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Search Results (20 CVEs found)
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| 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 | N/A |
| 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. | ||||
| CVE-2026-88983 | 1 Kubeflow | 1 Pipelines | 2026-11-05 | 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 | 2026-11-05 | 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-2026-118760 | 1 Kubeflow | 1 Pipelines | 2026-11-03 | 6.5 Medium |
| Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. From 0.5.1 until 2.17.0, the frontend artifact routes did not check whether an authenticated user was authorized to access the requested namespace. A user who can reach the frontend and knows an artifact location can select another tenant's namespace, or omit the namespace and reach an unguarded direct handler, to read artifact contents such as datasets, models, or pipeline outputs. The issue applies to multi-user deployments that rely on Kubeflow Pipelines authorization for tenant isolation. Authentication at ingress identifies the caller but does not authorize access to the selected artifact namespace. The issue does not establish arbitrary Kubernetes Secret disclosure, storage modification, or server-side code execution. This issue is fixed in version 2.17.0. | ||||
| CVE-2026-118759 | 1 Kubeflow | 1 Pipelines | 2026-10-30 | 7.5 High |
| 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. | ||||
| CVE-2026-118761 | 1 Kubeflow | 1 Pipelines | 2026-10-30 | 7.5 High |
| 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. | ||||
| CVE-2026-118758 | 1 Kubeflow | 1 Pipelines | 2026-10-29 | 7.5 High |
| Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.18.0, the volume artifact handler for GET /artifacts/get?source=volume did not confine a caller-supplied file path to the selected volume. A caller who can reach the route and identify an existing mounted volume can use path traversal to read files outside that volume when the artifact-serving process can read them. The frontend pod serves the request when artifact proxying is disabled, while the selected namespace's artifact-service pod serves it when proxying is enabled. A deployment with authentication disabled requires no application credentials; in multi-user deployments, the caller ordinarily must be authorized for the supplied namespace. This issue is fixed in version 2.18.0. | ||||
| CVE-2026-110220 | 1 Kubeflow | 1 Model Registry | 2026-10-26 | 7.1 High |
| Kubeflow Model Registry through 0.3.17 contains a server-side request forgery vulnerability that allows namespace users to steal the controller ServiceAccount token via the modelregistry.kubeflow.org/url annotation. Attackers can create an InferenceService pointing the annotation at their server, capture the bearer token, and modify other tenants' InferenceServices cluster-wide. | ||||
| CVE-2026-54745 | 1 Kubeflow | 1 Pipelines | 2026-08-31 | 10 Critical |
| Kubeflow Pipelines enables users to build and deploy portable, scalable machine learning workflows. Prior to 2.17.0, the Kubeflow Pipelines frontend exposes an unauthenticated server-side request forgery vulnerability through the /_proxy/ route in frontend/server/proxy-middleware.ts. The _routePathWithReferer() function accepts an arbitrary attacker-controlled HTTP or HTTPS target and passes its origin to createProxyMiddleware without a host allowlist or filtering for loopback, link-local, RFC1918, or cluster-local addresses. The route remains outside the authorization middleware when ENABLE_AUTHZ=true and is reachable through /apis/v1beta1/_proxy/, /apis/v2beta1/_proxy/, /pipeline/apis/v1beta1/_proxy/, and /pipeline/apis/v2beta1/_proxy/, including through a crafted Referer header. Requests can forward attacker-controlled methods, headers such as Authorization, Cookie, and X-Forwarded-For, and POST bodies to reachable internal services, while returning the upstream response to the unauthenticated client. This can expose cloud metadata credentials, Kubernetes or service APIs, and other cluster-internal endpoints to unauthorized read or modification. This issue is fixed in version 2.17.0. | ||||
| CVE-2026-47237 | 1 Kubeflow | 1 Community-distribution | 2026-07-23 | 8 High |
| Kubeflow Community Distribution helps users to install Kubeflow Platform in popular Kubernetes clusters. Prior to version 26.03-rc.1, a Kubeflow setup based on the official manifests or most other packaged Kubeflow distributions is vulnerable to authorization token stealing from any user of the Kubeflow UI or APIs, such as the Dashboard, Pipelines API, or Notebooks. With this token, the attacker can take over the user's account and the data that is processed by that user. The attacker needs a valid user with the ``kubeflow-edit`` role / Contributor role in a random Kubeflow namespace to perform this attack. This is given if _Automatic Profile Creation_ is enabled. Version 26.03-rc.1 fixes the issue. | ||||
| CVE-2024-9526 | 1 Kubeflow | 2 Kubeflow W Pipeline View, Pipelines | 2025-07-23 | 5.4 Medium |
| There exists a stored XSS Vulnerability in Kubeflow Pipeline View web UI. The Kubeflow Web UI allows to create new pipelines. When creating a new pipeline, it is possible to add a description. The description field allows html tags, which are not filtered properly. Leading to a stored XSS. We recommend upgrading past commit 930c35f1c543998e60e8d648ce93185c9b5dbe8d | ||||
| CVE-2024-5552 | 1 Kubeflow | 1 Kubeflow | 2024-11-21 | 7.5 High |
| kubeflow/kubeflow is vulnerable to a Regular Expression Denial of Service (ReDoS) attack due to inefficient regular expression complexity in its email validation mechanism. An attacker can remotely exploit this vulnerability without authentication by providing specially crafted input that causes the application to consume an excessive amount of CPU resources. This vulnerability affects the latest version of kubeflow/kubeflow, specifically within the centraldashboard-angular backend component. The impact of exploiting this vulnerability includes resource exhaustion, and service disruption. | ||||
| CVE-2023-6571 | 1 Kubeflow | 1 Kubeflow | 2024-11-21 | 6.1 Medium |
| Cross-site Scripting (XSS) - Reflected in kubeflow/kubeflow | ||||
| CVE-2023-6570 | 1 Kubeflow | 1 Kubeflow | 2024-11-21 | 6.5 Medium |
| Server-Side Request Forgery (SSRF) in kubeflow/kubeflow | ||||
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