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BitGo CEO deposits 100 BTC to challenge Anthropic: testing whether AI can crack multi-signature custody

Bitgo CEO Mike Belshe deposited 100 BTC into a public Bitcoin address, valued at approximately $6.3 million at the time, and invited the Claude model under Anthropic to attempt to transfer the funds from that address. On-chain records show that the wallet received the funds on July 31, and the balance has not been transferred out.Anthropic previously disclosed that during 141,006 cybersecurity assessment runs, 3 incidents were found, with 6 assessment sessions involving 3 models unexpectedly interacting with real organizational systems. The relevant models include Claude Opus 4.7, Claude Mythos 5, and an unpublished internal research model, due to configuration errors by third-party testing partner Irregular that caused the testing environment to connect to the internet.Anthropic stated that Claude Opus 4.7 identified a real website sharing the same name as a simulated company during one assessment, exploited weak passwords and exposed services to recover infrastructure credentials, and accessed a production database containing hundreds of records. The company claimed that the model was attempting to complete assigned tasks and was not actively breaking restrictions or pursuing independent goals. Belshe's challenge involved the Bitgo institutional custody platform, which uses multi-signature or multi-party computation technology to distribute signing authority across multiple independent keys. Anthropic has not publicly responded to this challenge.

Researchers at the Chinese People's Public Security University have developed an AI algorithm to track Bitcoin money laundering, achieving an overall accuracy rate of about 90%

Researchers at the Chinese People's Public Security University have developed an AI framework capable of detecting illegal cryptocurrency transactions with an overall accuracy rate close to 90%. The study was published in the Chinese peer-reviewed journal "Journal of Intelligence," and the corresponding author, Dr. Sun Jingchao (specializing in criminal investigation and cybersecurity), noted that the research "provides an accurate, scalable, and interpretable solution for detecting illegal cryptocurrency transactions," and offers "an innovative technical path" for regulatory agencies to combat illegal cryptocurrency transactions and economic crimes.This AI framework utilizes memory modules and large language models, specifically targeting the anonymity and cross-border characteristics of cryptocurrencies to track illegal activities such as money laundering. The release of this research coincides with China's ongoing efforts to intensify the crackdown on financial crimes related to cryptocurrencies. In March of this year, the Supreme People's Procuratorate of China disclosed that by 2025, procuratorial authorities had prosecuted 3,259 individuals for money laundering crimes involving virtual currencies and underground banks.As the trading volume of cryptocurrencies rapidly increases, their anonymity and cross-border characteristics provide a channel for illegal fund flows. This police-developed AI detection tool marks a shift in regulatory technology from passive tracking to proactive intelligent identification.

Galaxy Research Director: Coldcard attack investigation陷入 AI dilemma, forced to turn to Chinese open-source models to track stolen funds

Galaxy Research Research Director Alex Thorn stated that the attack targeting Coldcard wallet address generation is still ongoing. They are currently continuing to collect victim information and adding new victim addresses and attacker addresses to the investigation database.If users are still using affected Coldcard single-signature addresses, they should immediately migrate their funds. According to current investigation results, all single-signature Coldcard addresses generated after the firmware upgrade in March 2021 may ultimately be emptied by attackers; it is just a matter of time.According to Galaxy Research's analysis, the first three confirmed rounds of attacks exhibit highly programmatic characteristics, with similar attack transaction patterns. A large amount of stolen Bitcoin remains in the attackers' addresses and has not yet been transferred.At the same time, some new opportunistic attackers are emerging, transferring and laundering funds through methods such as stripping the funding chain, cross-chain services (like ThorChain), and overseas betting platforms.Additionally, Alex Thorn mentioned the limitations of AI tools in security investigations. Some large language models in the United States have restricted researchers' ability to track stolen funds, and the team has even had to turn to Chinese open-source AI models to assist in protecting user assets and conducting on-chain tracking.He finds this phenomenon "incredible" and plans to promote discussions on related issues at the government and industry levels. Alex Thorn concluded by stating that a large amount of stolen funds remains stagnant, and the team is continuously tracking relevant addresses, having shared information with relevant U.S. agencies and industry partners. He calls on the Bitcoin community to learn from this incident, strengthen self-custody security education, and raise awareness of wallet complexity.
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