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arXiv · 2606.06854

The Geometry of Last-Layer Model Stealing

Snigdha Chandan Khilar
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Abstract

This paper uses geometry to explain how a machine learning model can be stolen using an already existing well-known method. The author has shown the exact conditions required to perfectly copy the final layer of a transformer network. When looking deeper into the hidden layers the author has explained clear limits. The author has also demonstrated that a hidden network cannot be fully reverse engineered just by looking at the final results. The research clearly maps out what can and cannot be stolen from a model.

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Snigdha Chandan Khilar. 2026-06-05. The Geometry of Last-Layer Model Stealing. https://arxiv.org/abs/2606.06854

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