Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows

Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.16.1, the multi-user API server treats the empty namespace used by shared or global pipelines as an authorization exception for modification and deletion operations. An authenticated user can modify or delete shared pipeline definitions and versions without the required Kubernetes RBAC permissions, compromising their integrity and availability. Read-only access to shared pipelines remains intentional, single-user deployments intentionally bypass multi-user checks, and execution of a modified pipeline is a conditional downstream consequence. The separate shared-pipeline multipart upload authorization path is outside this advisory and is not remediated by version 2.16.1. This issue is fixed in version 2.16.1.