📊 Full opportunity report: The Coldcard Hack And AI: A Deep Dive Into The Possible Connection on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
A hardware security flaw in Coldcard wallets caused a theft of over $116 million in Bitcoin. While some speculate AI models like Kimi K3 played a role, no definitive evidence confirms this connection. The incident highlights ongoing vulnerabilities in offline crypto storage.
Security researchers have confirmed that a flaw in the Coldcard hardware wallet’s firmware was exploited to drain over $116 million in Bitcoin. The breach involved 1,816 BTC stolen from more than 5,200 addresses, despite the wallets never connecting to the internet, highlighting a critical vulnerability in offline storage devices.
The vulnerability originated from a firmware update in March 2021, which reduced the device’s entropy from 128 bits to approximately 40 bits, making the private keys generated on affected devices predictable and susceptible to brute-force attacks. The theft occurred over multiple waves starting July 30, with a prominent 41-minute window in which approximately 1,083 BTC was drained, primarily from single-signature wallets.
Claims have circulated suggesting that an AI model, specifically Moonshot’s Kimi K3, may have played a role in identifying or exploiting this flaw. However, experts and the device manufacturer, Coinkite, have emphasized that no direct evidence links AI models to the breach. The attack was primarily arithmetic, leveraging the reduced entropy to systematically recover private keys, which could be done with specialized hardware independent of AI assistance.
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 the Coldcard Firmware Flaw and AI Claims
This incident underscores the risks posed by hardware wallet vulnerabilities, especially when firmware issues reduce security guarantees. The speculation around AI models like Kimi K3 illustrates the growing concern about AI’s potential role in cybersecurity breaches, even though current evidence does not confirm AI involvement. It highlights the importance of rigorous security audits and the limits of AI in detecting hardware-level flaws.
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Background on Coldcard and the 2021 Firmware Update
Coldcard, produced by Canadian firm Coinkite, is a widely used hardware wallet designed for secure Bitcoin storage. In March 2021, a firmware update was released that inadvertently compromised the device’s entropy source, reducing its randomness from 128 bits to roughly 40 bits. This flaw remained unnoticed until the recent thefts, which exploited this predictable seed space. Prior to this, Coldcard was considered among the most secure offline options for Bitcoin holders.
"We are actively investigating the breach and have not found any evidence linking AI models to the vulnerability. The issue stems from a firmware flaw that significantly reduced entropy."
— Coinkite spokesperson
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Unconfirmed Role of AI in the Coldcard Breach
While some claims suggest AI models like Kimi K3 may have been used to identify or exploit the vulnerability, there is no direct evidence confirming this. The breach was primarily arithmetic, and AI’s role remains speculative. Investigators have not established how the flaw was discovered or whether AI models played any part in its exploitation.
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Ongoing Investigations and Security Reassessments
Authorities and Coinkite are conducting detailed investigations into the breach, including examining whether AI tools were involved. The company has also committed to reviewing its firmware security protocols and improving defenses against future vulnerabilities. Industry experts emphasize the need for enhanced hardware security audits and awareness of firmware-related risks.
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Key Questions
Was AI directly responsible for the Coldcard breach?
There is no confirmed evidence that AI models, including Kimi K3, directly caused or exploited the vulnerability. The breach was primarily arithmetic due to reduced entropy in the firmware.
How did the firmware flaw reduce security?
The firmware update from March 2021 caused Coldcard devices to generate less unpredictable, more predictable seeds, decreasing entropy from 128 bits to about 40 bits, making brute-force attacks feasible.
Could AI have lowered the cost of exploiting the flaw?
Yes, AI-assisted code analysis can make finding such vulnerabilities cheaper, but the core attack was arithmetic and could be performed with specialized hardware alone without AI assistance.
What is being done to prevent similar incidents?
Coinkite and security researchers are reviewing firmware security protocols, improving testing procedures, and emphasizing hardware security to prevent future vulnerabilities.
Is there a risk for other hardware wallets?
Yes, firmware flaws can affect other devices, highlighting the importance of rigorous security audits and updates for all hardware wallets.
Source: ThorstenMeyerAI.com
