Summary
JupyterLab: PyPI extension blocklist package-name canonicalization bypass
JupyterLab's PyPI extension manager enforces blocked_extensions_uris by comparing the requested install name to blocklist entries with a custom string normalization that is weaker than PyPI package-name canonicalization. An authenticated user can request a PyPI-equivalent spelling such as JupyterLab.Git for a blocklisted package such as jupyterlab-git; JupyterLab accepts the install request even though pip resolves the variant to the same package.
This has security implications only for deployments that combine all of the following:
- an allowlist/blocklist configured with the intent of restricting which packages users can install;
- the (default) PyPI Extension Manager enabled; and
- kernels and terminals disabled or delegated to remote hosts (otherwise a user with kernel access can install packages directly regardless of this check)
Workarounds
No action is required for deployments that do not have a custom allow/block list configured. Deployments wanting to disable programmatic extension installation entirely can switch to the read-only extension manager:
--LabApp.extension_manager=readonly
or the following traitlet:
c.LabApp.extension_manager = 'readonly'
You can confirm that the read-only manager is in use from GUI:
Impact
The vulnerability lets an authenticated user install a package the operator specifically intended to block, defeating the allowlist/blocklist control. Because extensions in principle allow for arbitrary code execution, this vulnerability enables untrusted users to impact the integrity and availability of the jupyter-server instance that was provisioned to them. The user already has access to their own single-user server's data, so installing an extension grants no new read access.
In particular, the integrity of data can be impacted, and any hardening or restrictions on permitted user actions (download/upload limits) within the single-user server can be circumvented. Availability impact on a JupyterHub deployment is limited: while a user can be expected to exhaust their own kernel pod's resources, this vulnerability makes it easier to also exhaust the single-user server resources or generate more requests to shared resources; where limits are absent, resource exhaustion could potentially degrade the wider deployment.
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 v4.6.2 and v4.5.10 contain the patch.
Users of applications that depend on JupyterLab, such as Notebook v7+, should update jupyterlab package too.
Frequently Asked Questions
- What is GHSA-89VP-JRXV-24W8? GHSA-89VP-JRXV-24W8 is a medium-severity security vulnerability in jupyterlab (pip), affecting versions >= 4.6.0, <= 4.6.1. It is fixed in 4.6.2, 4.5.10.
- Which versions of jupyterlab are affected by GHSA-89VP-JRXV-24W8? jupyterlab (pip) versions >= 4.6.0, <= 4.6.1 is affected.
- Is there a fix for GHSA-89VP-JRXV-24W8? Yes. GHSA-89VP-JRXV-24W8 is fixed in 4.6.2, 4.5.10. Upgrade to this version or later.
- Is GHSA-89VP-JRXV-24W8 exploitable, and should I be worried? Whether GHSA-89VP-JRXV-24W8 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-89VP-JRXV-24W8 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-89VP-JRXV-24W8?
- Upgrade
jupyterlabto 4.6.2 or later - Upgrade
jupyterlabto 4.5.10 or later
- Upgrade