Vulnerabilities

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.

JupyterLab Git is a Git extension for JupyterLab. From 0.30.0b3 before 0.54.0, the PlainTextDiff.ts createHeader() method passes Git filenames directly to innerHTML when rendering renamed files in commit history, allowing a crafted filename to execute JavaScript when a victim views the rename diff in the Git History tab. This issue is fixed in version 0.54.0.

Jupyter Server is the backend for Jupyter web applications. Prior to 2.20, the nbconvert HTTP handlers in jupyter_server render user-authored notebook HTML under the Jupyter origin without a sandbox directive in their Content-Security-Policy. Combined with nbconvert.HTMLExporter's default non-sanitizing behavior, a notebook carrying an HTML payload in a display_data output triggers stored XSS with cookie access, full /api/* authority, and kernel RCE. This vulnerability is fixed in 2.20.

A vulnerability in jupyter-server versions 1.12.0 through 2.17.0 allows an attacker to bypass CORS origin validation when the `allow_origin_pat` configuration is used. The issue arises from the use of `re.match()` for validating the `Origin` header, which only anchors at the start of the string. This allows attacker-controlled domains such as `trusted.example.com.evil.com` to pass validation against patterns intended to match `trusted.example.com`. The vulnerability affects multiple locations in the codebase, including CORS headers, WebSocket connections, referer validation, and login redirects, potentially enabling phishing attacks, arbitrary code execution, and unauthorized access to sensitive API responses.

A path traversal vulnerability exists in jupyter-server version 2.17.0 due to an incorrect root directory boundary check in the _get_os_path() function within jupyter_server/services/contents/fileio.py. The check uses startswith(root) without appending a trailing path separator, allowing sibling directories with names starting with the same prefix as root_dir to bypass the check. Additionally, the to_os_path() function in utils.py does not strip ".." from path parts, enabling traversal sequences to bypass the vulnerable check. This vulnerability can lead to unauthorized read/write access to files in sibling directories, potentially exposing sensitive data in shared hosting environments.

JupyterHub is software that allows users to create a multi-user server for Jupyter notebooks. In versions 4.1.0 through 5.4.4, XSRF protection (updated in 4.1.0) inappropriately treated requests with Sec-Fetch-Mode: no-cors as same-origin requests, bypassing XSRF checks. The JSON API is not affected, only HTTP form endpoints, such as /hub/spawn and /hub/accept-share, meaning attackers could trigger server spawn (but not access the server) and if the attacker is a JupyterHub user permitted to share access to their server, cause a user to accept a share and have access to the attacker's server. This issue has been fixed in version 5.4.5. If developers are unable to immediately upgrade, they can temporarily mitigate this issue by dropping requests to JupyterHub with Sec-Fetch-Mode: no-cors if they are using a reverse proxy.

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, 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.

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.

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/ `. 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/ `, 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.

JupyterLab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture. Users of JupyterLab who click on a malicious link may get their `Authorization` and `XSRFToken` tokens exposed to a third party when running an older `jupyter-server` version. JupyterLab versions 4.1.0b2, 4.0.11, and 3.6.7 are patched. No workaround has been identified, however users should ensure to upgrade `jupyter-server` to version 2.7.2 or newer which includes a redirect vulnerability fix.

jupyter-lsp is a coding assistance tool for JupyterLab (code navigation + hover suggestions + linters + autocompletion + rename) using Language Server Protocol. Installations of jupyter-lsp running in environments without configured file system access control (on the operating system level), and with jupyter-server instances exposed to non-trusted network are vulnerable to unauthorised access and modification of file system beyond the jupyter root directory. This issue has been patched in version 2.2.2 and all users are advised to upgrade. Users unable to upgrade should uninstall jupyter-lsp.

dockerspawner is a tool to spawn JupyterHub single user servers in Docker containers. Users of JupyterHub deployments running DockerSpawner starting with 0.11.0 without specifying `DockerSpawner.allowed_images` configuration allow users to launch _any_ pullable docker image, instead of restricting to only the single configured image, as intended. This issue has been addressed in commit `3ba4b665b` which has been included in dockerspawner release version 13. Users are advised to upgrade. Users unable to upgrade should explicitly set `DockerSpawner.allowed_images` to a non-empty list containing only the default image will result in the intended default behavior.

The Jupyter Server provides the backend (i.e. the core services, APIs, and REST endpoints) for Jupyter web applications like Jupyter notebook, JupyterLab, and Voila. Unhandled errors in API requests coming from an authenticated user include traceback information, which can include path information. There is no known mechanism by which to trigger these errors without authentication, so the paths revealed are not considered particularly sensitive, given that the requesting user has arbitrary execution permissions already in the same environment. A fix has been introduced in commit `0056c3aa52` which no longer includes traceback information in JSON error responses. For compatibility, the traceback field is present, but always empty. This commit has been included in version 2.11.2. Users are advised to upgrade. There are no known workarounds for this vulnerability.

jupyter-server is the backend for Jupyter web applications. Open Redirect Vulnerability. Maliciously crafted login links to known Jupyter Servers can cause successful login or an already logged-in session to be redirected to arbitrary sites, which should be restricted to Jupyter Server-served URLs. This issue has been addressed in commit `29036259` which is included in release 2.7.2. Users are advised to upgrade. There are no known workarounds for this vulnerability.

Jupyter Core is a package for the core common functionality of Jupyter projects. Jupyter Core prior to version 4.11.2 contains an arbitrary code execution vulnerability in `jupyter_core` that stems from `jupyter_core` executing untrusted files in CWD. This vulnerability allows one user to run code as another. Version 4.11.2 contains a patch for this issue. There are no known workarounds.

The GitHub Security Lab discovered sixteen ways to exploit a cross-site scripting vulnerability in nbconvert. When using nbconvert to generate an HTML version of a user-controllable notebook, it is possible to inject arbitrary HTML which may lead to cross-site scripting (XSS) vulnerabilities if these HTML notebooks are served by a web server (eg: nbviewer).

Jupyter Server provides the backend (i.e. the core services, APIs, and REST endpoints) for Jupyter web applications like Jupyter Notebook. Prior to version 1.17.1, if notebook server is started with a value of `root_dir` that contains the starting user's home directory, then the underlying REST API can be used to leak the access token assigned at start time by guessing/brute forcing the PID of the jupyter server. While this requires an authenticated user session, this URL can be used from a cross-site scripting payload or from a hooked or otherwise compromised browser to leak this access token to a malicious third party. This token can be used along with the REST API to interact with Jupyter services/notebooks such as modifying or overwriting critical files, such as .bashrc or .ssh/authorized_keys, allowing a malicious user to read potentially sensitive data and possibly gain control of the impacted system. This issue is patched in version 1.17.1.

Jupyter Notebook is a web-based notebook environment for interactive computing. Prior to version 6.4.12, authenticated requests to the notebook server with `ContentsManager.allow_hidden = False` only prevented listing the contents of hidden directories, not accessing individual hidden files or files in hidden directories (i.e. hidden files were 'hidden' but not 'inaccessible'). This could lead to notebook configurations allowing authenticated access to files that may reasonably be expected to be disallowed. Because fully authenticated requests are required, this is of relatively low impact. But if a server's root directory contains sensitive files whose only protection from the server is being hidden (e.g. `~/.ssh` while serving $HOME), then any authenticated requests could access files if their names are guessable. Such contexts also necessarily have full access to the server and therefore execution permissions, which also generally grants access to all the same files. So this does not generally result in any privilege escalation or increase in information access, only an additional, unintended means by which the files could be accessed. Version 6.4.12 contains a patch for this issue. There are currently no known workarounds.

OAuthenticator is an OAuth token library for the JupyerHub login handler. CILogonOAuthenticator is provided by the OAuthenticator package, and lets users log in to a JupyterHub via CILogon. This is primarily used to restrict a JupyterHub only to users of a given institute. The allowed_idps configuration trait of CILogonOAuthenticator is documented to be a list of domains that indicate the institutions whose users are authorized to access this JupyterHub. This authorization is validated by ensuring that the *email* field provided to us by CILogon has a *domain* that matches one of the domains listed in `allowed_idps`.If `allowed_idps` contains `berkeley.edu`, you might expect only users with valid current credentials provided by University of California, Berkeley to be able to access the JupyterHub. However, CILogonOAuthenticator does *not* verify which provider is used by the user to login, only the email address provided. So a user can login with a GitHub account that has email set to ` @berkeley.edu`, and that will be treated exactly the same as someone logging in using the UC Berkeley official Identity Provider. The patch fixing this issue makes a *breaking change* in how `allowed_idps` is interpreted. It's no longer a list of domains, but configuration representing the `EntityID` of the IdPs that are allowed, picked from the [list maintained by CILogon](https://cilogon.org/idplist/). Users are advised to upgrade.

The Jupyter notebook is a web-based notebook environment for interactive computing. Prior to version 6.4.9, unauthorized actors can access sensitive information from server logs. Anytime a 5xx error is triggered, the auth cookie and other header values are recorded in Jupyter server logs by default. Considering these logs do not require root access, an attacker can monitor these logs, steal sensitive auth/cookie information, and gain access to the Jupyter server. Jupyter notebook version 6.4.x contains a patch for this issue. There are currently no known workarounds.

The Jupyter Server provides the backend (i.e. the core services, APIs, and REST endpoints) for Jupyter web applications. Prior to version 1.15.4, unauthorized actors can access sensitive information from server logs. Anytime a 5xx error is triggered, the auth cookie and other header values are recorded in Jupyter Server logs by default. Considering these logs do not require root access, an attacker can monitor these logs, steal sensitive auth/cookie information, and gain access to the Jupyter server. Jupyter Server version 1.15.4 contains a patch for this issue. There are currently no known workarounds.

Jupyter Server Proxy is a Jupyter notebook server extension to proxy web services. Versions of Jupyter Server Proxy prior to 3.2.1 are vulnerable to Server-Side Request Forgery (SSRF). Any user deploying Jupyter Server or Notebook with jupyter-proxy-server extension enabled is affected. A lack of input validation allows authenticated clients to proxy requests to other hosts, bypassing the `allowed_hosts` check. Because authentication is required, which already grants permissions to make the same requests via kernel or terminal execution, this is considered low to moderate severity. Users may upgrade to version 3.2.1 to receive a patch or, as a workaround, install the patch manually.

JupyterHub is an open source multi-user server for Jupyter notebooks. In affected versions users who have multiple JupyterLab tabs open in the same browser session, may see incomplete logout from the single-user server, as fresh credentials (for the single-user server only, not the Hub) reinstated after logout, if another active JupyterLab session is open while the logout takes place. Upgrade to JupyterHub 1.5. For distributed deployments, it is jupyterhub in the _user_ environment that needs patching. There are no patches necessary in the Hub environment. The only workaround is to make sure that only one JupyterLab tab is open when you log out.

nbdime provides tools for diffing and merging of Jupyter Notebooks. In affected versions a stored cross-site scripting (XSS) issue exists within the Jupyter-owned nbdime project. It appears that when reading the file name and path from disk, the extension does not sanitize the string it constructs before returning it to be displayed. The diffNotebookCheckpoint function within nbdime causes this issue. When attempting to display the name of the local notebook (diffNotebookCheckpoint), nbdime appears to simply append .ipynb to the name of the input file. The NbdimeWidget is then created, and the base string is passed through to the request API function. From there, the frontend simply renders the HTML tag and anything along with it. Users are advised to patch to the most recent version of the affected product.

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.