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

Why it is Yellow

• CVSS 7.5 high
• Remotely exploitable without logging in

References

• https://github.com/kubeflow/pipelines/commit/1121d9a081fdf9e2107c39abb2cd8947727c6953
• https://github.com/kubeflow/pipelines/commit/ba9ae72cc860e3afbea232185e4132fc2896cac5
• https://github.com/kubeflow/pipelines/pull/14171
• https://github.com/kubeflow/pipelines/pull/14186
• https://github.com/kubeflow/pipelines/releases/tag/2.18.0
• https://github.com/kubeflow/pipelines/security/advisories/GHSA-8fmv-6rg5-5924

Original description

Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.18.0, the API server decompressed uploaded pipeline archives without adequately bounding expanded data while locating or reading the pipeline file. A small ZIP or gzip-compressed tar archive can consume disproportionately large memory and exhaust API-server resources. Multipart pipeline and pipeline-version upload processing performs decompression before namespace authorization, so a reachable caller does not need a successful pipeline-create authorization decision. URL-import handlers authorize before fetching and decompressing a remote archive. The amount of expansion required to interrupt service depends on deployment resource limits and concurrent activity, and no specific archive is guaranteed to crash every deployment. This issue is fixed in version 2.18.0.