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 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.
This vulnerability carries a CRITICAL severity rating with a CVSS v3.1 score of 9.6, indicating it can be exploited remotely over the network with relatively low complexity though user interaction is required and does not require pre-existing privileges . The vulnerability impacts confidentiality (data exposure), integrity (unauthorized modifications), and availability (service disruption) for affected systems. Impacting 1 product from jupyter organizations running these solutions should prioritize assessment and patching.
Reported in 2024, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.
2024-06-11T22:15:09.520
2025-04-10T20:35:41.367
Analyzed
CVSSv3.1: 9.6 (CRITICAL)
| Type | Vendor | Product | Version/Range | Vulnerable? |
|---|---|---|---|---|
| Application | jupyter | jupyter_server_proxy | < 3.2.4 | Yes |
| Application | jupyter | jupyter_server_proxy | < 4.2.0 | Yes |
SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For jupyter's affected products, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.