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7 CVE

Critique 3 Élevée 3 Moyenne 1 Faible 0 Non évaluée 0 Exploitées (KEV) Tri : plus récentes « Apache spark » ✕
  1. CVE-2026-32773 6.1 Moyenne

    Apache Software Foundation Apache Spark · Apache spark

    There is a lack of XSS escaping in the Spark History Server prior to 3.5.8 which allows a malicious Spark job to generate arbitrary unescaped frontend code which could lead to a minimal privilege escalation in browser. Users are encouraged to upgrade to Spark 3.5.8 or later. This CVE is marked as "low" since the path to exploit requires both relatively high permissions (ability to launch a Spark job) and requires tricking a user with higher permissions to log in and visit the Spark history web page. Users are encouraged to upgrade their Spark history servers to Spark 3.5.8 or later.

  2. CVE-2026-18428 8.8 Élevée

    AWS Opensearch · Github Opensearch

    A SQL query validation bypass in the Flint extension query handler in the OpenSearch SQL plugin allows a remote authenticated actor with async query access to execute arbitrary code on Apache Spark workers by sending a crafted SQL query to the direct query endpoint.

  3. CVE-2026-62391 8.1 Élevée

    Apache Software Foundation Apache Kyuubi · Apache kyuubi

    The security fix for CVE-2025-66518 is incomplete. Any client who can access to Apache Kyuubi Server via Kyuubi frontend protocols can bypass server-side config kyuubi.session.local.dir.allowlist via unprefixed Spark config aliases. This issue affects Apache Kyuubi: from 1.6.0 before 1.12.0. Users are recommended to upgrade to version 1.12.0, which fixes the issue.

  4. CVE-2026-44182 10.0 Critique

    jupyter-server enterprise_gateway · Jupyter enterprise gateway

    Jupyter Enterprise Gateway launches remote Jupyter Notebook kernels across distributed clusters like Apache Spark, Kubernetes, and Docker Swarm. In versions prior to 3.3.0, the server interpolates untrusted environment variables (e.g., KERNEL_XXX) into Kubernetes manifests without YAML-aware escaping, enabling YAML injection attacks. Attackers can inject new fields, overwrite critical fields (e.g., duplicate securityContext keys, where the last one prevails), and inject document boundaries (--- for new documents, ... for end-of-document) to generate multiple resources, potentially creating arbitrary types, such as privileged pods. The Jinja2 template for the Kubernetes manifest contains several kernel_xxx variables, such as kernel_working_dir that are used when rendering the manifest and are all vectors for YAML injection. This issue has been fixed in version 3.3.0.

  5. CVE-2026-44181 10.0 Critique

    jupyter-server enterprise_gateway · Jupyter enterprise gateway

    Jupyter Enterprise Gateway launches remote Jupyter Notebook kernels across distributed clusters like Apache Spark, Kubernetes, and Docker Swarm. In versions 2.0.0rc2 and above, prior to 3.3.0, the environment variables (KERNEL_XXX) used during the rendering of the Kubernetes manifest are vulnerable to Server Side Template Injection (SSTI). By including Jinja2 template expressions it is possible to execution Python code and OS Commands in the Enterprise Gateway service. The code can use or steal the Kubernetes service account token, which can steal Kubernetes secrets and be used to fully compromise the Kubernetes cluster by scheduling a privileged pod or a pod with a hostPath volume mount. This issue has been fixed in version 3.3.0.

  6. CVE-2026-44180 9.8 Critique

    jupyter-server enterprise_gateway · Jupyter enterprise gateway

    Jupyter Enterprise Gateway launches remote Jupyter Notebook kernels across distributed clusters like Apache Spark, Kubernetes, and Docker Swarm. Versions 2.0.0rc1 and above prior to 3.3.0 have a prohibited UID and GID feature that by default prevents launching kernels with UID or GID 0 (root), and this restriction can be bypassed using a specially crafted KERNEL_UID or KERNEL_GID value. This input validation vulnerability allows running Jupyter kernels as root, which can be dangerous as it allows more attack surface, and may lead to container escapes, compromising the worker node and all workloads running on it. Repeated exploitation can compromise all worker nodes, and thus the entire Kubernetes cluster. It is possible to specify volume mounts, so one vector for a container escape is to use a hostPath R/W volume mount, use this UID/GID bypass to run as root, and then gain code execution in the underlying worker node by creating a crontab entry in the mounted host file system. This issue has been fixed in version 3.0.0.

  7. CVE-2022-33891 8.8 Élevée Exploitée

    Apache Software Foundation Apache Spark · Apache spark

    The Apache Spark UI offers the possibility to enable ACLs via the configuration option spark.acls.enable. With an authentication filter, this checks whether a user has access permissions to view or modify the application. If ACLs are enabled, a code path in HttpSecurityFilter can allow someone to perform impersonation by providing an arbitrary user name. A malicious user might then be able to reach a permission check function that will ultimately build a Unix shell command based on their input, and execute it. This will result in arbitrary shell command execution as the user Spark is currently running as. This affects Apache Spark versions 3.0.3 and earlier, versions 3.1.1 to 3.1.2, and versions 3.2.0 to 3.2.1.