# Sentry: Authenticating Machine Learning Artifacts on the Fly

arXiv: https://arxiv.org/abs/2510.00554 · HTML: https://mlsanity.com/paper/2510.00554/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2025.

## Built on by (most cited first, 3 of 3)

- [Scalable GPU-Based Integrity Verification for Large Machine Learning Models](https://mlsanity.com/paper/2510.23938/) (2025)
- [One RNG to Rule Them All: How Randomness Becomes an Attack Vector in Machine Learning](https://mlsanity.com/paper/2602.09182/) (2026)
- [BadEngram: Backdoor Attack on Gated Memory Components in LLMs](https://mlsanity.com/paper/2609.13478/) (2026)

## Abstract (arXiv)

Machine learning systems increasingly rely on open-source artifacts such as datasets and models that are created or hosted by other parties. The reliance on external datasets and pre-trained models exposes the system to supply chain attacks where an artifact can be poisoned before it is delivered to the end-user. Such attacks are possible due to the lack of any authenticity verification in existing machine learning systems. Incorporating cryptographic solutions such as hashing and signing can mitigate the risk of supply chain attacks. However, existing frameworks for integrity verification based on cryptographic techniques can incur significant overhead when applied to state-of-the-art machine learning artifacts due to their scale, and are not compatible with GPU platforms. In this paper, we develop Sentry, a novel GPU-based framework that verifies the authenticity of machine learning artifacts by implementing cryptographic signing and verification for datasets and models. Sentry ties developer identities to signatures and performs authentication on the fly as artifacts are loaded on GPU memory, making it compatible with GPU data movement solutions such as NVIDIA GPUDirect that bypass the CPU. Sentry incorporates GPU acceleration of cryptographic hash constructions such as Merkle tree and lattice hashing, implementing memory optimizations and resource partitioning schemes for a high throughput performance. Our evaluations show that Sentry is a practical solution to bring authenticity to machine learning systems, achieving orders of magnitude speedup over a CPU-based baseline.