OAuthenticator is software that allows OAuth2 identity providers to be plugged in and used with JupyterHub. JupyterHub < 5.0, when used with `GlobusOAuthenticator`, could be configured to allow all users from a particular institution only. This worked fine prior to JupyterHub 5.0, because `allow_all` did not take precedence over `identity_provider`. Since JupyterHub 5.0, `allow_all` does take precedence over `identity_provider`. On a hub with the same config, now all users will be allowed to login, regardless of `identity_provider`. `identity_provider` will basically be ignored. This is a documented change in JupyterHub 5.0, but is likely to catch many users by surprise. OAuthenticator 16.3.1 fixes the issue with JupyterHub 5.0, and does not affect previous versions. As a workaround, do not upgrade to JupyterHub 5.0 when using `GlobusOAuthenticator` in the prior 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 vers…

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.

FirstUseAuthenticator is a JupyterHub authenticator that helps new users set their password on their first login to JupyterHub. When JupyterHub is used with FirstUseAuthenticator, a vulnerability in versions prior to 1.0.0 allows unauthorized access to any user's account if `create_users=True` and the username is known or guessed. One may upgrade to version 1.0.0 or apply a patch manually to mitigate the vulnerability. For those who cannot upgrade, there is no complete workaround, but a partial mitigation exists. One can disable user creation with `c.FirstUseAuthenticator.create_users = False`, which will only allow login with fully normalized usernames for already existing users prior to jupyterhub-firstuserauthenticator 1.0.0. If any users have never logged in with their normalized username (i.e. lowercase), they will still be vulnerable until a patch or upgrade occurs.

nbgitpuller is a Jupyter server extension to sync a git repository one-way to a local path. Due to unsanitized input, visiting maliciously crafted links could result in arbitrary code execution in the user environment. This has been resolved in version 0.10.2 and all users are advised to upgrade. No work around exist for users who can not upgrade.

jupyterhub-systemdspawner enables JupyterHub to spawn single-user notebook servers using systemd. In jupyterhub-systemdspawner before version 0.15 user API tokens issued to single-user servers are specified in the environment of systemd units. These tokens are incorrectly accessible to all users. In particular, the-littlest-jupyterhub is affected, which uses systemdspawner by default. This is patched in jupyterhub-systemdspawner v0.15

In jupyterhub-kubespawner before 0.12, certain usernames will be able to craft particular server names which will grant them access to the default server of other users who have matching usernames. This has been fixed in 0.12.