GHSA-89vp-jrxv-24w8: Jupyter — MEDIUM

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...

Full CISO analysis pending enrichment.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
Jupyter	pip	>= 4.6.0, <= 4.6.1	`4.6.2`
13.2K OpenSSF 5.8 1.9K dependents Pushed 7d ago 56% patched ~28d to patch Full package profile →

Do you use Jupyter? You're affected.

How severe is it?

CVSS 3.1

N/A

EPSS

N/A

Exploitation Status

No known exploitation

Sophistication

N/A

What should I do?

Patch available

Update Jupyter to version 4.6.2

Which compliance frameworks are affected?

Compliance analysis pending. Sign in for full compliance mapping when available.

Frequently Asked Questions

What is GHSA-89vp-jrxv-24w8?

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`](https://github.com/jupyterlab/jupyterlab/releases/tag/v4.6.2) and [`v4.5.10`](https://github.com/jupyterlab/jupyterlab/releases/tag/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: ```bash --LabApp.extension_manager=readonly ``` or the following traitlet: ```python 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" />

Is GHSA-89vp-jrxv-24w8 actively exploited?

No confirmed active exploitation of GHSA-89vp-jrxv-24w8 has been reported, but organizations should still patch proactively.

How to fix GHSA-89vp-jrxv-24w8?

Update to patched version: Jupyter 4.6.2.

What is the CVSS score for GHSA-89vp-jrxv-24w8?

No CVSS score has been assigned yet.

What are the technical details?

Original Advisory

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`](https://github.com/jupyterlab/jupyterlab/releases/tag/v4.6.2) and [`v4.5.10`](https://github.com/jupyterlab/jupyterlab/releases/tag/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: ```bash --LabApp.extension_manager=readonly ``` or the following traitlet: ```python 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" />

Weaknesses (CWE)

CWE-178 Improper Handling of Case Sensitivity Primary CWE-180 Incorrect Behavior Order: Validate Before Canonicalize Primary

CWE-178 — Improper Handling of Case Sensitivity: The product does not properly account for differences in case sensitivity when accessing or determining the properties of a resource, leading to inconsistent results.

[Architecture and Design] Avoid making decisions based on names of resources (e.g. files) if those resources can have alternate names.
[Implementation] Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylis

Source: MITRE CWE corpus.