Evaluation of Parameter-Based Attacks Against Embedded Neural Networks with Laser Injection

Mathieu Dumont, Kevin Hector, Pierre-Alain Moëllic, Jean-Max Dutertre, Simon Pontié

Abstract: Upcoming certification actions related to the security of machine learning (ML) based systems raise major evaluation challenges that are amplified by the large-scale deployment of models in many hardware platforms. Until recently, most of research works focused on API-based attacks that consider a ML model as a pure algorithmic abstraction. However, new implementation-based threats have been revealed, emphasizing the urgency to propose both practical and simulation-based methods to properly evaluate the robustness of models. A major concern is parameter-based attacks (such as the Bit-Flip Attack - BFA) that highlight the lack of robustness of typical deep neural network models when confronted by accurate and optimal alterations of their internal parameters stored in memory. Setting in a security testing purpose, this work practically reports, for the first time, a successful variant of the BFA on a 32-bit Cortex-M microcontroller using laser fault injection. It is a standard fault injection means for security evaluation, that enables to inject spatially and temporally accurate faults. To avoid unrealistic brute-force strategies, we show how simulations help selecting the most sensitive set of bits from the parameters taking into account the laser fault model.