# Securing generative artificial intelligence with parallel magnetic tunnel junction true randomness

arXiv: https://arxiv.org/abs/2510.01598 · HTML: https://mlsanity.com/paper/2510.01598/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 17 of 17)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [A Note on the Inception Score](https://mlsanity.com/paper/1801.01973/) (2018)
- [Energy-based Generative Adversarial Network](https://mlsanity.com/paper/1609.03126/) (2016)
- [Reflected Diffusion Models](https://mlsanity.com/paper/2304.04740/) (2023)
- [Exploiting Novel GPT-4 APIs](https://mlsanity.com/paper/2312.14302/) (2023)
- [Adversarial Attacks Against Deep Generative Models on Data: A Survey](https://mlsanity.com/paper/2112.00247/) (2021)
- [Jailbreak Antidote: Runtime Safety-Utility Balance via Sparse Representation Adjustment in Large Language Models](https://mlsanity.com/paper/2410.02298/) (2024)
- [CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning](https://mlsanity.com/paper/2304.05949/) (2023)
- [Machine Learning needs Better Randomness Standards: Randomised Smoothing and PRNG-based attacks](https://mlsanity.com/paper/2306.14043/) (2023)
- [A Guideline on Pseudorandom Number Generation (PRNG) in the IoT](https://mlsanity.com/paper/2007.11839/) (2020)
- [StyleGenes: Discrete and Efficient Latent Distributions for GANs](https://mlsanity.com/paper/2305.00599/) (2023)
- [Voltage-Controlled Magnetoelectric Devices for Neuromorphic Diffusion Process](https://mlsanity.com/paper/2407.12261/) (2024)

## Built on by (most cited first, 1 of 1)

- [Image Synthesis Using Spintronic Deep Convolutional Generative Adversarial Network](https://mlsanity.com/paper/2601.01441/) (2026)

## Abstract (arXiv)

Deterministic pseudo random number generators (PRNGs) used in generative artificial intelligence (GAI) models produce predictable patterns vulnerable to exploitation by attackers. Conventional defences against the vulnerabilities often come with significant energy and latency overhead. Here, we embed hardware-generated true random bits from spin-transfer torque magnetic tunnel junctions (STT-MTJs) to address the challenges. A highly parallel, FPGA-assisted prototype computing system delivers megabit-per-second true random numbers, passing NIST randomness tests after in-situ operations with minimal overhead. Integrating the hardware random bits into a generative adversarial network (GAN) trained on CIFAR-10 reduces insecure outputs by up to 18.6 times compared to the low-quality random number generators (RNG) baseline. With nanosecond switching speed, high energy efficiency, and established scalability, our STT-MTJ-based system holds the potential to scale beyond 106 parallel cells, achieving gigabit-per-second throughput suitable for large language model sampling. This advancement highlights spintronic RNGs as practical security components for next-generation GAI systems.