In side-channel analysis of ML-KEM (a NIST-standard PQC algorithm), SASCA is a powerful profiling attack. However, obtaining a profiling device strictly matching the target is challenging in practice. To address this, we propose the first profiling-device-free SASCA framework for ML-KEM. The framework first controls the NTT input by choosing ciphertexts and trains a leakage model. Subsequently, leveraging the similarity between NTT and INTT, it uses adversarial unsupervised domain adaptation to fine-tune the model for INTT and recover its secret input. Validated on real embedded devices, the framework achieves effective key recovery using a comparable number of traces to profiling SASCA.
BibTeX
@misc{cryptoeprint:2026/981,
author = {Yuxuan Wang},
title = {Profiling-Device-Free {SASCA} Framework for {ML}-{KEM}},
howpublished = {Cryptology {ePrint} Archive, Paper 2026/981},
year = {2026},
url = {https://eprint.iacr.org/2026/981}
}
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