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Bitcoin Quantum Security Crisis: 6.9 million BTC exposed to risk, governance challenges hinder response progress

According to CoinDesk, while quantum computers cannot disrupt the Bitcoin mining mechanism or the blockchain ledger, they may potentially crack the elliptic curve encryption system that protects wallet ownership through Shor's algorithm. Currently, about 6.9 million BTC (approximately one-third of the total supply) face potential risks due to public keys being visible on-chain, including around 1 million early holdings by Satoshi Nakamoto; transactions generated after the Taproot upgrade in 2021 are also affected due to public key exposure.Ethereum has established a formal quantum resistance migration plan since 2018, with 4 full-time teams and over 10 independent development groups, and has launched a dedicated progress website at pq.ethereum.org. In contrast, Bitcoin currently lacks a unified response roadmap, and the existing BIP-360 proposal and BitMEX Research detection scheme have not received widespread support from core developers. Notable Bitcoin advocate Nic Carter pointed out that Bitcoin's response is "the worst," while Blockstream CEO Adam Back believes that current quantum systems are still in the laboratory stage, but he also agrees that optional upgrade solutions should be deployed in advance.Analysts point out that Bitcoin's anti-centralization governance culture makes coordinating large-scale security upgrades extremely difficult, and how to handle historical legacy issues such as Satoshi Nakamoto's holdings is particularly challenging. A related paper from Google warns that once quantum attacks become a reality, the window for response may have already closed.

Coinbase upgrades its anti-fraud system, integrating machine learning with a rules engine, reducing response time to a few hours

Coinbase stated that it is optimizing the rule creation process in its anti-fraud system by integrating machine learning models with a rules engine, achieving more efficient risk management. It also proposed a dual-track strategy of "models responsible for long-term defense, rules responsible for rapid response," and built a unified framework to create a feedback loop between the two: rules are used to capture new types of fraud and train the model in reverse, thereby continuously enhancing overall defense capabilities.In terms of specific optimizations, Coinbase has transformed the previously manual rule creation process into a data-driven and automated recommendation system by restructuring data, automating schema evolution, and introducing notebook-based analytical tools, significantly improving efficiency. Among these improvements, the performance of rule backtesting has increased by more than 10 times, and the overall response time has been reduced from several days to a few hours. Additionally, the new system uses machine learning to recommend parameters, helping to reduce false positive rates while combating fraud and minimizing the impact on normal users. Coinbase indicated that the next step will be to advance event-driven automatic rule generation and explore the "one-click conversion" of efficient rules into model features, further moving towards an automated risk management system.
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