### 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 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](https://github.com/jupyterhub/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](https://github.com/jupyterhub/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.
### 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 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](https://github.com/jupyterhub/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](https://github.com/jupyterhub/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.
Update distributed to 2026.1.0 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanDask Distributed is Vulnerable to Remote Code Execution via Jupyter Proxy and Dashboard affects distributed (pip). Severity is medium. ### 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 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](https://github.com/jupyterhub/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](https://github.com/jupyterhub/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.
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| distributedpip | <2026.1.0 | 2026.1.0 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL GuardUpdate distributed to 2026.1.0 if you use the affected versions. Test the change in a non-production environment first.
Local check
hol-guard supply-chain scanDask Distributed is Vulnerable to Remote Code Execution via Jupyter Proxy and Dashboard affects distributed (pip). Severity is medium. ### 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 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](https://github.com/jupyterhub/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](https://github.com/jupyterhub/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.
AI coding agents often install or upgrade packages automatically in pip. A medium vulnerability in a dependency can be pulled into a project through a normal install or update without a human reviewing the change, expanding the blast radius from a single package to every agent workspace that depends on it.
| Package | Affected range | Fixed version |
|---|---|---|
| distributedpip | <2026.1.0 | 2026.1.0 |
Fixed versions are reported by the source feed; confirm compatibility before updating.
Reported by GitHub Security Advisories (ghsa).
HOL Guard can help your team review package activity against supported protection paths.
Explore HOL Guard