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.
This vulnerability carries a HIGH severity rating with a CVSS v3.1 score of 7.6, 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), limited integrity, and limited availability for affected systems. Impacting 2 products from jupyter, 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.
2024-08-28T20:15:07.963
2024-08-30T15:56:16.477
Analyzed
CVSSv3.1: 7.6 (HIGH)
| Type | Vendor | Product | Version/Range | Vulnerable? |
|---|---|---|---|---|
| Application | jupyter | jupyterlab | < 3.6.8 | Yes |
| Application | jupyter | jupyterlab | < 4.2.5 | Yes |
| Application | jupyter | notebook | < 7.2.2 | 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.