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| CVE | CVSS | EPSS | Vendor, product | Description | Modified |
|---|---|---|---|---|---|
| CVE-2026-115886 | 6.5 medium | 1.0% | no CPE yet | MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0rc0 until 3.17.0, the scorer invoke, register, and GenAI evaluate endpoints, including unauthenticated POST /ajax-api/3.0/mlflow/scorer/invoke, accept serialized third-party scorer data that Scorer.model_validate_json resolves using a module-path allowlist without constraining the selected class or its constructor arguments. When optional trulens and trulens-providers-litellm packages are installed, an attacker can select mlflow.genai.scorers.trulens, choose a model URI outside MLflow's native provider registry so execution uses the LiteLLM fallback, and supply api_base and api_key values that cause the server to send an outbound request to an attacker-chosen or internal host with attacker-controlled headers and body. The vulnerable deserialization occurs before a tracking-store lookup or job submission. This issue is fixed in version 3.17.0. | 19 h ago |
| 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. | 17 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. | 17 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. | 21 h ago |
| 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. | 21 h ago |
| CVE-2026-118092 | 4.3 medium | — | stylemixthemes, masterstudy lms | Missing Authorization vulnerability in StylemixThemes MasterStudy LMS masterstudy-lms-learning-management-system allows Exploiting Incorrectly Configured Access Control Security Levels.This issue affects MasterStudy LMS: from n/a through 3.7.55. | 20 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.