📊 Full opportunity report: Did AI Succeed Where Humans Failed In Discovering The Coldcard Hack? on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
An exploit of Coldcard hardware wallets resulted in the theft of over 1,800 BTC. While some claim AI models like Kimi K3 discovered the flaw, evidence suggests the breach was due to a known entropy vulnerability, not AI detection. The story raises questions about AI’s role in security flaws.
Confirmed reports indicate that over 1,800 BTC were drained from Coldcard hardware wallets in late July, despite the devices being offline and designed for maximum security. The incident is linked to a firmware flaw that reduced the device’s entropy, enabling automated, large-scale theft. While some claim AI models like Kimi K3 identified the vulnerability, authorities and experts have not confirmed AI involvement.
The breach involved 1,196 addresses being drained within a 41-minute window, totaling approximately $70 million. The attack appears to have been automated, with the pattern of wallet draining suggesting precomputed, brute-force operations. The core technical issue was a firmware update from March 2021 that quietly compromised the device’s randomness, collapsing its entropy from 128 bits to about 40 bits, making brute-force attacks feasible.
Claims that AI models, specifically Kimi K3, discovered the vulnerability within days of its exploitation have circulated widely. However, experts point out that the model’s capabilities are limited in security-specific tasks and that the attack was primarily arithmetic, relying on computational brute-force, not AI detection. Coinkite, the device manufacturer, stated that an attacker may have used AI to analyze firmware but has not confirmed any direct AI involvement in discovering the flaw. Additionally, independent researchers demonstrated that AI could analyze known vulnerabilities after they became public, but this does not prove AI identified the flaw beforehand.
Offline hardware wallets were emptied without an attacker touching a single device. The keys weren’t stolen — they were regenerated, because a firmware flaw had quietly shrunk the space of possible keys to something a machine could search.
▲ AI attribution unproven · Kimi K3 claim is a community theoryA hardware wallet’s security rests entirely on one moment: the randomness used to generate its recovery seed. A 2021 firmware change quietly broke that randomness on affected Coldcard Mk3 devices.
The signature — hundreds of unrelated wallets emptied against a prepared list — points to an automated operation working from precomputed keys, per Galaxy Research on-chain analysis.
A viral post framed this as “the AI reckoning” and named Moonshot’s new open-weight model. The timing is suggestive. The evidence is not conclusive.
- K3 weights dropped 27 Jul; first draining ~29–30 Jul — two days apart
- Public firmware is exactly what an AI code agent can read
- Widely shared, emotionally resonant, and entirely uncorroborated
- UK–US AISI eval: K3’s exploit ability reaches only ~40% of frontier US models
- Independent researchers reproduced it after the flaw was public — not cold
- A 40-bit search needs no LLM; specialised hardware brute-forces it
Strip out the attribution entirely and the important finding survives.
The real shift isn’t that AI broke cryptography — the mathematics held; the software around it did not. It’s that frontier models are collapsing the window between when a vulnerability is created, discovered, and exploited. A flaw sat dormant for four years. That dormancy is becoming the exception.
and the window from dormant bug to drained wallet just got much shorter for everyone shipping code.
Implications of AI in Security Vulnerability Discovery
This incident underscores the ongoing debate about AI's role in cybersecurity. While AI can assist in analyzing code and identifying weaknesses, current evidence suggests that the Coldcard breach was primarily a technical failure due to a firmware bug, not an AI-driven discovery. The case highlights that AI's contribution to security flaws remains uncertain, and that many vulnerabilities can be exploited through brute-force methods without AI assistance. The event also raises questions about the effectiveness of AI in security audits, given that Coinkite's own review failed to detect the bug.
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Background on Coldcard and the Firmware Flaw
Coldcard, manufactured by Canadian firm Coinkite, is a hardware wallet designed for secure, offline storage of Bitcoin. In March 2021, a firmware update introduced a vulnerability that reduced the device’s entropy from a secure 128 bits to approximately 40 bits, making the generation of recovery seeds predictable and susceptible to brute-force attacks. The vulnerability remained undetected for over two years, until the July 2023 theft, which involved multiple waves of automated draining of wallets. The incident has prompted scrutiny of hardware wallet security and the potential role of AI in vulnerability discovery.
"We cannot confirm how the flaw was discovered, but we assume an attacker may have used AI to analyze our firmware. Our internal review did not detect the issue."
— Coinkite spokesperson
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Unconfirmed Role of AI in Vulnerability Discovery
There is no definitive evidence that AI models like Kimi K3 identified the firmware flaw before it was exploited. While some claims suggest AI played a role, experts point out that the attack was primarily arithmetic brute-force, which does not require advanced AI capabilities. The extent of AI's involvement remains speculative and unconfirmed.
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Future Steps in Hardware Wallet Security Analysis
Authorities and security researchers are expected to conduct further investigations into the firmware vulnerability and the role of AI in its discovery. Coinkite may enhance its firmware review processes, possibly integrating more rigorous AI-based security checks. The incident is likely to prompt broader industry discussions on AI's capabilities and limitations in cybersecurity, as well as improved hardware security standards.
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Key Questions
Did AI models like Kimi K3 actually discover the Coldcard firmware flaw?
There is no confirmed evidence that AI models discovered the flaw independently. The attack was primarily arithmetic brute-force, and claims of AI involvement are speculative.
Could AI have lowered the cost of discovering this vulnerability?
Yes, AI tools can assist in analyzing code and vulnerabilities, making it easier to identify flaws after they are publicly known. However, this does not mean AI found the flaw before it was exploited.
What does this incident mean for hardware wallet security?
It highlights the importance of rigorous firmware security reviews and the potential vulnerabilities introduced by software updates. It also raises questions about AI's role in security assessments.
Will this lead to changes in how hardware wallets are secured?
Likely. Manufacturers may adopt more advanced security testing, possibly including AI-based analysis, to prevent similar vulnerabilities in the future.
Source: ThorstenMeyerAI.com
