CVE-2024-35225

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 users 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.

Weakness

The product prepares a structured message for communication with another component, but encoding or escaping of the data is either missing or done incorrectly. As a result, the intended structure of the message is not preserved.

Extended Description

Improper encoding or escaping can allow attackers to change the commands that are sent to another component, inserting malicious commands instead. Most products follow a certain protocol that uses structured messages for communication between components, such as queries or commands. These structured messages can contain raw data interspersed with metadata or control information. For example, “GET /index.html HTTP/1.1” is a structured message containing a command (“GET”) with a single argument ("/index.html") and metadata about which protocol version is being used (“HTTP/1.1”). If an application uses attacker-supplied inputs to construct a structured message without properly encoding or escaping, then the attacker could insert special characters that will cause the data to be interpreted as control information or metadata. Consequently, the component that receives the output will perform the wrong operations, or otherwise interpret the data incorrectly.

Potential Mitigations

• Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid.
• For example, consider using the ESAPI Encoding control [REF-45] or a similar tool, library, or framework. These will help the programmer encode outputs in a manner less prone to error.
• Alternately, use built-in functions, but consider using wrappers in case those functions are discovered to have a vulnerability.
• If available, use structured mechanisms that automatically enforce the separation between data and code. These mechanisms may be able to provide the relevant quoting, encoding, and validation automatically, instead of relying on the developer to provide this capability at every point where output is generated.
• For example, stored procedures can enforce database query structure and reduce the likelihood of SQL injection.

Related Attack Patterns

• https://cwe.mitre.org/data/definitions/104.html
• https://cwe.mitre.org/data/definitions/73.html
• https://cwe.mitre.org/data/definitions/81.html
• https://cwe.mitre.org/data/definitions/85.html

References

• https://github.com/jupyterhub/jupyter-server-proxy/blob/62a290f08750f7ae55a0c29ca339c9a39a7b2a7b/jupyter_server_proxy/handlers.py#L328
• https://github.com/jupyterhub/jupyter-server-proxy/commit/7abc9dc5bbb0b4b440548a5375261b8b8192fc22
• https://github.com/jupyterhub/jupyter-server-proxy/commit/ff78128087e73fb9d0909e1366f8bf051e8ea878
• https://github.com/jupyterhub/jupyter-server-proxy/security/advisories/GHSA-fvcq-4x64-hqxr