The Security Overview section helps you learn about:
the design of JupyterHub with respect to web security
the semi-trusted user
the available mitigations to protect untrusted users from each other
the value of periodic security audits
This overview also helps you obtain a deeper understanding of how JupyterHub works.
Semi-trusted and untrusted users#
JupyterHub is designed to be a simple multi-user server for modestly sized groups of semi-trusted users. While the design reflects serving semi-trusted users, JupyterHub can also be suitable for serving untrusted users.
As a result, using JupyterHub with untrusted users means more work by the administrator, since much care is required to secure a Hub, with extra caution on protecting users from each other.
One aspect of JupyterHub’s design simplicity for semi-trusted users is that the Hub and single-user servers are placed in a single domain, behind a proxy. If the Hub is serving untrusted users, many of the web’s cross-site protections are not applied between single-user servers and the Hub, or between single-user servers and each other, since browsers see the whole thing (proxy, Hub, and single user servers) as a single website (i.e. single domain).
Protect users from each other#
To protect users from each other, a user must never be able to write arbitrary HTML and serve it to another user on the Hub’s domain. This is prevented by JupyterHub’s authentication setup because only the owner of a given single-user notebook server is allowed to view user-authored pages served by the given single-user notebook server.
To protect all users from each other, JupyterHub administrators must ensure that:
A user does not have permission to modify their single-user notebook server, including:
the installation of new packages in the Python environment that runs their single-user server;
the creation of new files in any
PATHdirectory that precedes the directory containing
PATHis used to resolve the single-user executable instead of using an absolute path);
the modification of environment variables (e.g. PATH, PYTHONPATH) for their single-user server;
the modification of the configuration of the notebook server (the
If any additional services are run on the same domain as the Hub, the services must never display user-authored HTML that is neither sanitized nor sandboxed (e.g. IFramed) to any user that lacks authentication as the author of a file.
Mitigate security issues#
The several approaches to mitigating security issues with configuration options provided by JupyterHub include:
JupyterHub provides the ability to run single-user servers on their own
subdomains. This means the cross-origin protections between servers has the
desired effect, and user servers and the Hub are protected from each other. A
user’s single-user server will be at
username.jupyter.mydomain.com. This also
requires all user subdomains to point to the same address, which is most easily
accomplished with wildcard DNS. Since this spreads the service across multiple
domains, you will need wildcard SSL as well. Unfortunately, for many
institutional domains, wildcard DNS and SSL are not available. If you do plan
to serve untrusted users, enabling subdomains is highly encouraged, as it
resolves the cross-site issues.
Disable user config#
If subdomains are unavailable or undesirable, JupyterHub provides a
Spawner.disable_user_config, which can be set to prevent
the user-owned configuration files from being loaded. After implementing this
PATHs and package installation are the other things that the
admin must enforce.
Prevent spawners from evaluating shell configuration files#
For most Spawners,
PATH is not something users can influence, but it’s important that
the Spawner should not evaluate shell configuration files prior to launching the server.
Isolate packages using virtualenv#
Package isolation is most easily handled by running the single-user server in a virtualenv with disabled system-site-packages. The user should not have permission to install packages into this environment.
It is important to note that the control over the environment only affects the single-user server, and not the environment(s) in which the user’s kernel(s) may run. Installing additional packages in the kernel environment does not pose additional risk to the web application’s security.
Encrypt internal connections with SSL/TLS#
By default, all communications on the server, between the proxy, hub, and single
-user notebooks are performed unencrypted. Setting the
internal_ssl flag in
jupyterhub_config.py secures the aforementioned routes. Turning this
feature on does require that the enabled
Spawner can use the certificates
generated by the
Hub (the default
LocalProcessSpawner can, for instance).
It is also important to note that this encryption does not cover the
zmq tcp sockets between the Notebook client and kernel yet. While users cannot
submit arbitrary commands to another user’s kernel, they can bind to these
sockets and listen. When serving untrusted users, this eavesdropping can be
mitigated by setting
ipc. This applies standard
Unix permissions to the communication sockets thereby restricting
communication to the socket owner. The
internal_ssl option will eventually
extend to securing the
tcp sockets as well.
We recommend that you do periodic reviews of your deployment’s security. It’s good practice to keep JupyterHub, configurable-http-proxy, and nodejs versions up to date.
A handy website for testing your deployment is Qualsys’ SSL analyzer tool.
If you believe you have found a security vulnerability in JupyterHub, or any Jupyter project, please report it to firstname.lastname@example.org. If you prefer to encrypt your security reports, you can use this PGP public key.