CVE-2026-73417 Vulnerability Analysis & Exploit Details

CVE-2026-73417 Vulnerability Summary

CVE-2026-73417: jupyterlab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. From 3.3.0 until 4.5.10 and 4.6.2, JupyterLab allows notebook settings to be shared and applied through an overrides.json file using the Import button in the Settings Editor. In packages/notebook-extension/schema/tracker.json and packages/notebook-extension/src/index.ts, the sideBySideLeftMarginOverride and sideBySideRightMarginOverride settings are not properly validated before being inserted into style content, allowing a crafted settings file to contain instructions that execute as code instead of only changing display preferences. A user can import the malicious file, or an attacker with access to a shared settings location can plant an overrides.json that is applied automatically. The embedded code runs with the affected user's access and can read or modify notebooks and files and run code through the notebook server, including on a connected kernel. This issue is fixed in versions 4.5.10 and 4.6.2.

CVE-2026-73417 References

External References

• https://github.com/jupyterlab/jupyterlab/commit/9365f020baec5221deaf11535ed554c06637c999
• https://github.com/jupyterlab/jupyterlab/commit/be9303f5bcd5308eaeae953c5a3c903046682c2c
• https://github.com/jupyterlab/jupyterlab/commit/f1beab4a2027af4719d6edc07d52d6cf5a39a432
• https://github.com/jupyterlab/jupyterlab/pull/19184
• https://github.com/jupyterlab/jupyterlab/pull/19185
• https://github.com/jupyterlab/jupyterlab/pull/19186
• https://github.com/jupyterlab/jupyterlab/releases/tag/v4.5.10
• https://github.com/jupyterlab/jupyterlab/releases/tag/v4.6.2
• https://github.com/jupyterlab/jupyterlab/releases/tag/v4.7.0a1
• https://github.com/jupyterlab/jupyterlab/security/advisories/GHSA-pppj-hq3g-57pj

CWE Common Weakness Enumeration

CAPEC Common Attack Pattern Enumeration and Classification

• Cross Zone Scripting CAPEC-104 An attacker is able to cause a victim to load content into their web-browser that bypasses security zone controls and gain access to increased privileges to execute scripting code or other web objects such as unsigned ActiveX controls or applets. This is a privilege elevation attack targeted at zone-based web-browser security.
• User-Controlled Filename CAPEC-73 An attack of this type involves an adversary inserting malicious characters (such as a XSS redirection) into a filename, directly or indirectly that is then used by the target software to generate HTML text or other potentially executable content. Many websites rely on user-generated content and dynamically build resources like files, filenames, and URL links directly from user supplied data. In this attack pattern, the attacker uploads code that can execute in the client browser and/or redirect the client browser to a site that the attacker owns. All XSS attack payload variants can be used to pass and exploit these vulnerabilities.
• Web Server Logs Tampering CAPEC-81 Web Logs Tampering attacks involve an attacker injecting, deleting or otherwise tampering with the contents of web logs typically for the purposes of masking other malicious behavior. Additionally, writing malicious data to log files may target jobs, filters, reports, and other agents that process the logs in an asynchronous attack pattern. This pattern of attack is similar to "Log Injection-Tampering-Forging" except that in this case, the attack is targeting the logs of the web server and not the application.
• AJAX Footprinting CAPEC-85 This attack utilizes the frequent client-server roundtrips in Ajax conversation to scan a system. While Ajax does not open up new vulnerabilities per se, it does optimize them from an attacker point of view. A common first step for an attacker is to footprint the target environment to understand what attacks will work. Since footprinting relies on enumeration, the conversational pattern of rapid, multiple requests and responses that are typical in Ajax applications enable an attacker to look for many vulnerabilities, well-known ports, network locations and so on. The knowledge gained through Ajax fingerprinting can be used to support other attacks, such as XSS.

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