Key Highlights
- A comprehensive security audit examined 501 Bitcoin-related open-source projects, identifying 7,958 potential security concerns over 108 hours
- Among these discoveries, 1,280 were rated as critical or high-severity threats
- The audit primarily utilized Kimi K3, an AI model developed by China’s Moonshot AI
- BTCPay Server has already addressed a critical security flaw discovered by the team, which included a two-factor authentication vulnerability
- At the time of initial disclosure, just 24.7% of the identified issues included reproducible evidence, highlighting the continued necessity for human validation
The Bitcoin Red Team has successfully executed an extensive AI-driven security assessment covering nearly the complete Bitcoin open-source landscape, documenting 7,958 possible security vulnerabilities across 501 different projects throughout 108 hours of intensive analysis.
red team
rugged by openai cyber again.
don’t like asking for permission. loading up kimi k3. pic.twitter.com/wCEV2HYpLo
— calle
This security collective, which merges artificial intelligence capabilities with expert human analysis, employed Kimi K3—an AI model created by China’s Moonshot AI—as their principal investigative instrument. The ability to operate Kimi K3 locally proved advantageous, as it circumvented limitations researchers reported experiencing with U.S.-based AI platforms such as OpenAI and Anthropic when conducting security investigations.
According to Calle, the team’s pseudonymous primary developer, the group has successfully concluded an initial examination of virtually the complete Bitcoin open-source environment, with the most easily detectable security weaknesses already identified and documented.
“We’re experiencing a massive collision between decades of human open source slop against two weeks of Kimi K3,” Calle wrote on X. “Everything is broken, Bitcoin is burning.”
Many Findings Require Further Verification
It’s crucial to understand that the figure of 7,958 doesn’t represent 7,958 verified, exploitable security holes. Among these discoveries, 1,280 received classifications of critical or high severity. At the 108-hour milestone, only 24.7% had undergone dynamic reproduction testing, while 29.4% had been forwarded to the respective project maintainers.
Human oversight remains an essential component of this security assessment process. AI-driven audits can generate false alarms and duplicate entries, with severity classifications often requiring adjustment following manual examination.
An initial assessment wave uncovered 4,962 potential vulnerabilities spanning 390 Bitcoin projects, with 720 initially designated as critical or high severity. The current statistics demonstrate significant expansion beyond that preliminary examination.
BTCPay Server Addresses Critical Security Flaw
This security initiative has already yielded tangible security improvements. BTCPay Server publicly acknowledged Red Team researchers Bruno Garcia and Ben Carman for identifying a critical vulnerability that malicious actors were actively exploiting. The project’s version 2.4.2 release remediated a two-factor authentication bypass affecting Greenfield Basic Authentication.
BTCPay subsequently disclosed that threat actors had successfully extracted administrative credentials from compromised installations, subsequently utilizing these credentials to access connected Lightning wallets. The development team indicated they were evaluating additional vulnerability reports submitted by the Red Team and other security researchers.
On August 14, BTCPay introduced another security-centric release candidate that addressed additional security weaknesses. The BTCPay community also supported a recovery bounty initiative and contributed 0.21 BTC to the Bitcoin Red Team’s operational fund.
Heightened Expectations for Project Maintenance
Calle contended that artificial intelligence has significantly reduced the resources required to identify security vulnerabilities, suggesting that projects lacking active maintenance should now be viewed with increased skepticism. He emphasized that how quickly projects respond to security disclosures serves as a valuable metric for assessing project vitality.
OpenSats has established an expedited grant pathway specifically for red-teaming initiatives to help compensate researchers for AI-related expenses. Additionally, more than 40 Bitcoin and cryptocurrency-related organizations have petitioned prominent AI labs to provide verified open-source security researchers with access to cutting-edge AI models.
Calle observed that Lightning Network software presented particular challenges during the security review process due to its inherent complexity, characterizing it as “more broken than the average.” He emphasized that projects that initiated AI-assisted security audits months earlier are now substantially better positioned than those that delayed.
The critical insight for Bitcoin users is that this comprehensive security review encompasses wallets, Lightning Network infrastructure, payment processing software, and supporting libraries—not Bitcoin’s fundamental consensus protocol.
The post AI-Powered Security Scan Uncovers Nearly 8,000 Potential Flaws in Bitcoin Projects appeared first on Blockonomi.
This articles is written by : Nermeen Nabil Khear Abdelmalak
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