machine learning

Trusting AI Models in Kubernetes: Introducing the Sigstore Model Validation Operator

As machine learning becomes deeply embedded in critical infrastructure, the question of trust in deployed models is increasingly critical. How can we be sure that an AI model running in a Kubernetes cluster is exactly what it claims to be? The OpenSSF AI/ML working group believes the answer can be found in signing AI models. A long-standing practice in traditional software distribution is to leverage cryptographic signatures to help end-users verify provenance: that software is authentic, has not been tampered with, and was authored by the expected creator.

Taming the Wild West of ML: Practical Model Signing with Sigstore

Over the past year, in collaboration with OpenSSF, NVIDIA and HiddenLayer, we have worked on bringing Sigstore signatures to the world of machine learning, making ML models tamper resistant via transparent signatures. Today we are pleased to announce the launch of version 1.0 of the model-signing project, built on top of sigstore-python. After installing via pip install model-signing, users can use the CLI to sign and verify models, as per the following examples: