Machine-Learning Side-Channel Attacks on the GALACTICS Constant-Time Implementation of BLISS

TL;DR

This paper demonstrates machine-learning side-channel attacks on GALACTICS, a constant-time implementation of the post-quantum BLISS signature scheme, revealing vulnerabilities in Gaussian sampling that threaten its security.

Contribution

It introduces three novel machine-learning based side-channel attacks targeting GALACTICS, exposing security risks in its Gaussian sampling process.

Findings

Leakages enable high-accuracy prediction of sensitive data

Successful key recovery attacks demonstrated on Cortex-M4

Highlighting security vulnerabilities in constant-time implementations

Abstract

Due to the advancing development of quantum computers, practical attacks on conventional public-key cryptography may become feasible in the next few decades. To address this risk, post-quantum schemes that are secure against quantum attacks are being developed. Lattice-based algorithms are promising replacements for conventional schemes, with BLISS being one of the earliest post-quantum signature schemes in this family. However, required subroutines such as Gaussian sampling have been demonstrated to be a risk for the security of BLISS, since implementing Gaussian sampling both efficient and secure with respect to physical attacks is highly challenging. This paper presents three related power side-channel attacks on GALACTICS, the latest constant-time implementation of BLISS. All attacks are based on leakages we identified in the Gaussian sampling and signing algorithm of GALACTICS. To run the attack, a profiling phase on a device identical to the device under attack is required to train machine learning classifiers. In the attack phase, the leakages of GALACTICS enable the trained classifiers to predict sensitive internal information with high accuracy, paving the road for three different key recovery attacks. We demonstrate the leakages by running GALACTICS on a Cortex-M4 and provide proof-of-concept data and implementation for all our attacks.