jupyterlab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. From 4.5.0 until 4.5.10 and 4.6.2, in jupyterlab/extensions/manager.py and jupyterlab/extensions/pypi.py, JupyterLabs PyPI extension manager enforces blocked_extensions_uris by comparing requested install names to blocklist entries with custom 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, and JupyterLab accepts the install request even though pip resolves the variant to the same package. Security impact requires an allowlist or blocklist intended to restrict package installation, the PyPI Extension Manager, and kernels and terminals that are disabled or delegated to remote hosts. The bypass lets an authenticated user install a prohibited extension, defeat integrity restrictions, and affect availability without gaining new read access. This issue is fixed in versions 4.5.10 and 4.6.2.
Weakness
The product does not properly account for differences in case sensitivity when accessing or determining the properties of a resource, leading to inconsistent results.
Affected Software
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Jupyterlab | Ubuntu | upstream | * |
Extended Description
Improperly handled case sensitive data can lead to several possible consequences, including:
Potential Mitigations
- 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, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
References
- https://github.com/jupyterlab/jupyterlab/commit/9365f020baec5221deaf11535ed554c06637c999
- https://github.com/jupyterlab/jupyterlab/commit/be9303f5bcd5308eaeae953c5a3c903046682c2c
- https://github.com/jupyterlab/jupyterlab/commit/f1beab4a2027af4719d6edc07d52d6cf5a39a432
- https://github.com/jupyterlab/jupyterlab/pull/19184
- https://github.com/jupyterlab/jupyterlab/pull/19185
- https://github.com/jupyterlab/jupyterlab/pull/19186
- https://github.com/jupyterlab/jupyterlab/releases/tag/v4.5.10
- https://github.com/jupyterlab/jupyterlab/releases/tag/v4.6.2
- https://github.com/jupyterlab/jupyterlab/releases/tag/v4.7.0a1
- https://github.com/jupyterlab/jupyterlab/security/advisories/GHSA-89vp-jrxv-24w8