Galaxy Confirms Over 15 Attackers Hit Coldcard Flaw

According to fresh analysis from Galaxy Digital, more than fifteen separate threat actors have taken advantage of a serious flaw in the Coldcard hardware wallet. The research highlights how victim disclosures played a key role in uncovering additional perpetrators who might otherwise have remained u

According to fresh analysis from Galaxy Digital, more than fifteen separate threat actors have taken advantage of a serious flaw in the Coldcard hardware wallet. The research highlights how victim disclosures played a key role in uncovering additional perpetrators who might otherwise have remained undetected. Unlike typical centralized exchange breaches, this exploit operated in a decentralized manner, making it harder to trace without individual reports from affected users.

Expanded Losses Reach Significant Levels

The total impact from the Coldcard issue has escalated considerably. Galaxy Research now places the confirmed damages at around one hundred million dollars distributed across three distinct attack phases. Analysts also point to signs of a possible fourth phase that could push the overall Bitcoin losses close to one hundred thirty million dollars. These figures underscore the scale of the incident and have sparked renewed conversations about the true safety of self-custody solutions for digital assets.

One particular disclosure involving less than one Bitcoin proved especially valuable. It allowed researchers to identify an entirely new attack vector responsible for draining twelve Bitcoin from one hundred twenty-six separate addresses. Such granular information proved essential because the decentralized nature of the exploit differed sharply from more centralized theft patterns.

Low-Cost AI Measures Could Have Stopped the Breach

Experts have noted that a modest investment equivalent to roughly two dollars in AI-based security enhancements might have prevented the entire vulnerability from being exploited. This observation stems from experiments showing that certain artificial intelligence systems were able to identify the underlying issue within minutes when given access to relevant code. One open-source model reportedly located the weakness in approximately twenty minutes even when internet searches were disabled, illustrating both the promise and the current limitations of automated discovery tools.

However, caution is warranted regarding claims of rapid AI detection. Some reports originated from users who already knew the vulnerability existed after its public disclosure. Without controlled blind testing or proper evaluation of error rates, such assertions remain difficult to verify. The episode nevertheless demonstrates how quickly machine learning capabilities are advancing in the cybersecurity domain.

Root Cause Tied to Private Key Entropy Levels

Further technical examination revealed that the vulnerability originated from an unexpectedly low level of entropy used during private key generation. Coldcard employed only forty bits of entropy because of a firmware error, far below the one hundred twenty-eight bits typically provided by a standard twelve-word seed phrase in competing wallets. This reduced randomness made brute-force attempts significantly more feasible for determined attackers.

Industry observers expect the cost and time required to uncover similar weaknesses to continue declining as artificial intelligence tools become more sophisticated. The incident has therefore intensified discussions about rigorous testing standards for hardware wallets and whether current practices adequately protect users who choose to manage their own Bitcoin holdings.