CVE-2026-102830 (GCVE-0-2026-102830)
Vulnerability from cvelistv5 – Published: 2026-09-29 18:53 – Updated: 2026-09-29 20:00
VLAI
EPSS
VEX
Title
JupyterLab: Cross-site scripting (XSS) in JupyterLab via crafted language package (jupyterlab.json)
Summary
JupyterLab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. From JupyterLab 3.0.0 until 4.5.11 and 4.6.4, and in JupyterLite Core 0.8.3 and earlier, the Plural-Forms header in a selected third-party language pack can append JavaScript after a valid plural rule because prefix-only regular-expression validation accepts a matching prefix without requiring the entire header to match. JupyterLab passes the accepted expression to new Function, so loading the catalogue and translating a plural string executes the appended code in the authenticated JupyterLab origin. Where Jupyter Server kernels, terminals, and APIs are exposed, the code can use authenticated server APIs to read or modify files and run code. Impact is much more limited in JupyterLite because it typically lacks most exposed Jupyter Server surfaces. The default English locale is unaffected because it does not load a translation catalogue. This issue is fixed in JupyterLab 4.5.11 and 4.6.4 and JupyterLite Core 0.8.4.
Severity
6.8 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-29 20:00 UTC
CWE
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/jupyterlab/jupyterlab/security… | x_refsource_CONFIRM |
| https://github.com/jupyterlab/jupyterlab/commit/9… | x_refsource_MISC |
| https://github.com/jupyterlab/jupyterlab/commit/d… | x_refsource_MISC |
| https://github.com/jupyterlab/jupyterlab/commit/f… | x_refsource_MISC |
| https://github.com/jupyterlab/jupyterlab/releases… | x_refsource_MISC |
| https://github.com/jupyterlab/jupyterlab/releases… | x_refsource_MISC |
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| jupyterlab | jupyterlab |
Affected:
>= 3.0.0, < 4.5.11
Affected: >= 4.6.0, < 4.6.4 |
guessed | |
| jupyterlab | jupyterlite-core |
Affected:
< 0.8.4
|
guessed |
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"discovery": "UNKNOWN"
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"title": "JupyterLab: Cross-site scripting (XSS) in JupyterLab via crafted language package (jupyterlab.json)"
}
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}
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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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