# Fault Injection on Embedded Neural Networks: Impact of a Single Instruction Skip

arXiv: https://arxiv.org/abs/2308.16665 · HTML: https://mlsanity.com/paper/2308.16665/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://mlsanity.com/paper/1710.01878/) (2017)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://mlsanity.com/paper/1606.06160/) (2016)
- [MCUNet: Tiny Deep Learning on IoT Devices](https://mlsanity.com/paper/2007.10319/) (2020)
- [CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs](https://mlsanity.com/paper/1801.06601/) (2018)
- [Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search](https://mlsanity.com/paper/1903.12269/) (2019)
- [DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain of Bit Flips](https://mlsanity.com/paper/2003.13746/) (2020)
- [DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories](https://mlsanity.com/paper/2111.04625/) (2021)
- [A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters](https://mlsanity.com/paper/2211.05590/) (2022)
- [A Closer Look at Evaluating the Bit-Flip Attack Against Deep Neural Networks](https://mlsanity.com/paper/2209.14243/) (2022)
- [An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration](https://mlsanity.com/paper/2005.03451/) (2020)
- [Fault Attacks on Secure Embedded Software: Threats, Design and Evaluation](https://mlsanity.com/paper/2003.10513/) (2020)

## Built on by (most cited first, 2 of 2)

- [Robustness of deep learning classification to adversarial input on GPUs: asynchronous parallel accumulation is a source of vulnerability](https://mlsanity.com/paper/2503.17173/) (2025)
- [The Weight of a Bit: EMFI Sensitivity Analysis of Embedded Deep Learning Models](https://mlsanity.com/paper/2602.16309/) (2026)

## Abstract (arXiv)

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.