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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallAnthropic says it detected coordinated campaigns that used Claude to generate training material for competing AI models. Its February 2026 announcement named DeepSeek, Moonshot AI and MiniMax; a September update described additional activity and much larger attributed volumes. These remain Anthropic’s allegations: the reports reviewed do not provide raw logs for independent audit.
What model distillation is—and why the label needs care
Model distillation is a training method: a less capable model learns from outputs generated by a stronger model. The technique is not inherently improper. Anthropic says frontier labs use it legitimately to create smaller, less expensive models for customers. Written testimony to the U.S. Senate Judiciary Committee likewise describes prompting one model to generate outputs that can become training data for another.
Anthropic’s term “distillation attack” refers to what it alleges was misuse of that method—not to a separate machine-learning technique that is always illegitimate. In its February 23, 2026, post, the company wrote: “Distillation is a widely used and legitimate training method.” It also said, “What distinguishes a distillation attack from normal usage is the pattern.” That is Anthropic’s framing, not an independently validated universal test.
The key questions are whether model access and training use were authorized; whether traffic was coordinated through multiple accounts or proxies; whether prompts repeatedly targeted valuable capabilities; and whether end users’ conversations were replayed, routed or retained. The reports do not independently establish how much capability any resulting model gained from the exchanges.
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What Anthropic alleged in February 2026
In its February 23 announcement, Anthropic said three labs collectively generated more than 16 million exchanges with Claude through approximately 24,000 accounts it called fraudulent. It alleged the activity used proxy services and coordinated access patterns, and violated its terms and regional restrictions. The company’s reported figures for the three campaigns were:
| Lab named by Anthropic | Exchanges Anthropic reported | Capabilities Anthropic said were targeted |
|---|---|---|
| DeepSeek | Over 150,000 | Reasoning across tasks, rubric-based grading that could support reinforcement learning, and responses to politically sensitive questions that avoided censorship. |
| Moonshot AI | Over 3.4 million | Agentic reasoning and tool use, coding and data analysis, computer-use agents, and computer vision. |
| MiniMax | Over 13 million | Agentic coding, tool use, and orchestration. |
These are rounded figures and attributions from Anthropic’s account, not independently reconciled counts. The company described synchronized traffic, repeated prompt patterns and other account or infrastructure indicators. For DeepSeek, it said prompts elicited written reasoning traces and that request metadata linked accounts to researchers at the lab. For Moonshot, it reported hundreds of accounts across multiple access paths and a later effort to reconstruct reasoning traces; Anthropic said metadata matched public profiles of senior staff. For MiniMax, it cited metadata and infrastructure indicators and said the activity began before the model it was training was released. Anthropic further said that after it released a new model, MiniMax redirected nearly half its traffic within 24 hours.
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How the allegations changed by September
Anthropic’s September 2026 report said it had detected and disrupted further campaigns since February, which it attributed with high confidence to several additional labs. It described the activity as targeting Opus-class models. The later figures below are the company’s measurements and attributions for the stated periods; an exchange count alone does not show what training data was retained or how much it affected a model.
| Lab or activity named by Anthropic | Volume and period Anthropic reported | What the report alleges |
|---|---|---|
| Alibaba | Over 151 million exchanges between May and July 2026 | Extraction of reasoning transcripts from Opus 4.6 and 4.7, which Anthropic said were used in supervised fine-tuning and Qwen training. The report said activity peaked near three million exchanges a day and involved more than 3,500 accounts. |
| Moonshot | Over 23 million exchanges between May and July 2026 | Some Kimi customer requests were forwarded to Claude, and at least part of those exchanges was saved. |
| DeepSeek | Over 12.1 million exchanges over 14 days in July 2026 | A separate campaign attributed by Anthropic to DeepSeek. |
| Xiaomi | Over 400,000 requests across more than 1,500 accounts over 20 days in March and April 2026 | Replay of user conversations and coding sessions from Xiaomi’s own models through Claude. |
| Zhipu | Not stated in the September report’s figures summarized here | Anthropic described separate activity attributed to Zhipu. |
Anthropic’s February and September figures describe separate periods and should not be added together as if they were a single audited total. The September report also described an ecosystem of third-party routing and reseller services that it said could retain, resell or route exchanges. It cited sensitive personal and company information appearing in relayed traffic. Those are the company’s reported findings; they do not establish that every routing service handles data that way.
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How the alleged campaigns could operate
Anthropic says commercial access to Claude is not offered in China and alleges that proxy networks and fraudulent accounts were used to circumvent access restrictions. It described “hydra cluster” networks that spread requests across many accounts and services, mixing suspicious traffic with ordinary use. In February, the company said one proxy network managed more than 20,000 accounts simultaneously.
Distributed access can make a campaign harder to distinguish from normal use: an individual account may appear unremarkable even when many accounts are coordinating similar prompts. Anthropic says its attribution relied on combinations of metadata, infrastructure indicators, synchronized request patterns, account characteristics and, in one case, product-release timing. The company has not made the underlying raw logs available for the independent re-analysis reflected in the sources reviewed here.
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What this could mean for users of AI routing services
A model router or reseller can sit between a user and the model provider. If it forwards a prompt to another provider, the intermediary may also process or retain that prompt under its own terms. Anthropic’s September report alleges that some services forwarded customer requests to Claude and saved at least some exchanges; it also says sensitive information appeared in relayed traffic. The report does not support treating every router as unsafe, but it does make the intermediary’s data practices relevant.
- Before sending confidential, personal or proprietary material through a routing service, check which providers may receive it and what the service’s retention and reuse terms say.
- Distinguish the router’s promises from the underlying model provider’s policies: both may matter when a request is relayed.
- If you cannot establish how prompts are handled, avoid sending information whose disclosure would create a meaningful risk.
Anthropic’s response—and the access trade-off
Anthropic says it uses behavioral classifiers and account-network analysis to identify suspicious activity, shares intelligence with other labs and cloud providers, strengthens verification for education, research and startup access, and applies product, API and model safeguards. It says it blocks or bans accounts when its attribution confidence is sufficient. The September report also describes measures intended to make reasoning traces less useful for distillation and identity verification when potential abuse is detected.
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Controls can reduce opportunities for coordinated extraction, but stricter checks can also burden legitimate users. Written Senate Judiciary Committee testimony notes the tension between preventing misuse and keeping products accessible, including the risk that enforcement measures can incorrectly flag normal activity. The evidence therefore supports neither an assumption that every restriction is harmless nor that every flagged user is engaged in abuse.
What is established—and what remains an allegation
Anthropic’s February post and September report are primary sources for what the company says it observed, how it attributed activity and what it did in response. The September report describes its later attributions as high confidence. But the materials reviewed do not expose raw telemetry for independent verification, and the congressional testimony largely relies on company statements. No court finding or independent technical audit of the campaign logs is established by these sources.
A separate House Select Committee on the CCP witness repeated Anthropic’s initial figures and offered analysis of what the allegations might mean for broader national strategy. That policy interpretation is the witness’s argument, not a direct measurement of the campaigns. The most accurate reading is therefore specific: Anthropic has made detailed, consequential allegations about unauthorized, coordinated use of Claude; the reports provide the company’s account and evidence categories, but not an independent audit of its underlying data.
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