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.

JupyterLab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. From 4.0.0 to 4.5.6, the allow-list of extensions that can be installed from PyPI Extension Manager (allowed_extensions_uris) is not correctly enforced by JupyterLab. The PyPI Extension Manager was not contained to packages listed on the default PyPI index. This vulnerability is fixed in 4.5.7.

Jupyter Server is the backend for Jupyter web applications. In versions 2.17.0 and earlier, the secret used to sign authentication cookies is persisted to a static file at ~/.local/share/jupyter/runtime/jupyter_cookie_secret and is never rotated when a user changes their password. After a password reset and server restart, any previously issued authentication cookie remains cryptographically valid because the signing key has not changed. An attacker who has captured a session cookie through any means retains full authenticated access to the server regardless of subsequent password changes. This affects deployments using password-based authentication, particularly shared or public-facing servers where credential rotation is expected to revoke existing sessions. This issue has been fixed in version 2.18.0.

Jupyter Server is the backend for Jupyter web applications. In versions 2.17.0 and earlier, the Origin header validation uses Python's re.match() to check incoming origins against the allow_origin_pat configuration value. Because re.match() only anchors at the start of the string and does not require a full match, a pattern intended to match only a trusted domain (e.g., trusted.example.com) will also match any origin that begins with that domain followed by additional characters (e.g., trusted.example.com.evil.com). An attacker who controls such a domain can bypass the CORS origin restriction and make cross-origin requests to the Jupyter Server API from an untrusted site. This issue has been fixed in version 2.18.0.

Jupyter Server is the backend for Jupyter web applications. In versions 2.17.0 and earlier, a path traversal vulnerability in the REST API allows an authenticated user to escape the configured root_dir and access sibling directories whose names begin with the same prefix as the root_dir. For example, with a root_dir named "test", the API permits access to a sibling directory named "testtest" through a crafted request to the /api/contents endpoint using encoded path components. An attacker can read, write, and delete files in affected sibling directories. Multi-tenant deployments using predictable naming schemes are particularly at risk, as a user with a directory named "user1" could access directories for user10 through user19 and beyond. A user who can choose a single-character folder name could gain access to a significant number of sibling directories. Version 2.18.0 contains a fix. As a workaround, ensure folder names do not share a common prefix with any sibling directory.

Jupyter Server is the backend for Jupyter web applications. In jupyter_server versions through 2.17.0, the next query parameter in the login flow is insufficiently validated in `LoginFormHandler._redirect_safe()`, which allows redirects to arbitrary external domains via values such as `///example.com`. An attacker can use a crafted login URL to redirect users to a malicious site and facilitate phishing attacks. This issue is fixed in version 2.18.0.

The nbconvert tool, jupyter nbconvert, converts Jupyter notebooks to various other formats via Jinja templates. In versions 6.5 through 7.17.0, when `HTMLExporter.embed_images=True`, nbconvert's markdown renderer allows arbitrary file read via path traversal in image references. A malicious notebook can exfiltrate sensitive files from the conversion host by embedding them as base64 data URIs in the output HTML. nbconvert 7.17.1 contains a fix. As a workaround, do not enable `HTMLExporter.embed_images`; it is not enabled by default.

The nbconvert tool, jupyter nbconvert, converts Jupyter notebooks to various other formats via Jinja templates. Versions 6.5 through 7.17.0 allow arbitrary file writes to locations outside the intended output directory when processing notebooks containing crafted cell attachment filenames. The `ExtractAttachmentsPreprocessor` passes attachment filenames directly to the filesystem without sanitization, enabling path traversal attacks. This vulnerability provides complete control over both the destination path and file extension. Version 7.17.1 contains a patch.

LTI JupyterHub Authenticator is a JupyterHub authenticator for LTI. Prior to version 1.6.3, the LTI 1.1 validator stores OAuth nonces in a class-level dictionary that grows without bounds. Nonces are added before signature validation, so an attacker with knowledge of a valid consumer key can send repeated requests with unique nonces to gradually exhaust server memory, causing a denial of service. This issue has been patched in version 1.6.3.

JupyterHub is software that allows one to create a multi-user server for Jupyter notebooks. Prior to version 5.4.4, an open redirect vulnerability in JupyterHub allows attackers to construct links which, when clicked, take users to the JupyterHub login page, after which they are sent to an arbitrary attacker-controlled site outside JupyterHub instead of a JupyterHub page, bypassing JupyterHub's check to prevent this. This issue has been patched in version 5.4.4.

OAuthenticator is software that allows OAuth2 identity providers to be plugged in and used with JupyterHub. Prior to version 17.4.0, an authentication bypass vulnerability in oauthenticator allows an attacker with an unverified email address on an Auth0 tenant to login to JupyterHub. When email is used as the usrname_claim, this gives users control over their username and the possibility of account takeover. This issue has been patched in version 17.4.0.

The nbconvert tool, jupyter nbconvert, converts Jupyter notebooks to various other formats via Jinja templates. Versions of nbconvert up to and including 7.16.6 on Windows have a vulnerability in which converting a notebook containing SVG output to a PDF results in unauthorized code execution. Specifically, a third party can create a `inkscape.bat` file that defines a Windows batch script, capable of arbitrary code execution. When a user runs `jupyter nbconvert --to pdf` on a notebook containing SVG output to a PDF on a Windows platform from this directory, the `inkscape.bat` file is run unexpectedly. This issue has been patched in version 7.17.0.

jupyterlab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. Prior to version 4.4.8, links generated with LaTeX typesetters in Markdown files and Markdown cells in JupyterLab and Jupyter Notebook did not include the noopener attribute. This is deemed to have no impact on the default installations. Theoretically users of third-party LaTeX-rendering extensions could find themselves vulnerable to reverse tabnabbing attacks if links generated by those extensions included target=_blank (no such extensions are known at time of writing) and they were to click on a link generated in LaTeX (typically visibly different from other links). This issue has been patched in version 4.4.8.

Jupyter Core is a package for the core common functionality of Jupyter projects. When using Jupyter Core prior to version 5.8.0 on Windows, the shared `%PROGRAMDATA%` directory is searched for configuration files (`SYSTEM_CONFIG_PATH` and `SYSTEM_JUPYTER_PATH`), which may allow users to create configuration files affecting other users. Only shared Windows systems with multiple users and unprotected `%PROGRAMDATA%` are affected. Users should upgrade to Jupyter Core version 5.8.0 or later to receive a patch. Some other mitigations are available. As administrator, modify the permissions on the `%PROGRAMDATA%` directory so it is not writable by unauthorized users; or as administrator, create the `%PROGRAMDATA%\jupyter` directory with appropriately restrictive permissions; or as user or administrator, set the `%PROGRAMDATA%` environment variable to a directory with appropriately restrictive permissions (e.g. controlled by administrators _or_ the current user).

`jupyterhub-ltiauthenticator` is a JupyterHub authenticator for learning tools interoperability (LTI). LTI13Authenticator that was introduced in `jupyterhub-ltiauthenticator` 1.3.0 wasn't validating JWT signatures. This is believed to allow the LTI13Authenticator to authorize a forged request. Only users that has configured a JupyterHub installation to use the authenticator class `LTI13Authenticator` are affected. `jupyterhub-ltiauthenticator` version 1.4.0 removes LTI13Authenticator to address the issue. No known workarounds are available.

jupyterlab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. This vulnerability depends on user interaction by opening a malicious notebook with Markdown cells, or Markdown file using JupyterLab preview feature. A malicious user can access any data that the attacked user has access to as well as perform arbitrary requests acting as the attacked user. JupyterLab v3.6.8, v4.2.5 and Jupyter Notebook v7.2.2 have been patched to resolve this issue. Users are advised to upgrade. There is no workaround for the underlying DOM Clobbering susceptibility. However, select plugins can be disabled on deployments which cannot update in a timely fashion to minimise the risk. These are: 1. `@jupyterlab/mathjax-extension:plugin` - users will loose ability to preview mathematical equations. 2. `@jupyterlab/markdownviewer-extension:plugin` - users will loose ability to open Markdown previews. 3. `@jupyterlab/mathjax2-extension:plugin` (if installed with optional `jupyterlab-mathjax2` package) - an older version of the mathjax plugin for JupyterLab 4.x. To disable these extensions run: ```jupyter labextension disable @jupyterlab/markdownviewer-extension:plugin && jupyter labextension disable @jupyterlab/mathjax-extension:plugin && jupyter labextension disable @jupyterlab/mathjax2-extension:plugin ``` in bash.

JupyterHub is software that allows one to create a multi-user server for Jupyter notebooks. Prior to versions 4.1.6 and 5.1.0, if a user is granted the `admin:users` scope, they may escalate their own privileges by making themselves a full admin user. The impact is relatively small in that `admin:users` is already an extremely privileged scope only granted to trusted users. In effect, `admin:users` is equivalent to `admin=True`, which is not intended. Note that the change here only prevents escalation to the built-in JupyterHub admin role that has unrestricted permissions. It does not prevent users with e.g. `groups` permissions from granting themselves or other users permissions via group membership, which is intentional. Versions 4.1.6 and 5.1.0 fix this issue.

JupyterLab extension template is a `copier` template for JupyterLab extensions. Repositories created using this template with `test` option include `update-integration-tests.yml` workflow which has an RCE vulnerability. Extension authors hosting their code on GitHub are urged to upgrade the template to the latest version. Users who made changes to `update-integration-tests.yml`, accept overwriting of this file and re-apply your changes later. Users may wish to temporarily disable GitHub Actions while working on the upgrade. We recommend rebasing all open pull requests from untrusted users as actions may run using the version from the `main` branch at the time when the pull request was created. Users who are upgrading from template version prior to 4.3.0 may wish to leave out proposed changes to the release workflow for now as it requires additional configuration.

Jupyter Server Proxy allows users to run arbitrary external processes alongside their notebook server and provide authenticated web access to them. Versions of 3.x prior to 3.2.4 and 4.x prior to 4.2.0 have a reflected cross-site scripting (XSS) issue. The `/proxy` endpoint accepts a `host` path segment in the format `/proxy/<host>`. When this endpoint is called with an invalid `host` value, `jupyter-server-proxy` replies with a response that includes the value of `host`, without sanitization [2]. A third-party actor can leverage this by sending a phishing link with an invalid `host` value containing custom JavaScript to a user. When the user clicks this phishing link, the browser renders the response of `GET /proxy/<host>`, which runs the custom JavaScript contained in `host` set by the actor. As any arbitrary JavaScript can be run after the user clicks on a phishing link, this issue permits extensive access to the user's JupyterLab instance for an actor. Patches are included in versions 4.2.0 and 3.2.4. As a workaround, server operators who are unable to upgrade can disable the `jupyter-server-proxy` extension.

The Jupyter Server provides the backend for Jupyter web applications. Jupyter Server on Windows has a vulnerability that lets unauthenticated attackers leak the NTLMv2 password hash of the Windows user running the Jupyter server. An attacker can crack this password to gain access to the Windows machine hosting the Jupyter server, or access other network-accessible machines or 3rd party services using that credential. Or an attacker perform an NTLM relay attack without cracking the credential to gain access to other network-accessible machines. This vulnerability is fixed in 2.14.1.

JupyterHub is an open source multi-user server for Jupyter notebooks. By tricking a user into visiting a malicious subdomain, the attacker can achieve an XSS directly affecting the former's session. More precisely, in the context of JupyterHub, this XSS could achieve full access to JupyterHub API and user's single-user server. The affected configurations are single-origin JupyterHub deployments and JupyterHub deployments with user-controlled applications running on subdomains or peer subdomains of either the Hub or a single-user server. This vulnerability is fixed in 4.1.0.

OAuthenticator provides plugins for JupyterHub to use common OAuth providers, as well as base classes for writing one's own Authenticators with any OAuth 2.0 provider. `GoogleOAuthenticator.hosted_domain` is used to restrict what Google accounts can be authorized access to a JupyterHub. The restriction is intented to be to Google accounts part of one or more Google organization verified to control specified domain(s). Prior to version 16.3.0, the actual restriction has been to Google accounts with emails ending with the domain. Such accounts could have been created by anyone which at one time was able to read an email associated with the domain. This was described by Dylan Ayrey (@dxa4481) in this [blog post] from 15th December 2023). OAuthenticator 16.3.0 contains a patch for this issue. As a workaround, restrict who can login another way, such as `allowed_users` or `allowed_google_groups`.

Jupyter Server Proxy allows users to run arbitrary external processes alongside their Jupyter notebook servers and provides authenticated web access. Prior to versions 3.2.3 and 4.1.1, Jupyter Server Proxy did not check user authentication appropriately when proxying websockets, allowing unauthenticated access to anyone who had network access to the Jupyter server endpoint. This vulnerability can allow unauthenticated remote access to any websocket endpoint set up to be accessible via Jupyter Server Proxy. In many cases, this leads to remote unauthenticated arbitrary code execution, due to how affected instances use websockets. The websocket endpoints exposed by `jupyter_server` itself is not affected. Projects that do not rely on websockets are also not affected. Versions 3.2.3 and 4.1.1 contain a fix for this issue.