Jupyter-server vulnerabilities

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.

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.

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.