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AI model distillation trains a smaller “student” model using signals from a larger “teacher” model. With language models, those signals can include the teacher’s answers to prompts. Distillation is a standard technique with legitimate uses; the controversy arises when someone systematically collects a service’s outputs to imitate its capabilities without permission or in breach of its terms.
How does AI model distillation work?
A teacher model produces responses to prompts, and those responses become examples for training a student. The student can learn to reproduce selected behaviors without the person training it receiving the teacher’s internal model weights. The resulting models need not be identical: output-based training can transfer particular capabilities or response patterns without copying the complete model.
Training may use teacher answers as supervised fine-tuning examples or as part of a reinforcement-learning pipeline. A 2024 survey by Xiaohan Xu and coauthors describes knowledge distillation as a capability-transfer method used for model compression and self-improvement, including data augmentation and training focused on particular skills or domains.
That makes “copying” an informal shorthand. It can describe imitation of selected capabilities, but it does not establish that a student has the teacher’s weights or is an identical replica.
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When is distillation legitimate, and when is it model extraction?
The method itself is not inherently malicious. Authorization, the source and scale of the training examples, compliance with the service’s terms, and the goal of the training help distinguish ordinary distillation from unauthorized extraction.
| Question | Legitimate distillation | Unauthorized extraction |
|---|---|---|
| Is the activity authorized? | It is permitted by the applicable arrangement and service terms. | It is conducted without permission or contrary to applicable terms. |
| Where do the training signals come from? | They may come from a teacher model as part of an authorized transfer, compression, or improvement process. | They may be systematically collected from a service’s outputs through repeated queries. |
| What do the scale and coordination suggest? | The activity is consistent with its authorized training purpose. | Large, coordinated, repetitive querying may indicate an effort to gather training material. |
| What is the apparent goal? | Transfer or improve capabilities within the permitted use. | Imitate a provider’s differentiated capabilities through systematic collection of its outputs. |
Some leading LLM services expressly prohibit using their outputs to train competing models, as discussed in a 2025 ACL paper by Leyi Pan and coauthors. Terms differ and can change, so check the current terms for the specific service. The available evidence does not establish a universal legal rule for every form of distillation, jurisdiction, or set of circumstances.
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What large-scale campaigns have providers reported?
In a report published on 23 February 2026, Anthropic said it had identified campaigns that used fraudulent accounts and proxy services to query Claude at scale. The company attributed the campaigns using signals including IP correlations, request metadata, infrastructure indicators and, in some cases, corroboration from industry partners. These are Anthropic’s reported findings and attributions, not independently audited conclusions.
Anthropic reported these exchange counts for campaigns it attributed to the named companies:
| Company named in 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 reported campaigns targeted capabilities including reasoning, agentic tool use, coding, data analysis, computer use and computer vision. It also described coordinated accounts, proxy access, repeated prompt structures and traffic redirected to a newer model after its launch. Those descriptions and attributions are the company’s account.
Why is model distillation so hard to stop?
Ordinary-looking prompts can add up
A single prompt may look like normal use. Intent becomes harder to infer from an individual exchange than from patterns across many requests: unusually high volume, repeated structures, coordination across accounts, or sustained focus on capabilities that could be valuable training targets.
Accounts and proxies can be replaced
If traffic is distributed across accounts and proxy services, blocking one account may not stop the broader activity. Providers therefore need to identify relationships among requests and accounts, not just respond to isolated usage.
Detection and attribution are different from prevention
Spotting a suspicious pattern does not necessarily prevent every response from being collected. Connecting activity across accounts may help a provider investigate or attribute a campaign, but attribution is not itself a barrier to querying. Likewise, identifying a student model after it has been trained does not undo the collection of teacher outputs.
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What defenses do providers use, and what are their limits?
Anthropic says its response includes measures aimed at different stages of the problem. The company’s description is not proof that these measures prevent every attempt.
| Defensive aim | Reported or researched approach | What it can and cannot establish |
|---|---|---|
| Prevention | Anthropic describes stronger verification for certain account pathways and safeguards intended to reduce the training value of outputs. | These measures may raise the effort required or make collected answers less useful; they do not demonstrate universal prevention. |
| Detection | Anthropic describes classifiers, behavioral fingerprinting and analysis of coordinated activity across accounts. | Patterns can help flag suspicious behavior, but a flag is not by itself proof of intent or a guarantee that all activity will be recognized. |
| Attribution | Anthropic describes combining IP correlations, request metadata and infrastructure indicators, sometimes with industry-partner corroboration; it also says providers share information with other organizations. | These are investigative signals and reported practices, not an independent audit of the company’s conclusions. |
| Deterrence or traceability | Researchers have examined watermarking and methods that rewrite teacher-generated reasoning traces. | Experimental techniques may help identify or reduce unauthorized use, but the cited work does not establish a deployed, unbreakable defense. |
A 2025 ACL study by Pan and coauthors reports that, in its experiments, targeted paraphrasing and inference-time watermark neutralization could remove inherited watermarks while preserving useful knowledge transfer. The result is limited to the paper’s methods and experimental conditions; it is a reason not to treat watermarks as an unbreakable lock, not proof that every watermark can always be removed.
A 2026 ACL paper by Xinhang Ma and coauthors investigates rewriting teacher-generated reasoning traces to make outputs less useful for unauthorized distillation while aiming to preserve answer correctness and semantic coherence. Its abstract reports experimental anti-distillation effects and detectable watermarks. This is a research approach, not evidence of a universally proven or widely deployed defense.
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