CVE-2026-118759

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{"bugzilla": {"description": "github.com/kubeflow/pipelines: Kubeflow Pipelines: Information disclosure via unauthorized log retrieval handler", "id": "2563530", "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2563530"}, "csaw": false, "cvss3": {"cvss3_base_score": "7.5", "cvss3_scoring_vector": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N", "status": "draft"}, "cwe": "CWE-639", "details": ["Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.18.0, the API server's GET /apis/v1alpha1/runs/{run_id}/nodes/{node_id}/log handler retrieved logs without authorizing the caller against the selected run. A caller who can reach the backend and knows a victim run ID and suitable pod or node identifier can read live or archived logs with the API server's permissions. For live logs, the caller-controlled node ID was also accepted as a pod name without verifying that the pod belonged to the requested run, allowing a known run ID to select the namespace of an unrelated pod and read its main container logs. The handler does not authenticate requests itself, although an external authentication gate or network policy can limit reachability. Exposed logs can contain sensitive workload data or credentials. This issue is fixed in version 2.18.0.", "A flaw was found in Kubeflow Pipelines. An unauthenticated remote attacker with access to the API server can retrieve execution logs without proper authorization checks. By submitting requests with known workflow run and pod identifiers, an attacker can access live or archived logs from targeted and unrelated containers. This vulnerability results in information disclosure, potentially exposing sensitive workload data and credentials."], "mitigation": {"lang": "en:us", "value": "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."}, "name": "CVE-2026-118759", "package_state": [{"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-ml-pipelines-api-server-v2-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-ml-pipelines-driver-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-ml-pipelines-launcher-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-ml-pipelines-persistenceagent-v2-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}, {"cpe": "cpe:/a:redhat:openshift_ai", "fix_state": "Affected", "package_name": "rhoai/odh-ml-pipelines-scheduledworkflow-v2-rhel9", "product_name": "Red Hat OpenShift AI (RHOAI)"}], "public_date": "2026-10-29T16:30:25Z", "references": ["https://www.cve.org/CVERecord?id=CVE-2026-118759\nhttps://nvd.nist.gov/vuln/detail/CVE-2026-118759\nhttps://github.com/kubeflow/pipelines/commit/be4723e33727c6dad71a95cba61e8f61562ab20f\nhttps://github.com/kubeflow/pipelines/pull/13684\nhttps://github.com/kubeflow/pipelines/releases/tag/2.18.0\nhttps://github.com/kubeflow/pipelines/security/advisories/GHSA-wg5q-x4j9-qm54"], "statement": "Red Hat rates this vulnerability as Important because an unauthorized user with cluster network access can bypass access control checks to inspect sensitive pipeline logs and environment secrets across workloads. While successful exploitation requires reaching the internal pipeline service and obtaining valid execution identifiers, the backend daemon executes log retrieval utilizing its own elevated cluster privileges without validating caller ownership. In Red Hat OpenShift AI environments, machine learning pipelines regularly handle proprietary datasets and authentication credentials, presenting a significant confidentiality risk.", "threat_severity": "Important"}

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