Anthropic Accuses Alibaba of Running 29 Million Fake Queries to Clone Claude

Training a frontier artificial intelligence (AI) model costs billions of dollars and years of research. Reproducing much of its behavior can cost a fraction of that, if you have access to enough of its outputs. That gap is what makes model distillation, a technique that trains a smaller AI system on the responses of a more powerful one, the economic problem Anthropic highlighted this week.

Anthropic accused operators affiliated with Alibaba and its AI lab of conducting the largest known distillation campaign against its Claude models to date, CNBC reported. The alleged operation ran from April 22 to June 5 and generated more than 28.8 million interactions with Claude through roughly 25,000 fraudulent accounts.

The scale puts the alleged campaign in a different category from what came before. In February, Anthropic named three Chinese AI labs—DeepSeek, Moonshot AI and MiniMax—as having collectively generated more than 16 million Claude interactions through roughly 24,000 fraudulent accounts in a February blog post. The alleged Alibaba campaign dwarfs that combined total in just six weeks.

How 25,000 Fake Accounts Extracted Claude’s Core Intelligence

The way distillation works is simple. A campaign sends large volumes of carefully designed prompts to a target model and captures its responses. Those responses become training data. The competing model learns to reason and respond in ways that replicate the original, without paying for the research behind it. It is less like hacking a system and more like sitting next to the best student in class and copying every answer they write, at industrial scale.

Detection is hard. A distillation query looks identical to a legitimate one. A developer asking Claude to help debug a function and a campaign systematically extracting Claude’s coding behavior send the same kind of request. The only signal is pattern: massive volume, repetitive structures and prompts targeting the same narrow capabilities, arriving from hundreds of coordinated accounts in sequence. “As organizations increasingly integrate LLMs into their core operations, the proprietary logic and specialized training of these models have emerged as high-value targets,” Google’s Threat Intelligence Group warned in a February blog post, PYMNTS reported.

There is a safety dimension beyond the commercial one. When a lab distills a frontier model without permission, the copy does not inherit the safety guardrails built into the original. The dangerous capabilities transfer through the outputs. The months spent making the model refuse harmful requests do not. Distillation itself is a legitimate and widely used technique. Companies routinely use it to compress their own large models into smaller, faster versions that run more cheaply. The line Anthropic is drawing is between using it on your own models, which is standard practice, and using it on a competitor’s model without permission.

Anthropic Wants Congress to Make Model Theft Illegal

In a letter to senators, Anthropic’s Head of Policy Sarah Heck said the attacks were carried out “illicitly, systematically, and at industrial scale to harvest U.S. AI capabilities across frontier labs and repackage them as their own without incurring the training and R&D costs,” Business Insider reported.

House Republicans are seeking sanctions on Chinese companies that copy American-made AI models, PYMNTS reported. Sen. Bill Hagerty and Sen. Andy Kim are moving to add an amendment to defense legislation that would blacklist or sanction entities found conducting such campaigns, according to CNBC. The White House Office of Science and Technology Policy issued a memorandum in April warning of industrial-scale foreign distillation of U.S. AI models.

The structural problem goes beyond any single campaign. A distillation query is indistinguishable from a legitimate one. The only way to fully close the gap is to restrict who can access the model. That conflicts directly with the commercial logic of selling AI as a service. If adversarial distillation becomes routine, AI labs may find themselves spending as much on access controls and identity verification as they do on training, treating every API call as a potential intelligence transfer rather than a revenue event.