On August 9, a volunteer security team said it used frontier AI models to scan codebases of roughly 150 Bitcoin core-related projects, uncovering more than a dozen vulnerabilities spanning wallets, cryptography libraries, and infrastructure. Team members reported finding about one severe vulnerability per person per hour, and had submitted security reports to multiple projects over the past 12 hours without naming them. Recent incidents involving Coldcard and Boltz also suggest AI is being used by both researchers and attackers to spot software flaws faster.
BlockBeats reported on August 9 that a volunteer security team recently deployed frontier AI models to scan codebases linked to roughly 150 Bitcoin core-related projects, uncovering more than a dozen vulnerabilities across wallets, cryptography libraries, and infrastructure.
The team said it used Kimi K3, OpenAI's GPT Sol, Anthropic's Claude Fable and Opus models, and Z.ai's GLM 5.2 to identify vulnerabilities and generate supporting documentation. According to team members, each person is currently finding about one severe vulnerability per hour. Over the past 12 hours, they have submitted security reports to multiple projects, though they have not publicly disclosed which projects were affected.
Recent security incidents involving Coldcard and Boltz also show that AI is being used by both security researchers and attackers to discover software vulnerabilities more quickly.
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