  With the large-scale integration and use of neural network models, especially
in critical embedded systems, their security assessment to guarantee their
reliability is becoming an urgent need. More particularly, models deployed in
embedded platforms, such as 32-bit microcontrollers, are physically accessible
by adversaries and therefore vulnerable to hardware disturbances. We present
the first set of experiments on the use of two fault injection means,
electromagnetic and laser injections, applied on neural networks models
embedded on a Cortex M4 32-bit microcontroller platform. Contrary to most of
state-of-the-art works dedicated to the alteration of the internal parameters
or input values, our goal is to simulate and experimentally demonstrate the
impact of a specific fault model that is instruction skip. For that purpose, we
assessed several modification attacks on the control flow of a neural network
inference. We reveal integrity threats by targeting several steps in the
inference program of typical convolutional neural network models, which may be
exploited by an attacker to alter the predictions of the target models with
different adversarial goals.
