# Attack on a PUF-based Secure Binary Neural Network

arXiv: https://arxiv.org/abs/2510.24422 · HTML: https://mlsanity.com/paper/2510.24422/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](https://mlsanity.com/paper/1612.07119/) (2016)
- [Optical Adversarial Attack](https://mlsanity.com/paper/2108.06247/) (2021)
- [ChewBaccaNN: A Flexible 223 TOPS/W BNN Accelerator](https://mlsanity.com/paper/2005.07137/) (2020)
- [Device-Bind Key-Storageless Hardware AI Model IP Protection: A PUF and Permute-Diffusion Encryption-Enabled Approach](https://mlsanity.com/paper/2212.11133/) (2022)
- [Comprehensive Benchmarking of Binary Neural Networks on NVM Crossbar Architectures](https://mlsanity.com/paper/2308.06227/) (2023)

## Abstract (arXiv)

Binarized Neural Networks (BNNs) deployed on memristive crossbar arrays provide energy-efficient solutions for edge computing but are susceptible to physical attacks due to memristor nonvolatility. Recently, Rajendran et al. (IEEE Embedded Systems Letter 2025) proposed a Physical Unclonable Function (PUF)-based scheme to secure BNNs against theft attacks. Specifically, the weight and bias matrices of the BNN layers were secured by swapping columns based on device's PUF key bits. In this paper, we demonstrate that this scheme to secure BNNs is vulnerable to PUF-key recovery attack. As a consequence of our attack, we recover the secret weight and bias matrices of the BNN. Our approach is motivated by differential cryptanalysis and reconstructs the PUF key bit-by-bit by observing the change in model accuracy, and eventually recovering the BNN model parameters. Evaluated on a BNN trained on the MNIST dataset, our attack could recover 85% of the PUF key, and recover the BNN model up to 93% classification accuracy compared to the original model's 96% accuracy. Our attack is very efficient and it takes a couple of minutes to recovery the PUF key and the model parameters.