How severe is GHSA-c336-7962-wfj2?

No CVSS score has been assigned to GHSA-c336-7962-wfj2 yet. Review the advisory details and affected package list to assess your exposure.

Which packages are affected by GHSA-c336-7962-wfj2?

GHSA-c336-7962-wfj2 affects the following packages: distributed (PyPI). Ecosystems affected: PyPI.

How do I fix GHSA-c336-7962-wfj2?

Update distributed to 2026.1.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-c336-7962-wfj2 is resolved across your whole dependency graph.

How do I detect GHSA-c336-7962-wfj2 in my PyPI dependencies?

Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for distributed, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

How do I mitigate GHSA-c336-7962-wfj2 if there is no patch (or I can't update yet)?

Escape or sanitise the affected output on the server side rather than relying on client-side filtering, and add a Content-Security-Policy that blocks inline script execution so injected markup cannot run even if it reaches the page.

Is GHSA-c336-7962-wfj2 actively exploited in the wild?

No public exploit code has been indexed for GHSA-c336-7962-wfj2 yet. This does not mean the vulnerability cannot be exploited — absence of public exploits does not imply safety. Apply the recommended fix and use O3 Security to monitor your exposure.

What is the EPSS score for GHSA-c336-7962-wfj2?

GHSA-c336-7962-wfj2 has an EPSS (Exploit Prediction Scoring System) score of 0.2%, placing it in the 15th percentile of all CVEs. EPSS is maintained by FIRST.org and estimates the probability that a vulnerability will be exploited in the wild within the next 30 days. This score indicates relatively lower exploitation probability, though the CVSS severity should still guide your patching priority.

What type of vulnerability is GHSA-c336-7962-wfj2?

GHSA-c336-7962-wfj2 is classified as Cross-site Scripting (XSS) (CWE-79), CWE-80 (CWE-80), CWE-250 (CWE-250). These weakness types describe the underlying flaw category, which helps determine the potential impact and the right class of mitigation. This is a high-impact weakness class that often enables remote code execution or data exposure.

When was GHSA-c336-7962-wfj2 published, and has it been updated?

GHSA-c336-7962-wfj2 was published on January 16, 2026 and was last updated on September 10, 2026. Advisory data evolves as severity scores, affected ranges, and exploit intelligence are revised — always check the latest version of the advisory before acting.

🐍 PyPI

Not in CISA KEV

Dask Distributed is Vulnerable to Remote Code Execution via Jupyter Proxy and DashboardGHSA-c336-7962-wfj2

Fix: dask/distributed@ab72092

GHSA-c336-7962-wfj2 is a Cross-site Scripting (XSS) vulnerability in distributed. A fix is available for distributed — see the affected versions and patch details below.

Also known asCVE-2026-23528PYSEC-2026-169

Published

Jan 16, 2026

Updated

Sep 10, 2026

Affected

1 pkg

Patched

1 / 1

Exploits

None indexed

Exploitation data as of Nov 10, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.

Exploitation and automatability from CISA’s SSVC triage for GHSA-c336-7962-wfj2.

EPSS Exploitation Probability

via FIRST.org ↗

0.2%probability of exploitation in next 30 days

Lower Risk+0.00%

Lower risk than most CVEs15th percentile — riskier than 15% of all scored CVEsHighest risk

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

Real-World Exposure

1 pkg affected

🐍distributed

Real-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

When Jupyter Lab, jupyter-server-proxy and Dask distributed are all run together it is possible to craft a URL which will result in code being executed by Jupyter due to a cross-side-scripting (XSS) bug in the Dask dashboard.

It is possible for attackers to craft a phishing URL that assumes Jupyter Lab and Dask may be running on localhost and using default ports. If a user clicks on the malicious link it will open an error page in the Dask Dashboard via the Jupyter Lab proxy which will cause code to be executed by the default Jupyter Python kernel.

In order for a user to be impacted they must be running Jupyter Lab locally on the default port (with the jupyter-server-proxy) and a Dask distributed cluster on the default port. Then they would need to click the link which would execute the malicious code.

Patches

This has been fixed in the 2026.1.0 release. All users should upgrade to this version.

Mitigations

There are no known workarounds for this bug. The only complete solution is to upgrade to a newer release of Dask. However, there are a few things you could do to reduce your risk.

It is possible to avoid code execution via Jupyter by uninstalling the jupyter-server-proxy and accessing the Dask dashboard directly at it's URL. However, it is still possible for an attacker to craft a URL that executes JavaScript in the user's browser in the Dask dashboard. Which is still a moderate vulnerability. Therefore we recommend all users upgrade to the latest Dask release.

Another potential mitigation is to ensure both Jupyter and the Dask dashboard are running on non-standard ports. While this doesn't resolve the problem it reduces the chance of this being exploited. If an attacker knew which ports you were using they could still craft a malicious URL, but it would require a more targeted attack.

Affected Packages

1 total 1 fixed

Ecosystem	Package	Vulnerable range	Fix
🐍PyPI	`distributed`	all versions	2026.1.0`pip install --upgrade 'distributed==2026.1.0'`

Affected Products

1 product · 1 configurations

Application

daskanaconda

< 2026.1.0

range

Detection & mitigation playbook

Open-source dependency

Detect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for distributed, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update distributed to 2026.1.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-c336-7962-wfj2 is resolved across your whole dependency graph.
Workarounds
Escape or sanitise the affected output on the server side rather than relying on client-side filtering, and add a Content-Security-Policy that blocks inline script execution so injected markup cannot run even if it reaches the page.

Frequently Asked Questions

### Impact When [Jupyter Lab](https://jupyterlab.readthedocs.io/en/latest/), [jupyter-server-proxy](https://github.com/jupyterhub/jupyter-server-proxy) and [Dask distributed](https://github.com/dask/distributed) are all run together it is possible to craft a URL which will result in code being executed by Jupyter due to a cross-side-scripting (XSS) bug in the Dask dashboard. It is possible for attackers to craft a phishing URL that assumes Jupyter Lab and Dask may be running on localhost and using default ports. If a user clicks on the malicious link it will open an error page in the Dask Das