CVE-2026-102832 (GCVE-0-2026-102832)
Vulnerability from cvelistv5 – Published: 2026-10-13 15:19 – Updated: 2026-10-13 16:09
VLAI
EPSS
VEX
Title
JupyterLab: Command link trust prompt in JupyterLab can trust a document other than the one clicked
Summary
JupyterLab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. From 4.5.7 until 4.5.11 and 4.6.4, the markdownviewer:trust and notebook:trust command-link handlers use tracker.currentWidget to resolve the current Markdown viewer or notebook when the user answers rather than retaining the document whose command button was clicked. If another document becomes current while the prompt is open and the user chooses Trust, JupyterLab marks the second document trusted while leaving the clicked document untrusted. Later command buttons in the incorrectly trusted document run without a prompt, and trusting a notebook marks all cells trusted, saves unsaved edits, and persists the server signature to disk. Applications that bundle JupyterLab, including affected Notebook and JupyterLite releases, inherit the behavior. This issue is fixed in versions 4.5.11 and 4.6.4.
Severity
4.2 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-10-13 16:08 UTC
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/jupyterlab/jupyterlab/security… | x_refsource_CONFIRM |
| https://github.com/jupyterlab/jupyterlab/commit/0… | x_refsource_MISC |
| https://github.com/jupyterlab/jupyterlab/commit/b… | 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
3 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| jupyterlab | jupyterlab |
Affected:
>= 4.5.7, < 4.5.11
Affected: >= 4.6.0, < 4.6.4 |
guessed | |
| jupyterlab | jupyterlite-core |
Affected:
>= 0.7.5, < 0.8.4
|
guessed | |
| jupyterlab | notebook |
Affected:
>= 7.5.6, < 7.6.3
|
guessed |
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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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