CVE-2021-22214 (GCVE-0-2021-22214)
Vulnerability from cvelistv5 – Published: 2021-06-08 14:59 – Updated: 2024-08-03 18:37Summary
When requests to the internal network for webhooks are enabled, a server-side request forgery vulnerability in GitLab CE/EE affecting all versions starting from 10.5 was possible to exploit for an unauthenticated attacker even on a GitLab instance where registration is limited
Severity
6.8 (Medium)
CWE
- Server-side request forgery (ssrf) in GitLab
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://gitlab.com/gitlab-org/gitlab/-/issues/322926 | x_refsource_MISC |
| https://hackerone.com/reports/1110131 | x_refsource_MISC |
| https://gitlab.com/gitlab-org/cves/-/blob/master/… | x_refsource_CONFIRM |
Impacted products
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 7e606498-eef5-4673-884c-5603bbeca58a
Exploited: Yes
Timestamps
First Seen: 2021-06-08
Asserted: 2021-06-08
Scope
Notes: When requests to the internal network for webhooks are enabled, a server-side request forgery vulnerability in GitLab CE/EE affecting all versions... | Affected: GitLab / GitLab | CVSS: 6.8 (MEDIUM) | EPSS: 0.27806 | Used in malware: unknown | Not yet in CISA KEV: True
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: previdian
Details
| Feed | Previdian (previdian.com) |
|---|---|
| Title | When requests to the internal network for webhooks are enabled, a server-side request forgery vulnerability in GitLab CE/EE affecting all versions... |
| Cve Id | CVE-2021-22214 |
| Vendor | GitLab |
| Ghsa Id | None |
| Product | GitLab |
| Added Date | 2021-06-08T14:59:37.000Z |
| Cvss Score | 6.8 |
| Epss Score | 0.27806 |
| Previous Ids | |
| Cvss Severity | MEDIUM |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.98039 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2021-22214 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-02 09:11 CEST
| Updated: 2026-10-02 09:11 CEST
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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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