Curvature Cryptanalysis of Smooth Transformer Feed-Forward Networks
We show that smooth two-layer feed-forward networks (FFNs) expose an additional structural model extraction channel under a chosen-input raw-output oracle at the FFN branch; consider transformer FFN branches with GELU or SiLU activations under chosen-input raw-output access, without access to parameters, gradients, or internal activations; exploit a second-order leakage channel in which projected input Hessians form different mixtures of the same hidden symmetric rank-one factors induced by the FFN input weights. We formalize resulting Hessian collection as a partially symmetric decomposition to establish conditions for local identifiability and stability to exploit vector-output stencil ...
| Authors | Munawar Hasan, Apostol Vassilev |
|---|---|
| Primary category | cs.LG |
| Published | 28 August 2026 |
| Last updated at source | 16 September 2026 |
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