Summary
JupyterLab: Cross-site scripting (XSS) via crafted settings file (overrides.json)
JupyterLab 4.5+ allows notebook settings to be shared and applied through an overrides.json file using the Import button in the Settings Editor.
Certain notebook display settings were not properly validated before being applied. As a result, a crafted settings file could contain hidden instructions that run as code inside JupyterLab when imported, instead of only changing a display preference.
Because importing a settings file appears harmless, a user could import a file shared by another party without realizing it could do more. On multi-tenant file systems without proper permission control, another user could plant a malicious overrides.json.
CVE assignment pending, GitHub CNA is experiencing severe backlog
User Interaction vs Privileges Required
Write access to a loaded settings location
If an attacker can write to a directory JupyterLab loads settings from (e.g. on shared or multi-tenant file system), they could place a crafted overrides.json that is applied to another user automatically at startup. This requires high privilages but no action by the victim.
User-imported settings file
A user can import a crafted overrides.json through the Import button in the Settings Editor, having received it from another party. This requires no privileges but a deliberate action by the victim, who reasonably expects a settings file to change preferences rather than run code.
Workarounds
None
Hardening
- Treat a settings file as something that can affect how JupyterLab behaves, not only how it appears. Administrators are encouraged to establish a trusted process for distributing configuration rather than relying on ad-hoc importing of shared files.
- On multi-tenant or shared file systems, restrict write permissions on the application settings directory and other Jupyter configuration paths so that one user cannot place an
overrides.json(or other configuration) readable by another user. A settings file in these locations is applied automatically, without an import step, so directory permissions are the primary control against cross-user tampering.
Impact
When a malicious settings file is applied, the embedded code runs with the same access as the affected user. This could allow an attacker to read or modify that user's notebooks and files, and to run code on the user's behalf through the notebook server, including on any connected kernel.
Untrusted input is rendered as active markup in a victim's browser, which can run script in their session. Typical impact: session or credential theft, and actions taken as the user.
Affected versions
Security releases
Kodem intelligence
Severity tells you how bad this could be in the worst case. It does not tell you whether you are exposed. Exploitability and impact are functions of runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A vulnerable package can sit in your dependency tree and never run.
Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter. Kodem's runtime-powered SCA identifies whether this CVE is reachable in your applications.
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JupyterLab 4.6.2 and 4.5.10 were patched.
Frequently Asked Questions
- What is GHSA-PPPJ-HQ3G-57PJ? GHSA-PPPJ-HQ3G-57PJ is a high-severity cross-site scripting (XSS) vulnerability in jupyterlab (pip), affecting versions >= 4.6.0, <= 4.6.1. It is fixed in 4.6.2, 4.5.10. Untrusted input is rendered as active markup in a victim's browser, which can run script in their session.
- Which versions of jupyterlab are affected by GHSA-PPPJ-HQ3G-57PJ? jupyterlab (pip) versions >= 4.6.0, <= 4.6.1 is affected.
- Is there a fix for GHSA-PPPJ-HQ3G-57PJ? Yes. GHSA-PPPJ-HQ3G-57PJ is fixed in 4.6.2, 4.5.10. Upgrade to this version or later.
- Is GHSA-PPPJ-HQ3G-57PJ exploitable, and should I be worried? Whether GHSA-PPPJ-HQ3G-57PJ is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
- What actually determines whether GHSA-PPPJ-HQ3G-57PJ is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
- How do I fix GHSA-PPPJ-HQ3G-57PJ?
- Upgrade
jupyterlabto 4.6.2 or later - Upgrade
jupyterlabto 4.5.10 or later
- Upgrade