Distillation & model-extraction probing
Probe whether a deployed model can be cheaply queried to reconstruct its behaviour, training data, or a usable distilled copy — a confidentiality and IP attack surface.
Published June 26, 2026
How it works
A model exposed behind an API is also a teacher: an adversary can query it systematically and train a cheaper student on the responses, recovering much of its behaviour without its weights. Related probes test for memorised training data leaking back out verbatim. Treating extraction as a red-team objective — how much capability can be siphoned, how detectable is the harvesting — measures a confidentiality and intellectual-property risk that functional testing never touches.
When to use it
Threat-modelling any model offered as a public or partner API; assessing exposure of proprietary fine-tunes and the data behind them.
Limitations
Realistic extraction is resource-intensive and its feasibility shifts with rate-limiting and output design; a negative result is bounded by the effort you were willing to spend.
Cite this
Qlarify Labs. (2026). Distillation & model-extraction probing. Retrieved from https://labs.qlarify.fi/catalog/model-extraction-probing