ModelRefs / Model Stealing — AI Glossary
Model Stealing — AI Glossary
An attack extracting a proprietary model's functionality by querying its API and training a surrogate model on the query-response pairs.
Overview
Model stealing (Tramèr et al. 2016) reconstructs a model's decision boundaries or capabilities by querying the target API at scale and distilling responses into a local model. Concerns: IP theft, circumventing rate limits/safety, and extracting training data signals. Defenses: rate limiting, output perturbation, and watermarking.
Reference details
| Topic | safety |
|---|---|
| Also known as | model extraction |
| Last reviewed | 2026-06-24 |
Related terms
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Model Stealing — AI Glossary.
Frequently asked questions
What is Model Stealing?
An attack extracting a proprietary model's functionality by querying its API and training a surrogate model on the query-response pairs.
Is Model Stealing the same as model extraction?
Yes — model extraction are common aliases for Model Stealing.
What concepts are related to Model Stealing?
Closely related concepts include adversarial attack, llm security, knowledge distillation.