Model Extraction
also: model stealing
Querying a model enough times to train a cheap substitute that mimics its behavior — effectively stealing it without the weights.
A model extraction attack systematically queries a target model (often via its public API) and uses the input-output pairs to train a substitute model that approximates its behavior — no access to weights required. It’s a real threat to any team monetizing a proprietary model through an API, since the API surface that makes a model useful is the same surface that makes it queryable at scale.