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AI model distillation trains a smaller “student” model using signals from a larger “teacher” model. With large language models, those signals can be answers gathered by prompting the teacher. Distillation is a legitimate training technique; the controversy is about using a service’s outputs at scale to imitate its capabilities without authorization. It is hard to stop because individual prompts can look ordinary, while the more revealing pattern may span many accounts and intermediaries.
How does AI model distillation work?
A teacher model provides examples or other learning signals that help train a student. For a language model, a developer might collect teacher-generated answers to prompts and use them as examples in supervised fine-tuning, or incorporate them into a reinforcement-learning pipeline. The student learns from the outputs; it does not need access to the teacher’s internal weights.
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A 2024 survey by Xiaohan Xu and coauthors describes large-language-model distillation as a way to transfer capabilities, including for model compression, data augmentation, and skill- or domain-focused training. A smaller student may imitate selected behaviors without becoming identical to its teacher. “Copying” is therefore an informal description, not evidence that the student reproduces the teacher’s internal structure or full capabilities.
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Collected answers can demonstrate how to respond to particular prompts or perform particular tasks. Their usefulness depends on what is queried, how much relevant material is collected, and how the student is trained. Output-based training can transfer useful behavior, but it does not by itself establish that the student matches the teacher across all tasks.
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When is distillation legitimate, and when is it an unauthorized extraction?
The technique itself is not inherently malicious. The distinction is whether the training is authorized and consistent with the relevant service terms, alongside the purpose and scale of the collection. There is no universal query-count threshold in the sources discussed here that separates ordinary use from an attack.
| Question | Legitimate distillation | Unauthorized model extraction |
|---|---|---|
| Authorization and terms | Conducted with the required permission and in compliance with the applicable terms. | Conducted without authorization or in violation of applicable terms; the exact terms depend on the service. |
| Training material | May use teacher outputs or other permitted training signals. | Systematically collects a service’s outputs to train or improve a competing model. |
| Scale and coordination | Not defined by a universal scale threshold; the purpose and permission matter. | May involve high-volume, repeated, coordinated queries across accounts or infrastructure, as described in Anthropic’s 2026 report. |
| Goal | Capability transfer, compression, or focused improvement within permitted use. | Imitation of a provider’s differentiated capabilities through its service outputs. |
These are practical distinctions, not a legal test. A high volume of use alone does not establish a violation, and the sources do not establish that every form of output-based training is unlawful.
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What campaigns has Anthropic reported?
In a report dated 23 February 2026, Anthropic said it identified large-scale campaigns that it attributed to three AI companies. The figures below are Anthropic’s reported exchange counts and attributions; they are not independently established findings in the sources discussed here.
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| Anthropic’s attribution | Exchanges Anthropic reported |
|---|---|
| DeepSeek | More than 150,000 |
| Moonshot AI | More than 3.4 million |
| MiniMax | More than 13 million |
Anthropic said the campaigns targeted capabilities including reasoning, agentic tool use, coding, data analysis, computer use, and computer vision. It described coordinated accounts, proxy access, and repeated prompt structures, and said some traffic shifted to a newer model after its launch. These are the company’s reported observations and allegations, not an independent audit of the campaigns.
Why is model distillation so hard to stop?
An individual prompt can look like normal use
A single question to a model usually does not reveal whether the answer is for ordinary work, evaluation, research, or training a competing system. The purpose becomes more apparent, if at all, through patterns: unusually numerous or repetitive requests, coordinated behavior across accounts, or concentrated queries about capabilities that are valuable training targets.
Traffic can be distributed
Anthropic’s account describes campaigns using fraudulent accounts and proxy services. If requests are spread across accounts or routed through intermediaries, blocking one account may not stop the broader activity. Anthropic said it used IP correlation, request metadata, infrastructure indicators, and, in some cases, corroboration from industry partners to attribute activity. Those methods are described by the company; the report does not establish that they will identify every campaign.
Collection and attribution are different problems
A provider may notice suspicious behavior without being able to prove who organized it or what a student model learned from the outputs. Conversely, a model that appears to imitate a teacher does not, on that fact alone, show how its training data was obtained. Detection, attribution, and evidence of terms violations require different kinds of information.
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Anthropic describes several controls and analysis methods. They address different stages of the problem, and none amounts to proof that output collection can always be prevented.
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| Defense or function | What it can do | What it does not establish |
|---|---|---|
| Access controls and stronger verification | Raise the effort required to obtain or maintain access through certain account pathways. | That all unauthorized users or account networks will be blocked. |
| Classifiers and behavioral fingerprinting | Help flag requests or activity patterns that appear consistent with large-scale extraction. | That an isolated prompt reveals its purpose, or that every flagged pattern proves a violation. |
| Cross-account activity analysis | Help identify coordination that may be missed when accounts are examined separately. | That distributed activity can always be linked to a responsible organization. |
| Information sharing | Let organizations compare indicators that may help identify related activity. | That shared indicators are complete or that attribution is certain. |
| Output safeguards | Aim to reduce how useful outputs are as training material for unauthorized imitation. | That answers remain unusable for training while retaining their ordinary usefulness. |
Watermarks can help with monitoring, but are not an unbreakable lock
Watermarking attempts to place a detectable signal in generated text. A 2025 ACL paper by Leyi Pan and coauthors reports that, in its experiments, targeted paraphrasing and inference-time watermark neutralization could remove inherited watermarks while preserving useful knowledge transfer. That result is limited to the paper’s methods and experimental conditions; it does not show that every watermark can always be removed. It does show why a watermark is better understood as a potential monitoring signal than as a guarantee against distillation.
Trace rewriting is an experimental approach
A 2026 ACL paper by Xinhang Ma and coauthors studies rewriting teacher-generated reasoning traces to make them less useful for unauthorized distillation while aiming to retain correct answers and semantic coherence. Its abstract reports experimental anti-distillation effects and detectable watermarks. This is a research approach, not evidence that a deployed defense works universally.
Is training on another AI service’s answers allowed?
It depends in part on the service’s current terms and the circumstances of use. The 2025 ACL paper notes that some leading large-language-model services expressly prohibit using outputs to train competing models. Terms differ among providers and can change, so anyone considering this use should consult the current terms for the specific service.
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