# Scalable GPU-Based Integrity Verification for Large Machine Learning Models

arXiv: https://arxiv.org/abs/2510.23938 · HTML: https://mlsanity.com/paper/2510.23938/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [Poisoning Web-Scale Training Datasets is Practical](https://mlsanity.com/paper/2302.10149/) (2023)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Atlas: A Framework for ML Lifecycle Provenance & Transparency](https://mlsanity.com/paper/2502.19567/) (2025)
- [Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud](https://mlsanity.com/paper/2410.05930/) (2024)
- [Sentry: Authenticating Machine Learning Artifacts on the Fly](https://mlsanity.com/paper/2510.00554/) (2025)
- [Confidential Computing on Heterogeneous CPU-GPU Systems: Survey and Future Directions](https://mlsanity.com/paper/2408.11601/) (2024)
- [GPU Accelerated Keccak (SHA3) Algorithm](https://mlsanity.com/paper/1902.05320/) (2019)
- [Enhancing supply chain security with automated machine learning](https://mlsanity.com/paper/2406.13166/) (2024)
- [Toward Heterogeneous, Distributed, and Energy-Efficient Computing with SYCL](https://mlsanity.com/paper/2505.06022/) (2025)

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

- [One RNG to Rule Them All: How Randomness Becomes an Attack Vector in Machine Learning](https://mlsanity.com/paper/2602.09182/) (2026)

## Abstract (arXiv)

We present a security framework that strengthens distributed machine learning by standardizing integrity protections across CPU and GPU platforms and significantly reducing verification overheads. Our approach co-locates integrity verification directly with large ML model execution on GPU accelerators, resolving the fundamental mismatch between how large ML workloads typically run (primarily on GPUs) and how security verifications traditionally operate (on separate CPU-based processes), delivering both immediate performance benefits and long-term architectural consistency. By performing cryptographic operations natively on GPUs using dedicated compute units (e.g., Intel Arc's XMX units, NVIDIA's Tensor Cores), our solution eliminates the potential architectural bottlenecks that could plague traditional CPU-based verification systems when dealing with large models. This approach leverages the same GPU-based high-memory bandwidth and parallel processing primitives that power ML workloads ensuring integrity checks keep pace with model execution even for massive models exceeding 100GB. This framework establishes a common integrity verification mechanism that works consistently across different GPU vendors and hardware configurations. By anticipating future capabilities for creating secure channels between trusted execution environments and GPU accelerators, we provide a hardware-agnostic foundation that enterprise teams can deploy regardless of their underlying CPU and GPU infrastructures.