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Anthropic is turning away distillers' paying traffic
The News
On October 12, 2026, Anthropic said early evidence suggests new defences have cut the data harvested by distillers, who covertly extract Claude's capabilities to replicate them in other models, by around 90 to 99%. It says it has turned away “meaningful revenue” and trillions of tokens' worth of paying traffic from distillers.
Chandni Melwani
Founder & Editor
Oct 21, 2026 · 1 MIN READ
Know More
- — Bans: tens of thousands of accounts associated with distillation, often less than two hours after one starts a campaign.
- — Models covered: the two-stage classifier runs on Claude Sonnet 5.5, Opus 5 and 5.5, and Fable 5 and 5.1.
- — Preserved thinking: introduced with Fable 5.1 and Opus 5.5; API accounts created from August 31, 2026 can't edit prior context.
- — Reasoning access: with Claude Sonnet 5.5, transcripts became accessible only to the organisation whose request produced them.
One of Anthropic’s first defences was to show users a summary of Claude’s reasoning, the thinking it writes out before responding. Trained on full Claude Opus 4.8 transcripts, a pretrained Claude model scored 80.4% on the SWE-bench Verified coding benchmark in Anthropic’s test; on summarised ones, 71.8%.
Distillers then prompted Claude to reveal its full reasoning and, once past the first safeguards, ran proxy networks controlling hundreds of thousands of fraudulent accounts, Anthropic says. Newer defences limit where reasoning can go, block extraction attempts with a two-stage classifier, and restrict, then ban, persistent accounts.
Anthropic says Zhipu distilled the cyber capabilities of leading US models while developing GLM-5.3, and describes a case where requests to Moonshot’s Kimi models were secretly forwarded to Claude for distillation. The White House accused Moonshot in July of distilling Anthropic’s Fable. Some days, Anthropic says, the reasoning tokens distillers could extract on its own platform were too few to measure.
The Read
Defences are expensive and “illicit distillers are still paying customers,” Anthropic says, so it “would be easy for companies to do nothing, or not enough.” It published its methods to show “a workable alternative.” Distillers spread traffic across its API, third-party clouds and other inference providers, it says, so extraction will probably drift toward whichever platforms are least defended.
Room for Disagreement
Distillers may not need to move. The extraction figures are Anthropic's estimates, the headline one labelled “early evidence.” Anthropic says it is confident the measures work but has “yet to see a highly sophisticated and persistent attempt to circumvent” them, and doesn't yet know how long the gains will last.
Related
Frequently Asked Questions
What do Anthropic's distillation defences cost legitimate users?
Anthropic says that in February it wrote of working to make distillation harder without affecting the experience for legitimate users, and that “We haven't met this goal completely.” Preserved thinking has required some developers to update their integrations, its classifiers can misidentify or block benign requests in fewer than 0.1% of cases, and it now charges users for those blocked requests. Since September 24 it has resumed charging for requests its safeguards block before Claude responds, in the specific areas, including distillation, where they have a false-positive rate under 0.1%.
Why does Anthropic treat distillation as more than a commercial problem?
It gives two reasons. It says distillation lets companies build models that approach the capability frontier but have, in practice, been released “without proportionally robust safeguards.” And it says illicitly distilled models “present a national security risk,” adding that the companies that distill US models are integrating the capabilities they gain “into foreign military, intelligence, and surveillance systems.”
What does Anthropic ask of other AI companies?
It says preventing illicit distillation “requires work and buy-in from all frontier model developers,” and that its own efforts would be undermined if attackers could easily access similarly capable models that lack similarly robust anti-distillation safeguards. Platforms, it says, can “materially slow down distillation” by quickly detecting and disrupting abuse, and by offboarding fraudulent accounts.
Chandni Melwani
Chandni Melwani is the founder and editor of New in AI, covering AI agents, M&A, and enterprise adoption. She holds a Master's in Management of Artificial Intelligence from Queen's University and brings a practitioner's perspective from her work in Data and AI leadership.
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