bounty.

Cantina AI’s Apex scanner later flagged a signature-check logic error in a proposed XRP Ledger Batch amendment. It might have let attackers submit bulk transfers from victims’ accounts without private keys, placing about $80 billion at risk. Validators rejected the amendment and deployed an emergency patch.

Count first para: Veria(1) Labs’(2) AI(3) review(4) traced(5) an(6) integer(7) overflow(8) in(9) the(10) XRP(11) Ledger(12) payment(13) engine(14) back(15) to(16) 2015(17). The(18) bug(19) could(20) allegedly(21) mint(22) 18.45(23) trillion(24) XRP(25) per(26) transaction—184(27?) times(28) the(29) token’s(30) supply—while(31) XRP’s(32) market(33) value(34) stood(35) near(36) $94(37) billion(38). The(39) foundation(40) said(41) no(42) unauthorized(43) tokens(44) appeared(45), and(46) the(47) team(48) earned(49) a(50) $250,000(51) maximum(52) bounty(53). Second: Cantina(1) AI’s(2) Apex(3) scanner(4) later(5) flagged(6) a(7) signature-check(8) logic(9) error(10) in(11) a(12) proposed(13) XRP(14) Ledger(15) Batch(16) amendment(17). It(18) might(19) have(20) let(21) attackers(22) submit(23) bulk(24) transfers(25) from(26) victims’(27) accounts

Context

under 90 words. Measured analysis possible impact society, who affected and how, framed could/may, no political advocacy. Need mention crypto holders, exchanges, validators, developers, AI auditing. Could say: These discoveries may strengthen confidence among XRP and Zcash holders, exchanges, and validators by showing AI-assisted audits can catch flaws before exploitation. If similar tools become standard, developers could face faster disclosure and patching cycles, while users might benefit from reduced risk of counterfeit tokens or unauthorized transfers. Yet reliance on AI may also raise questions about audit transparency and accountability, especially when findings affect billions in assets. Count? Let's count. These1 discoveries2 may3 strengthen4 confidence5 among6 XRP7 and8 Zcash9 holders10, exchanges11, and12 validators13 by14 showing15 AI-assisted16 audits17 can18 catch19 flaws20 before21 exploitation22. If23 similar24 tools25 become26 standard27, developers28 could29 face30 fa

Expanded detail and Context are AI-generated analysis; the linked article remains the authoritative source.

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