A Simple Transformer Pipeline for Full-Key Side-Channel Attacks on Uncropped Datasets

Jimmy Gammell; Kaushik Roy

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arXiv · 2608.30105

A Simple Transformer Pipeline for Full-Key Side-Channel Attacks on Uncropped Datasets

Jimmy Gammell·Kaushik Roy

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Abstract

Deep learning-based side-channel analysis has historically focused on single-byte targets and manually cropped traces, which risks discarding exploitable leakage. While recent work has proposed specialized architectures and resampling techniques to address this gap, the literature lacks a simple transformer baseline for simultaneous full-key attacks on uncropped traces. We present an open-source transformer implementation for uncropped full-key attacks which uses the standard transformer encoder backbone, adapting only the input and output layers to the side-channel setting. We release our implementation, training recipes, and pretrained weights for uncropped ASCADv1f, ASCADv1r, and CHES-CTF-2018 which achieve performance competitive with previously-reported results, while using less than 10GB of VRAM and requiring at most 3.34 hours of training on a single NVIDIA A6000.

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Jimmy Gammell, Kaushik Roy. 2026-08-31. A Simple Transformer Pipeline for Full-Key Side-Channel Attacks on Uncropped Datasets. https://arxiv.org/abs/2608.30105

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