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🐍 PyPI

GHSA-89vp-jrxv-24w8

JupyterLab: PyPI extension blocklist package-name canonicalization bypass

Published
Jul 22, 2026
Updated
Jul 22, 2026
Affected
2 pkgs
Patched
2 / 2
Exploits
None indexed

Blast Radius

2 pkgs affected
🐍jupyterlab🐍jupyterlab

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

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)

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.

Patches

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.

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:

<img width="293" height="293" alt="image" src="https://github.com/user-attachments/assets/8016c809-633e-4ed0-a5bc-6bc4793caa0f" />

Affected Packages

2 total 2 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIjupyterlab4.6.0&&< 4.6.24.6.2
🐍PyPIjupyterlab4.5.0&&< 4.5.104.5.10

Detection & mitigation playbook

Open-source dependency
  1. Detect

    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.

  2. Fix

    Update jupyterlab to 4.6.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-89vp-jrxv-24w8 is resolved across your whole dependency graph.

  3. 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.

  4. How O3 protects you

    O3 pinpoints whether GHSA-89vp-jrxv-24w8 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-89vp-jrxv-24w8. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

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
O3 Security · Impact-Aware SCA

Is GHSA-89vp-jrxv-24w8 in your dependencies?

O3 detects GHSA-89vp-jrxv-24w8 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.