Security researchers leverage AI models to discover dozens of critical bugs in Bitcoin software

A team of independent security researchers recently demonstrated how artificial intelligence can dramatically speed up software audits by scanning hundreds of crypto projects in a single day. Led by developer Calle and industry executive Rob Hamilton, the group used several AI models to review nearly 400 open-source codebases in under 28 hours. As reported by Bitcoin Magazine and cited by CoinAlertNews, the automated evaluation uncovered 85 critical software flaws alongside more than 600 other high-severity vulnerabilities.

For everyday crypto holders, the findings highlight both the fragility of financial software and the immense defensive capability of automated tools. Artificial intelligence can now process complex code at a pace human engineers cannot match, exposing deeply buried glitches before attackers capitalize on them. The immediate impact was felt when the Boltz platform temporarily paused services to fix its flagged weaknesses, demonstrating how quickly automated reports can force real-world operational changes.

What remains unclear is how effectively open-source maintainers can patch thousands of flagged issues without accidentally revealing exploitable details to cybercriminals. Describing the rapid pace of discovered bugs, Calle noted that the situation is "extremely bad." This effort prompts a vital question for the broader tech ecosystem: if volunteer auditors can use commercial AI to locate dozens of severe exploits in hours, are malicious actors already using identical methods to map out targets secretly?