A vulnerability in the Coldcard hardware wallet, discovered by an AI model for roughly $2, is forcing a rethink of security economics across the crypto hardware industry. The flaw, which could have allowed an attacker to extract private keys under certain conditions, was found with startling ease and minimal cost.
According to a market update from WuBlockchain, Dragonfly Managing Partner Haseeb Qureshi shared details of the incident, noting that it shows how AI is reshaping the economics of product safety testing. He introduced what he calls a “Cost of Discovery” metric, which measures how cheaply a frontier model can reproduce a vulnerability.
The $2 Discovery
Qureshi estimated the Coldcard flaw’s discovery cost at around $2. One reported attempt using Claude Code took just eight minutes, though he cautioned that the speed may have been influenced by web search access. A separate offline test with the GLM model reproduced the issue in about 20 minutes, confirming that the vulnerability was not dependent on real-time internet lookups.
The figures are jarring because hardware wallets are supposed to be the last line of defense for serious crypto holders. A flaw that costs pocket change to find undermines the assumption that rigorous, expensive auditing is the only way to break a device. It signals that the cost curve for vulnerability discovery is collapsing.
Cost of Discovery and Its Implications
Qureshi’s new metric isn’t just an academic exercise. It provides a raw dollar figure that hardware makers can benchmark against their own internal testing budgets. If a flaw can be spotted for $2, then any well-resourced adversary—or even a curious researcher—can automate the search and scale it across multiple firmware versions or device models.
This shifts the burden onto wallet manufacturers. They now face a future where security cannot rely on the obscurity of embedded code or the high cost of reverse engineering. Instead, they must assume that AI tools will probe every release, and that the time between a firmware update and a public vulnerability disclosure could shrink to hours, not weeks.
The incident also pressures bug bounty programs. Payouts that once seemed generous may look inflated when the cost to find a bug is negligible. Companies will need to decide whether to reward low-cost AI-aided discoveries at all, or to restructure incentives to prioritize severity over novelty of the method.
AI’s Growing Footprint in Crypto
The Coldcard event lands at a moment when AI is permeating nearly every corner of the crypto market. UXLINK and Origins Network are pairing up to power scalable AI-driven Web3 applications, while storage networks like Filecoin are attracting attention because of rising demand for on-chain AI data storage. The hype around AI-themed assets remains strong too, with BRC-20 NFTs like $X@AI topping weekly sales charts.
But the Coldcard case shows a grittier side of this integration. AI is not just powering new token use cases; it is rewriting the security playbook for the infrastructure layer that safeguards billions of dollars in digital assets. For hardware wallet makers, the competitive moat is no longer just about chip design or sealed elements—it now includes the speed and cost of machine-assisted auditing.
What Remains Unclear
It’s still an open question whether other widely used hardware wallets will face similarly cheap discoveries. Coldcard is known for its open-source, Bitcoin-only focus, which may make its codebase easier to parse than some closed-source alternatives. But the trend line is unmistakable: frontier AI models are getting faster and cheaper, and their application to security testing is only going to intensify.
There’s also uncertainty around whether the cost-of-discovery data will flow into regulatory frameworks. If a vulnerability can be found for pocket change, should the bar for mandatory disclosure or recall become lower? That debate hasn’t started yet, but the events surrounding this $2 flaw suggest it won’t be long before it does.