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

Why it is Yellow

• CVSS 8.6 high
• Remotely exploitable without logging in

References

• https://github.com/kubeflow/pipelines/commit/0b77f9b07bfb9860dc90106003bbc3e2fe0e0965
• https://github.com/kubeflow/pipelines/pull/14349
• https://github.com/kubeflow/pipelines/releases/tag/2.18.0
• https://github.com/kubeflow/pipelines/security/advisories/GHSA-3pcj-8j5w-jmxq

Original description

Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.18.0, the frontend artifact download path for source=s3 or source=minio accepts providerInfo.Params.endpoint, and the archived pod-log path accepts S3-compatible endpoint metadata from workflow status, without requiring those endpoints to match operator-controlled storage configuration. In the shipped authorization-disabled standalone configuration, an unauthenticated caller can make the shared ml-pipeline-ui pod connect to arbitrary reachable hosts and return response data; requests may also reveal an access-key identifier when signed with configured object-store credentials. Multi-user secret-backed metadata remains restricted to the server namespace. This issue is fixed in version 2.18.0.