CVE-2019-10109 (GCVE-0-2019-10109)
Vulnerability from cvelistv5 – Published: 2019-05-15 19:14 – Updated: 2024-08-04 22:10
VLAI
EPSS
VEX
Summary
An Information Exposure issue (issue 1 of 2) was discovered in GitLab Community and Enterprise Edition before 11.7.8, 11.8.x before 11.8.4, and 11.9.x before 11.9.2. EXIF geolocation data were not removed from images when uploaded to GitLab. As a result, anyone with access to the uploaded image could obtain its geolocation, device, and software version data (if present).
Severity
5.3 (Medium)
CWE
- n/a
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://about.gitlab.com/blog/categories/releases/ | x_refsource_MISC |
| https://about.gitlab.com/2019/04/01/security-rele… | x_refsource_MISC |
| https://gitlab.com/gitlab-org/gitlab-ce/issues/55469 | x_refsource_MISC |
| https://gitlab.com/gitlab-org/gitlab-ce/issues/54220 | x_refsource_MISC |
Date Public
2018-11-19 00:00
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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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