Since access to the Jupyter Server means access to running arbitrary code,
it is important to restrict access to the server.
For this reason, Jupyter Server uses a token-based authentication that is on by default.
If you enable a password for your server,
token authentication is not enabled by default.
When token authentication is enabled, the server uses a token to authenticate requests.
This token can be provided to login to the server in three ways:
in the Authorization header, e.g.:
Authorization: token abcdef...
In a URL parameter, e.g.:
In the password field of the login form that will be shown to you if you are not logged in.
When you start a Jupyter server with token authentication enabled (default),
a token is generated to use for authentication.
This token is logged to the terminal, so that you can copy/paste the URL into your browser:
[I 11:59:16.597 ServerApp] The Jupyter Server is running at:
If the Jupyter server is going to open your browser automatically,
an additional token is generated for launching the browser.
This additional token can be used only once,
and is used to set a cookie for your browser once it connects.
After your browser has made its first request with this one-time-token,
the token is discarded and a cookie is set in your browser.
At any later time, you can see the tokens and URLs for all of your running servers with jupyter server list:
$ jupyter server list
Currently running servers:
http://localhost:8888/?token=abc... :: /home/you/notebooks
https://0.0.0.0:9999/?token=123... :: /tmp/public
http://localhost:8889/ :: /tmp/has-password
For servers with token-authentication enabled, the URL in the above listing will include the token,
so you can copy and paste that URL into your browser to login.
If a server has no token (e.g. it has a password or has authentication disabled),
the URL will not include the token argument.
Once you have visited this URL,
a cookie will be set in your browser and you won’t need to use the token again,
unless you switch browsers, clear your cookies, or start a Jupyter server on a new port.
If a generated token doesn’t work well for you,
you can set a password for your server.
jupyter server password will prompt you for a password,
and store the hashed password in your jupyter_server_config.json.
New in version 5.0: jupyter server password command is added.
It is possible disable authentication altogether by setting the token and password to empty strings,
but this is NOT RECOMMENDED, unless authentication or access restrictions are handled at a different layer in your web application:
c.ServerApp.token = ''
c.ServerApp.password = ''
As Jupyter Server become more popular for sharing and collaboration,
the potential for malicious people to attempt to exploit the notebook
for their nefarious purposes increases. IPython 2.0 introduced a
security model to prevent execution of untrusted code without explicit
The whole point of Jupyter is arbitrary code execution. We have no
desire to limit what can be done with a notebook, which would negatively
impact its utility.
Unlike other programs, a Jupyter notebook document includes output.
Unlike other documents, that output exists in a context that can execute
The security problem we need to solve is that no code should execute
just because a user has opened a notebook that they did not
write. Like any other program, once a user decides to execute code in
a notebook, it is considered trusted, and should be allowed to do
Untrusted HTML is always sanitized
Outputs generated by the user are trusted
others) is never trusted
The central question of trust is “Did the current user do this?”
When a notebook is executed and saved, a signature is computed from a
digest of the notebook’s contents plus a secret key. This is stored in a
database, writable only by the current user. By default, this is located at:
~/.local/share/jupyter/nbsignatures.db # Linux
~/Library/Jupyter/nbsignatures.db # OS X
%APPDATA%/jupyter/nbsignatures.db # Windows
Each signature represents a series of outputs which were produced by code the
current user executed, and are therefore trusted.
When you open a notebook, the server computes its signature, and checks if it’s
output in the notebook will be trusted at load, otherwise it will be
Any output generated during an interactive session is trusted.
A notebook’s trust is updated when the notebook is saved. If there are
any untrusted outputs still in the notebook, the notebook will not be
trusted, and no signature will be stored. If all untrusted outputs have
been removed (either via Clear Output or re-execution), then the
notebook will become trusted.
While trust is updated per output, this is only for the duration of a
single session. A newly loaded notebook file is either trusted or not in its
Sometimes re-executing a notebook to generate trusted output is not an
option, either because dependencies are unavailable, or it would take a
long time. Users can explicitly trust a notebook in two ways:
At the command-line, with:
jupyter trust /path/to/notebook.ipynb
After loading the untrusted notebook, with File / Trust Notebook
File / Trust Notebook
These two methods simply load the notebook, compute a new signature, and add
that signature to the user’s database.
If you find a security vulnerability in Jupyter, either a failure of the
code to properly implement the model described here, or a failure of the
model itself, please report it to firstname.lastname@example.org.
If you prefer to encrypt your security reports,
you can use this PGP public key.
this PGP public key
Some use cases that work in Jupyter 1.0 became less convenient in
2.0 as a result of the security changes. We do our best to minimize
these annoyances, but security is always at odds with convenience.
While never officially supported, it had become common practice to put
not be visible on the page. Since Markdown cells are now sanitized (by
(including click event handlers, etc.) and CSS will be stripped.
We plan to provide a mechanism for notebook themes, but in the meantime
styling the notebook can only be done via either custom.css or CSS
in HTML output. The latter only have an effect if the notebook is
trusted, because otherwise the output will be sanitized just like
When collaborating on a notebook, people probably want to see the
outputs produced by their colleagues’ most recent executions. Since each
collaborator’s key will differ, this will result in each share starting
in an untrusted state. There are three basic approaches to this:
re-run notebooks when you get them (not always viable)
explicitly trust notebooks via jupyter trust or the notebook menu
(annoying, but easy)
share a notebook signatures database, and use configuration dedicated to the
collaboration while working on the project.
To share a signatures database among users, you can configure:
c.NotebookNotary.data_dir = "/path/to/signature_dir"
to specify a non-default path to the SQLite database (of notebook hashes,