📊 Full opportunity report: Did AI Play A Role In Discovering The Coldcard Vulnerability? on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
The Coldcard hardware wallet was exploited after a firmware bug reduced its entropy from 128 to 40 bits. While some claim AI models like Kimi K3 found the vulnerability, evidence suggests the attack was computationally straightforward and AI likely played a limited role.
Coldcard hardware wallets were exploited in late July 2023 after a firmware vulnerability reduced their randomness, leading to the theft of over 1,816 BTC. The attack was automated and involved precomputed keys, not direct hacking of devices. While some claim AI models like Kimi K3 played a role, authorities and researchers have not confirmed any direct AI involvement.
On 30 July, a series of coordinated withdrawals drained over 1,816 BTC from Coldcard wallets, with a significant portion in a single 25-minute window. The breach was linked to a firmware change made in March 2021 that compromised the device’s entropy, collapsing its randomness from 128 bits to approximately 40 bits. This made the seed space searchable by brute-force methods.
Initial speculation suggested that an AI language model, specifically Kimi K3, might have been involved in discovering the vulnerability. This claim was based on the timing of the model’s release and the attack, which occurred shortly after Kimi K3’s weights were made public. However, no concrete evidence has established that AI was used in the discovery process. Coinkite, the maker of Coldcard, stated it has no proof linking the breach to AI, noting that the vulnerability could be exploited through straightforward computational methods.
Experts highlight that the attack was arithmetic in nature, involving brute-force searches that specialized hardware could perform independently of AI assistance. An independent UK–US AI safety evaluation found Kimi K3’s capability in security tasks was limited, reaching only about 40% of the effectiveness of top US models. Additionally, researchers demonstrated that AI models could reproduce known vulnerabilities after they were publicly disclosed, but this does not prove the models discovered the flaw unprompted.
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 for AI and Hardware Wallet Security
This incident underscores that hardware wallet vulnerabilities can be exploited through computational means without needing advanced AI capabilities. While AI can lower the cost of analyzing code or searching for flaws, it is not a magic bullet for discovering security vulnerabilities. The fact that Coinkite’s own AI review did not detect the bug highlights the current limitations of AI in security auditing and the importance of multiple layers of verification. The broader relevance lies in understanding AI’s role in cybersecurity: it may assist but does not replace traditional methods of vulnerability detection.
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Background on Coldcard Firmware and the 2021 Vulnerability
The Coldcard device, designed for secure cold storage of Bitcoin, relies on high-quality randomness during seed generation. In March 2021, a firmware update introduced a bug that caused the device to rely on predictable seed generation, drastically reducing entropy from 128 bits to about 40 bits. This flaw remained undetected until the July attack, enabling an automated, large-scale theft through brute-force methods. The incident has prompted renewed scrutiny of hardware security practices and the role of automated tools in vulnerability discovery.
"We have no evidence that AI models like Kimi K3 discovered the flaw. Our review before the attack did not identify this bug, and the exploitation was consistent with brute-force methods."
— Coinkite spokesperson
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Unconfirmed Role of AI in the Vulnerability Discovery
There is no definitive evidence linking AI models like Kimi K3 to the discovery of the Coldcard firmware flaw. While timing suggests a possible connection, experts agree that the vulnerability could be identified through traditional brute-force techniques without AI assistance. The extent to which AI lowered the cost or facilitated the discovery remains unconfirmed and is subject to ongoing investigation.
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Next Steps in Securing Hardware Wallets and AI’s Security Role
Researchers and manufacturers will likely enhance firmware review processes, possibly integrating more advanced AI tools, but with awareness of their current limitations. Further analysis is expected to clarify whether AI played any role in discovering the flaw or if traditional computational methods sufficed. The incident may lead to stricter security standards for hardware wallets and increased scrutiny of AI-assisted vulnerability detection.
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Key Questions
Did AI models like Kimi K3 actually find the Coldcard vulnerability?
There is no confirmed evidence that AI models directly discovered the vulnerability. The attack was computationally straightforward, and AI’s role remains speculative.
Could AI have made the vulnerability easier to find?
While AI might reduce analysis costs, the core flaw—reduced entropy—was detectable through brute-force methods that do not require AI assistance.
What does this incident mean for hardware wallet security?
It highlights that vulnerabilities can be exploited through arithmetic and brute-force, underscoring the importance of rigorous firmware testing and verification beyond AI review.
Will AI be used more in security reviews after this?
Likely yes, but with an understanding of AI’s current limitations and as a supplementary tool rather than a primary security scanner.
Source: ThorstenMeyerAI.com
