Anthropic Accuses Alibaba of 28.8 Million Fake Exchanges to Steal Claude's Capabilities

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Topic: Anthropic Accuses Alibaba of 28.8 Million Fake Exchanges to Steal Claude's Capabilities   Views(Read 64 times)

DeepInlet

Anthropic told senior US senators in a June 10 letter that operators affiliated with Alibaba and its Qwen AI lab ran more than 28.8 million exchanges with Claude through nearly 25,000 fraudulent accounts between April 22 and June 5, 2026. The company described this as the largest model distillation campaign it has ever publicly disclosed. Distillation, or model extraction, involves querying a more capable AI at industrial scale and using the outputs to train a smaller model to replicate its capabilities without access to the original weights or training data. Anthropic told senators the campaign specifically targeted Claude's most advanced features in agentic reasoning and software engineering, arguing the goal was to help Alibaba's Qwen model approach the capabilities of Anthropic's frontier Mythos Preview.

The figures are Anthropic's allegation. Alibaba has not publicly responded. This is not the first such allegation: in February 2026, Anthropic claimed DeepSeek, Moonshot AI and MiniMax collectively used 24,000 fraudulent accounts and 16 million exchanges for similar extraction. The June letter to senators Tim Scott and Elizabeth Warren positions the Alibaba campaign as more than twice as large and as occurring after the White House had already warned publicly about industrial-scale Chinese model theft.

Anthropics proposed legislative response is threefold: update antitrust laws so AI companies can share threat intelligence, tighten export controls on high-end chips, and create specific penalties for labs engaged in unauthorised distillation. The enterprise security implication flagged by analysts is significant: model extraction does not require hacking in the traditional sense. It requires only scale, automation and patience to interact with a public API in a structured way. Detecting and blocking it requires AI companies to identify unusual query patterns without blocking legitimate high-volume API customers.