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| CVE | CVSS | EPSS | Vendor, product | Description | Modified |
|---|---|---|---|---|---|
| CVE-2026-31228 | 9.8 critical | 1.0% | no CPE yet | The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains a remote code execution vulnerability in its Kubeflow component. The robustness evaluation function for PyTorch models uses the unsafe eval() function to dynamically evaluate user-supplied strings for the LossFn and Optimizer parameters without any sanitization or security restrictions. An attacker can exploit this by providing a specially crafted string that contains arbitrary Python code, which will be executed when eval() is called, leading to complete compromise of the system running the ART evaluation. | 19 Oct |
| CVE-2026-31229 | 9.8 critical | 1.0% | no CPE yet | The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains an insecure deserialization vulnerability (CWE-502) in its Kubeflow component's model loading functionality. When loading model weights from a file (e.g., model.pt) during robustness evaluation, the code uses torch.load() without the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the Pickle module. An attacker can exploit this by uploading a maliciously crafted model file to an object storage location referenced by the pipeline, or by controlling the model_id parameter to point to such a file. When the pipeline loads the model, the malicious payload is executed, leading to remote code execution. | 19 Oct |
| CVE-2026-31230 | 9.8 critical | 1.0% | no CPE yet | The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains a command-line argument injection vulnerability in its Kubeflow component (robustness_evaluation_fgsm_pytorch.py). The script uses the unsafe eval() function to parse string values provided via the --clip_values and --input_shape command-line arguments. This allows an attacker to inject arbitrary Python code into these arguments, which will be executed when eval() is called. The vulnerability can be exploited remotely if an attacker can control these arguments (e.g., through pipeline configuration or automated scripts), leading to arbitrary code execution on the system running the ART evaluation. | 19 Oct |
| CVE-2026-47237 | 8.0 high | 0.00% | no CPE yet | 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. | Sat 24 Oct |
| CVE-2026-118761 | 7.5 high | — | kubeflow, pipelines | 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. | 10 h ago |
| CVE-2026-118758 | 7.5 high | — | kubeflow, pipelines | 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. | 10 h ago |
| CVE-2026-118759 | 7.5 high | — | kubeflow, pipelines | 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. | 14 h ago |
| CVE-2026-110220 | 7.1 high | 0.00% | no CPE yet | 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. | Mon 26 Oct |
| CVE-2026-118760 | 6.5 medium | — | kubeflow, pipelines | 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. | 13 h ago |
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EPSS scores: 2026-10-29. NVD cursor: 2026-10-29 00:22. The News page shows only notable CVEs (KEV, your stack, new criticals, EPSS over 50%, or active exploitation per SSVC); this table has all of them.