CVE-2023-3500 (GCVE-0-2023-3500)
Vulnerability from cvelistv5 – Published: 2023-08-02 00:07 – Updated: 2025-11-20 04:07
VLAI
EPSS
VEX
Title
Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') in GitLab
Summary
An issue has been discovered in GitLab CE/EE affecting all versions starting from 10.0 before 16.0.8, all versions starting from 16.1 before 16.1.3, all versions starting from 16.2 before 16.2.2. A reflected XSS was possible when creating specific PlantUML diagrams that allowed the attacker to perform arbitrary actions on behalf of victims.
Severity
4.8 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-07-24 14:25 UTC
CWE
- CWE-79 - Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://gitlab.com/gitlab-org/gitlab/-/issues/416902 | issue-trackingpermissions-required |
| https://hackerone.com/reports/2010926 | technical-descriptionexploitpermissions-required |
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"orgId": "ceab7361-8a18-47b1-92ba-4d7d25f6715a",
"shortName": "GitLab"
},
"references": [
{
"name": "GitLab Issue #416902",
"tags": [
"issue-tracking",
"permissions-required"
],
"url": "https://gitlab.com/gitlab-org/gitlab/-/issues/416902"
},
{
"name": "HackerOne Bug Bounty Report #2010926",
"tags": [
"technical-description",
"exploit",
"permissions-required"
],
"url": "https://hackerone.com/reports/2010926"
}
],
"solutions": [
{
"lang": "en",
"value": "Upgrade to versions 16.2.2, 16.1.3, 16.0.8 or above."
}
],
"title": "Improper Neutralization of Input During Web Page Generation (\u0027Cross-site Scripting\u0027) in GitLab"
}
},
"cveMetadata": {
"assignerOrgId": "ceab7361-8a18-47b1-92ba-4d7d25f6715a",
"assignerShortName": "GitLab",
"cveId": "CVE-2023-3500",
"datePublished": "2023-08-02T00:07:15.239Z",
"dateReserved": "2023-07-04T09:22:12.252Z",
"dateUpdated": "2025-11-20T04:07:53.323Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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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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