Accelerating End-to-End Cryptanalytic Extraction via Geometric Critical Point Screening

Cryptanalytic model extraction recovers neural-network parameters from prediction queries, exposing information that model owners seek to protect in prediction systems. Layerwise methods often spend many queries on critical points from unrelated layers, while parameter errors propagate into subsequent recovery stages. To address these bottlenecks, we propose Geometric Critical Point Screening (GCPS) within an end-to-end framework for layerwise parameter recovery. GCPS checks geometric relations among critical points before measuring shallow-layer signatures, reducing the number of candidates requiring costly directional measurements. Numerical refinement and coordinate alignment prepare these shallow estimates for subsequent layers, where the full-prefix Jacobian identifies reachable measurement directions. Guarded low-dimensional measurements reduce directional queries, while native input-space repair, cross-region clustering, and completion handle unreliable checks and partial observations. Experiments compare independent true-prefix recovery and end-to-end extraction on a public FP64 $784-8^{(8)}-1$ ReLU checkpoint, supplemented by shallow MNIST and CIFAR comparisons. Both protocols use truth-assisted signs and native auxiliary truth, with end-to-end costs summed over selected completed or resumed stages. Relative to the reference implementation, our end-to-end procedure achieves 1.94× query and 2.00× recorded layer-time speedups under this accounting. Both end-to-end methods verify 6593 of 6643 effective weights. Independent true-prefix recovery achieves a 1.85× query speedup and verifies 6607 effective weights, compared with 6603 for the reference under the same evaluation.