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Search Results (9 CVEs found)
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2026-88983 | 1 Kubeflow | 1 Pipelines | 2026-11-06 | 5 Medium |
| Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.17.0, the API server's pipeline and pipeline-version creation paths fetch a caller-supplied pipelineURL without restricting the destination. An authenticated caller with permission to create a pipeline or pipeline version can make requests from the API server to internal services and observe a limited status-code oracle. Arbitrary response-body disclosure, credential theft, and integrity or availability impact are not established. This issue is fixed in version 2.17.0. | ||||
| CVE-2026-88979 | 1 Kubeflow | 1 Pipelines | 2026-11-06 | 5.8 Medium |
| Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. From 2.17.0 until 2.18.0, the TensorBoard proxy token introduced by the incomplete remediation for CVE-2026-54745 uses MINIO_SECRET_KEY as its HMAC secret, whose bundled default is publicly known. In an authorization-disabled deployment using that default, an unauthenticated attacker can forge a token containing a chosen viewerName and namespace; the proxy constructs the destination from the viewerName plus the -service suffix, the namespace, and the configured cluster domain, then reads responses on port 80 under the /tensorboard/<viewer>/ path. Resource-name validation and path-traversal rejection remain enforced, authorization-enabled deployments still check access to the selected namespace, and unrestricted metadata access, writes, or service disruption are not established. This issue is fixed in version 2.18.0. | ||||
| CVE-2026-88985 | 1 Kubeflow | 1 Pipelines | 2026-11-05 | 6.5 Medium |
| 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. | ||||
| CVE-2026-88986 | 1 Kubeflow | 1 Pipelines | 2026-11-05 | 6.1 Medium |
| Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.15.0, the frontend routes /pipeline/k8s/pod and /pipeline/k8s/pod/events use frontend/server/k8s-helper.ts to include untrusted podname and podnamespace query values and backend error details in HTML-capable error responses. An unauthenticated attacker can craft a link that executes JavaScript in the frontend origin when a user who satisfies the deployment's authentication and route-access requirements follows it and reaches an affected response. Cookie disclosure depends on cookie attributes, and server-side code execution or availability impact is not established. This issue is fixed in version 2.15.0. | ||||
| CVE-2026-88984 | 1 Kubeflow | 1 Pipelines | 2026-11-05 | 8.1 High |
| 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. | ||||
| CVE-2026-88980 | 1 Kubeflow | 1 Pipelines | 2026-11-05 | 8.6 High |
| Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.18.0, the frontend GET /artifacts/get route accepts source=http or source=https with caller-controlled bucket and key values, and isAllowedResourceName permits an all-digit decimal host that the URL parser converts to an IPv4 address. The default ALLOWED_ARTIFACT_DOMAIN_REGEX and authorization-disabled standalone configuration allow an unauthenticated caller to fetch internal HTTP resources and receive their response body. In the authenticated multi-user configuration, the same destination-validation defect is reachable by a low-privilege tenant with a valid identity and authorized namespace. Cloud credential disclosure depends on reachable metadata services and attached roles, and the issue does not establish IMDSv2 bypass or credential access when metadata is blocked. This issue is fixed in version 2.18.0. | ||||
| CVE-2026-88981 | 1 Kubeflow | 1 Pipelines | 2026-11-05 | 8.6 High |
| 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. | ||||
| CVE-2026-77596 | 1 Kubeflow | 1 Pipelines | 2026-11-05 | 9.6 Critical |
| Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.18.0, the multi-user HTTP endpoints POST /apis/v2beta1/pipelines/upload and POST /apis/v2beta1/pipelines/upload_version call canUploadVersionedPipeline, which returns without SubjectAccessReview when the target namespace is empty while REQUIRE_NAMESPACE_FOR_PIPELINES defaults to false. A low-privilege tenant can create shared pipelines or inject attacker-controlled versions into existing shared pipelines without the required write permission; if the API server is directly reachable without an authenticating proxy, an unauthenticated caller can exploit the same bypass, while deployments protected by such a proxy do not expose anonymous access. An enabled unpinned recurring run that follows the latest version can then execute the injected specification in another tenant's namespace under that workload's service account, while pinned or embedded-specification runs are not automatically substituted. This issue is fixed in version 2.18.0. | ||||
| CVE-2026-88982 | 1 Kubeflow | 1 Pipelines | 2026-11-05 | 5.4 Medium |
| Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.18.0, the legacy V1/raw-Argo execution cache mutating webhook and cache-server storage path hash selected template fields without the Kubernetes namespace, use the key alone for lookups and duplicate checks, and trust the mutable pipelines.kubeflow.org/execution_cache_key annotation when publishing outputs. In multi-user deployments where tenant namespaces share the cache service, a tenant with workload-submission privileges can read another namespace's cached outputs or seed matching outputs that are substituted into a later step. Standard KFP V2 component pods bypass this cache, matching requires the selected template fields and staleness policy, and arbitrary code execution or service availability impact is not established. This issue is fixed in version 2.18.0. | ||||
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