GHSA-9q39-rmj3-p4r2
HIGHHTML injection in Jupyter Notebook and JupyterLab leading to DOM Clobbering
EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
Blast Radius
jupyterlab🐍notebook🐍jupyterlabReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
Impact
The 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.
Patches
JupyterLab v3.6.8, v4.2.5 and Jupyter Notebook v7.2.2 were patched.
Workarounds
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:
@jupyterlab/mathjax-extension:plugin- users will loose ability to preview mathematical equations@jupyterlab/markdownviewer-extension:plugin- users will loose ability to open Markdown previews@jupyterlab/mathjax2-extension:plugin(if installed with optionaljupyterlab-mathjax2package) - 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
To confirm that the plugins were disabled run:
jupyter labextension list
References
None
Notes
This change has a potential to break rendering of some markdown. There is a setting in Sanitizer which allows to revert to the previous sanitizer settings (allowNamedProperties).
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | jupyterlab | all versions | 3.6.8 |
| 🐍PyPI | notebook | ≥ 7.0.0&&< 7.2.2 | 7.2.2 |
| 🐍PyPI | jupyterlab | ≥ 4.0.0&&< 4.2.5 | 4.2.5 |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for jupyterlab. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update jupyterlab to 3.6.8 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-9q39-rmj3-p4r2 is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether GHSA-9q39-rmj3-p4r2 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to GHSA-9q39-rmj3-p4r2. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-9q39-rmj3-p4r2 in your dependencies?
O3 detects GHSA-9q39-rmj3-p4r2 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.