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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Model distillation is a way to train a student model from a teacher; model extraction is an attempt to learn or reproduce information about a target model. They can both involve copying a model’s outputs, but they differ in purpose, authorization, and what is being reproduced. Distillation can be a legitimate deployment technique; extraction is an adversarial objective when used to obtain a model’s protected information or functionality without authorization.
How distillation and extraction differ
| Question | Knowledge distillation | Model extraction |
|---|---|---|
| What is it? | A training technique in which a student learns from a teacher model or ensemble. | An attack objective: learn information about a target model, potentially by querying it. |
| What is the usual aim? | Represent useful teacher behavior in a student that is easier to deploy. | Reproduce model functionality or obtain information such as architecture or parameters. |
| What can the process involve? | Training the student on information supplied by the teacher, including its predictions. | Queries to a model service, adaptive query selection, algebraic analysis, or side channels. |
| Does it necessarily reproduce the same thing? | No. Distillation trains a student; it does not imply an exact copy of the teacher’s weights. | No. Functional imitation may be the practical goal; exact parameter recovery is not a necessary or general outcome. |
| Does the label alone settle whether it is authorized? | No. The source of the teacher’s outputs and the permissions governing their use matter. | No. Whether a particular activity violates a contract or law depends on its facts and jurisdiction. |
The terms can overlap in technique: an attacker may use teacher outputs to train a substitute, a process resembling distillation. The purpose and permission distinguish legitimate training from an attempt to copy a protected service. Neither “distillation” nor “extraction” by itself proves that exact weights were transferred.
How knowledge distillation works
Teacher to student
A teacher model, or an ensemble of models, supplies information used to train a student. The student is optimized to reproduce useful behavior without requiring the deployed service to run the whole ensemble. In Distilling the Knowledge in a Neural Network (2015), Geoffrey Hinton, Oriol Vinyals, and Jeff Dean describe this as a compression approach motivated by the expense and operational complexity of making predictions with an ensemble. Their paper reports experiments on MNIST and an acoustic model.
The important practical point is the workflow: train a student using a teacher’s outputs or other teacher-provided information, then deploy the student for inference. Distillation is not automatically successful, smaller in every implementation, or authorized simply because it is called distillation. Those outcomes depend on the training setup and the rights and access governing the teacher.
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Not the same as defensive distillation
Knowledge distillation is commonly discussed as a compression and training method. “Defensive distillation” refers to a separate proposal to make neural networks less susceptible to adversarial examples. It is not a general guarantee against model extraction, and the two uses of “distillation” should not be conflated.
How model extraction attacks work
NIST’s March 2025 Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations treats model extraction as a model-privacy attack: an adversary seeks information about a model, including its architecture or parameters, and may submit queries to a machine-learning service to do so. The attack does not have to recover exact weights. A functionally similar substitute can be valuable even if its internal structure differs from the target.
Query-driven learning
An attacker sends inputs to an exposed prediction interface and uses the returned outputs to guide a substitute model’s training. Query strategies can be chosen to use the available budget efficiently. Active learning selects informative examples; reinforcement learning can adapt which queries to make as results arrive. The amount and type of information returned by the service shape what can be learned.
Algebraic recovery
Some extraction approaches exploit the mathematical form of operations in particular neural networks to infer model information directly. Such methods depend on the architecture and conditions; they are not a universal recipe for recovering parameters from any model.
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Extraction may also use signals beyond ordinary predictions. NIST’s taxonomy describes research involving electromagnetic and hardware-fault channels. These differ from API-query attacks: they depend on access to, or observable behavior from, relevant hardware or execution environments.
Representations and embeddings
A service that returns embeddings or other internal representations exposes a different surface from one that returns only a final class label. In a peer-reviewed 2022 study, Dziedzic and colleagues examined extraction from self-supervised models using stolen representations. They reported query-efficient attacks and found that existing defenses were inadequate or not easily adapted to that setting. This is evidence about the studied representation-based setup, not proof that every embedding endpoint can be extracted in the same way.
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What an attacker may be trying to copy
“Extraction” can refer to different targets, and those targets have different security and privacy implications. A 2025 survey by Zhao and colleagues on large language model (LLM) extraction groups attacks into functionality extraction, training-data extraction, and prompt-targeted attacks.
- Functionality: Reproduce useful input-output behavior, potentially by training a substitute from API responses. This need not reveal the target’s original weights.
- Architecture or parameters: Infer structural or parameter information about the target. Exact recovery is a more specific aim than functional imitation and is not guaranteed.
- System prompts: Use prompt-targeted attacks to obtain hidden instructions or other prompt content. This is different from copying the model’s general capabilities.
- Training examples: Attempt to elicit or infer private records used in training. This is a data-privacy concern, not simply another name for copying model behavior.
Related privacy attacks also have distinct aims: membership inference asks whether a particular record was in the training set; data reconstruction or inversion seeks record content; and property inference seeks information about characteristics of the training distribution. Naming the target precisely avoids treating every model- or data-related attack as “model extraction.”
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Risks and limits of what the evidence establishes
Model confidentiality and competition
A successful substitute may let a competitor or attacker reproduce useful functionality without access to the original parameters. NIST also notes that extraction can provide knowledge that makes subsequent attacks easier when they have white-box or gray-box access. These are reasons to protect exposed model interfaces and sensitive model information. Whether a particular activity breaches a contract, infringes rights, or violates trade-secret or other law depends on the facts and jurisdiction; technical descriptions alone do not decide that question.
Model extraction is not a prevalence statistic
The cited taxonomy, studies, and survey describe attack methods and risks, but do not establish a market-wide rate of extraction or misuse. A technique’s existence is not evidence of how often it succeeds in deployed services.
A bounded result about defensive distillation
In a 2016 MNIST experiment, Nicholas Carlini and David Wagner reported 96.4% targeted-misclassification success while changing an average of 4.7% of pixels against defensively distilled networks. That result shows that defensive distillation was not a sufficient adversarial-example defense in that evaluated setup. It is not an extraction success rate, nor an estimate of the performance of present-day models or defenses.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to reduce extraction risk
No single mitigation is established as a guarantee across architectures, interfaces, and attacker capabilities. Choose controls according to what the service exposes and evaluate their effect on both attackers and legitimate users.
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Expose only what the application needs
- Decide whether clients need probabilities, embeddings, detailed intermediate outputs, or only a final answer.
- Return the least revealing output that still supports the product’s legitimate use.
- Treat limiting outputs as risk reduction, not proof that extraction is impossible.
Control and observe query access
- Apply authentication and authorization appropriate to the service and its users.
- Use rate controls and monitoring to make large-scale or adaptive probing harder to conduct unnoticed.
- Investigate repeated or unusual query patterns in context; high volume alone does not prove malicious intent.
These controls address the query access that features prominently in NIST’s extraction taxonomy. Their value depends on implementation and threat conditions; they do not establish that a determined attacker cannot learn anything.
Secure representation interfaces separately
Do not assume a defense designed for label or probability outputs transfers to an embedding endpoint. The self-supervised-learning study by Dziedzic and colleagues highlights that stolen representations can support query-efficient extraction and that defenses may not retrofit easily. Assess the representation itself as an exposed capability, and test mitigations against attacks suited to that interface.
Use differential privacy for training-data privacy, not model secrecy
Differential privacy (DP) can be appropriate when the concern is information about training records and a formal privacy guarantee is needed. Its privacy parameters and utility costs require careful accounting. NIST explicitly distinguishes that protection from model confidentiality: DP protects training data and does not itself guarantee protection against model extraction.
Evaluate adaptive attacks and service impact
Test defenses against attackers who can adjust queries based on prior responses. For generative models, include prompt-targeted and functionality-copying scenarios as well as data-privacy risks; the 2025 LLM survey organizes defenses across model protection, data privacy, and prompt-targeted strategies and emphasizes evaluation suited to generative systems.
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Before deciding that an interface or mitigation is adequate, document the threat model and test the trade-offs:
- Authorization: Who may use the model, and what do the applicable permissions allow them to do with its outputs?
- Interface: Does the service expose labels, confidence values, embeddings, prompts, intermediate outputs, or only final responses?
- Attacker access: Can an attacker make queries, observe hardware behavior, or obtain other access relevant to the proposed method?
- Query budget: How many queries can be made, over what period, and can the attacker adapt them to earlier responses?
- Target and fidelity: Is the concern functional imitation, architecture or parameter recovery, prompt disclosure, or training-record leakage? Define what counts as a meaningful reproduction.
- Attacker cost: What expertise, computation, access, and time would an attack require under the tested conditions?
- Mitigation performance: Does the defense reduce the relevant extraction capability under adaptive testing, rather than merely limiting one fixed attack?
- Legitimate-user impact: How do reduced outputs, rate controls, or other restrictions affect valid users and the service’s utility?
Keep extraction tests and privacy tests separate where their targets differ. A defense that reduces model copying does not automatically protect training records, and a training-data privacy guarantee does not automatically prevent model copying.
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