  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.
