Polynomial Time Cryptanalytic Extraction of Deep Neural Networks in the Hard-Label Setting (Extended Version)

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Deep neural networks (DNNs) are valuable assets, yet black-box access to them raises security concerns about parameter extraction by malicious adversaries. Recent work by Carlini et al. (Crypto’20) and Canales-Martínez et al. (Eurocrypt’24) drew a parallel between model extraction and block-cipher key recovery via chosen-plaintext attacks. Using tools from differential cryptanalysis, they showed that all weights and biases of black-box ReLU-based DNNs can be recovered with a polynomial number of