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CVE-2025-7739 (GCVE-0-2025-7739)

Vulnerability from cvelistv5 – Published: 2025-08-13 17:26 – Updated: 2025-08-13 20:36
VLAI
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 from 18.2 before 18.2.2 that, under certain conditions, could have allowed authenticated users to achieve stored cross-site scripting by injecting malicious HTML content in scoped label descriptions.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-08-13 20:36 UTC
CWE
  • CWE-79 - Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
References
URL Tags
https://gitlab.com/gitlab-org/gitlab/-/issues/556111 issue-trackingpermissions-required
https://hackerone.com/reports/3255849 technical-descriptionexploitpermissions-required
Impacted products
Vendor Product Version CPE status
GitLab GitLab Affected: 18.2 , < 18.2.2 (semver)
    cpe:2.3:a:gitlab:gitlab:*:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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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

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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.
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  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
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  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

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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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