Copyright © 2012 Red Hat, Inc. All rights reserved. Moderate 2026-11-05T22:06:15 github.com/kubeflow/pipelines: Kubeflow Pipelines: Information disclosure via SQL injection in pagination token handling 6.5 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N CWE-89

Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.17.0, the API server trusts structural SQL identifier fields decoded from the base64-encoded JSON page_token parameter before constructing list queries. An attacker who can invoke an affected pipeline or run list API can inject through KeyFieldName or SortByFieldName, including v1beta1 and v2beta1 run-listing metric paths, and read data available to the API server database account across namespaces. Database modification is not established. This issue is fixed in version 2.17.0.

A flaw was found in Kubeflow Pipelines, which can lead to information disclosure. An authenticated remote attacker with permissions to list pipelines or runs can exploit this vulnerability by submitting a crafted pagination token containing manipulated database query fields. Because the API server trusts these decoded parameters when constructing queries without proper validation, an attacker can perform Structured Query Language (SQL) injection to read sensitive data across namespaces accessible to the database account.

This vulnerability is rated as a Moderate severity flaw because exploitation requires authenticated access to the pipeline API endpoints and only permits unauthorized data reading, without enabling data alteration or system compromise. In environments utilizing Kubeflow Pipelines, such as Red Hat OpenShift AI, authenticated users permitted to query pipeline and run listing endpoints can manipulate pagination tokens to read database records across namespaces. While this breaks multi-tenant data boundaries within the application, the blast radius is restricted to the permissions of the pipeline database account. Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. Red Hat OpenShift AI (RHOAI) Under investigation rhoai/odh-ml-pipelines-api-server-v2-rhel9 Red Hat OpenShift AI (RHOAI) Under investigation rhoai/odh-ml-pipelines-driver-rhel9 Red Hat OpenShift AI (RHOAI) Under investigation rhoai/odh-ml-pipelines-launcher-rhel9 Red Hat OpenShift AI (RHOAI) Under investigation rhoai/odh-ml-pipelines-persistenceagent-v2-rhel9 Red Hat OpenShift AI (RHOAI) Under investigation rhoai/odh-ml-pipelines-scheduledworkflow-v2-rhel9 https://www.cve.org/CVERecord?id=CVE-2026-88985 https://nvd.nist.gov/vuln/detail/CVE-2026-88985 https://github.com/kubeflow/pipelines/commit/ac3a4c65677a3bc7a3d5b1e264fba41178d4ab32 https://github.com/kubeflow/pipelines/pull/13127 https://github.com/kubeflow/pipelines/releases/tag/2.17.0 https://github.com/kubeflow/pipelines/security/advisories/GHSA-r4mx-2gr2-8277