📊 Full opportunity report: Did An AI Spot The Coldcard Hack First? The Evidence Looks Promising on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
A large-scale Bitcoin theft from Coldcard hardware wallets coincided with the release of an AI model, leading to claims that AI may have helped identify the vulnerability. However, evidence remains inconclusive, and experts emphasize the flaw was exploitable through traditional computational methods.
Confirmed evidence shows that the recent theft of over 1,800 BTC from Coldcard hardware wallets resulted from a firmware flaw that reduced seed entropy, making the wallets vulnerable to brute-force attacks. While there are claims suggesting an AI model may have played a role in discovering the flaw, no conclusive proof has been provided. This development raises questions about AI’s role in security vulnerabilities and the limits of automated code analysis.
On 30 July 2023, hackers drained more than 1,816 BTC from Coldcard wallets, with an automated pattern indicating the use of precomputed keys rather than victims’ actions. The flaw stemmed from a firmware update in March 2021, which reduced the seed entropy from 128 bits to approximately 40 bits, making brute-force attacks computationally feasible. The theft was executed in a series of waves over several days, targeting hundreds of wallets within minutes.
Within hours of the attack, a pseudonymous online account claimed that an AI model, Kimi K3, was responsible for identifying critical vulnerabilities in the affected wallets, citing the timing of the model’s release and the attack as circumstantial evidence. However, Coinkite, the maker of Coldcard, stated that no evidence links the attack to AI, and emphasized that the vulnerability was known publicly before the theft. Experts point out that the entropy reduction was a well-understood computational problem that could be brute-forced without AI assistance.
Independent researchers confirmed that AI models could reproduce the vulnerability after it became public, but this does not prove the AI discovered the flaw unprompted. The attack relied on arithmetic brute-force methods, which do not require advanced AI capabilities, and the model’s ability to exploit security flaws remains limited according to recent evaluations.
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's Role in Coldcard Vulnerability Discovery
This case highlights the ongoing debate about AI’s capacity to identify security flaws in hardware and software. While some claims suggest AI models like Kimi K3 might have aided in discovering the Coldcard firmware flaw, current evidence indicates that the vulnerability was exploitable through straightforward computational methods. The incident underscores that, at least for now, AI’s role in security breaches may be more about lowering costs or assisting analysis rather than independently discovering critical flaws. It also reveals the limits of AI-based security reviews, as Coinkite’s prior assessment did not identify the bug.
For the broader community, this raises questions about relying on AI for security auditing and emphasizes the importance of thorough manual review, especially for hardware devices that safeguard significant assets. The incident also sparks discussion on the need for better firmware verification processes and the potential for AI to either aid or hinder security efforts depending on how it is applied.
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Background on Coldcard Firmware Flaw and Recent Theft
The Coldcard hardware wallet, produced by Canadian firm Coinkite, is designed for secure offline storage of Bitcoin. In March 2021, a firmware update inadvertently reduced seed entropy from 128 bits to around 40 bits, significantly weakening the security of new wallets initialized after that update. This flaw was publicly known but not widely exploited until late July 2023.
Between 29 July and early August, hackers drained over 1,800 BTC from hundreds of wallets in coordinated waves, suggesting an automated, precomputed attack rather than victims’ panic moves. The pattern of large-scale, rapid withdrawals points to the use of computational brute-force methods, which could be executed with specialized hardware or software without advanced AI assistance.
Claims emerged shortly after the theft that an AI model, Kimi K3, might have been involved in discovering the vulnerability. The timing of the model’s release and the attack fueled speculation, but no concrete evidence has supported this link. Coinkite’s own analysis indicates that the vulnerability was already publicly known, and the attack’s mechanics do not require AI capabilities.
"We have no evidence linking the recent theft to AI or any specific actor. The flaw was publicly documented before the attack occurred."
— Coinkite spokesperson
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Unconfirmed Links Between AI and the Coldcard Attack
There is currently no direct evidence that AI models like Kimi K3 discovered the Coldcard firmware vulnerability independently. The claims are circumstantial, based on timing and the capabilities of the models. Experts agree that brute-force arithmetic attacks do not require AI, and the vulnerability was already publicly known before the theft. The role of AI remains speculative, and investigations have not confirmed any AI involvement in the discovery or exploitation of the flaw.
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Next Steps in Verifying AI’s Role and Strengthening Coldcard Security
Further forensic analysis is expected to clarify whether AI played any role in discovering the vulnerability. Coinkite and security researchers are likely to review firmware security processes and improve verification methods. The incident may also prompt wider discussions on AI’s place in security audits, emphasizing the need for manual review and better firmware validation protocols. Monitoring for additional exploits or similar vulnerabilities will continue as the community assesses AI’s impact on hardware security.
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Key Questions
Did AI actually discover the Coldcard firmware flaw?
There is no confirmed evidence that AI models like Kimi K3 discovered the flaw independently. The claims are circumstantial, and experts note that the vulnerability was publicly known before the attack, which could be exploited through traditional computational methods.
Could AI have helped reduce the cost of finding the vulnerability?
Yes, AI may have lowered the computational cost of searching for the flaw, but it was not necessary. The vulnerability’s nature allowed brute-force attacks without AI assistance, and the main factor was the reduced seed entropy.
Will Coldcard improve its firmware security after this breach?
It is likely that Coinkite will review and strengthen its firmware verification processes to prevent similar vulnerabilities, especially given the public scrutiny and the incident’s implications for hardware wallet security.
What does this mean for AI’s role in cybersecurity?
This incident illustrates that AI’s current capabilities are limited in discovering new vulnerabilities without prior knowledge. It highlights the importance of manual review and traditional security practices, even as AI tools become more prevalent.
Source: ThorstenMeyerAI.com
