CVE-2021-41134 (GCVE-0-2021-41134)
Vulnerability from cvelistv5 – Published: 2021-11-03 17:50 – Updated: 2024-08-04 02:59
VLAI
EPSS
VEX
Title
Stored XSS in Jupyter nbdime
Summary
nbdime provides tools for diffing and merging of Jupyter Notebooks. In affected versions a stored cross-site scripting (XSS) issue exists within the Jupyter-owned nbdime project. It appears that when reading the file name and path from disk, the extension does not sanitize the string it constructs before returning it to be displayed. The diffNotebookCheckpoint function within nbdime causes this issue. When attempting to display the name of the local notebook (diffNotebookCheckpoint), nbdime appears to simply append .ipynb to the name of the input file. The NbdimeWidget is then created, and the base string is passed through to the request API function. From there, the frontend simply renders the HTML tag and anything along with it. Users are advised to patch to the most recent version of the affected product.
Severity
8.7 (High)
CWE
- CWE-79 - Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/jupyter/nbdime/security/adviso… | x_refsource_CONFIRM |
| https://github.com/jupyter/nbdime/commit/e44a5cc7… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| jupyter | nbdime |
Affected:
< 1.1.1 - nbdime (pip)
Affected: >= 2.0.0 , < 2.1.1 - nbdime (pip) Affected: >= 3.0.0, < 3.1.1 - nbdime (pip) Affected: < 5.0.2 - nbdime (npm) Affected: >= 6.0.0, < 6.1.2 - nbdime (npm) Affected: < 1.0.1 - nbdime-jupyterlab (npm) Affected: >= 2.0.0, < 2.1.1 - nbdime-jupyterlab (npm) |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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