CVE-2022-20619 (GCVE-0-2022-20619)
Vulnerability from cvelistv5 – Published: 2022-01-12 19:05 – Updated: 2024-08-03 02:17
VLAI
EPSS
VEX
Summary
A cross-site request forgery (CSRF) vulnerability in Jenkins Bitbucket Branch Source Plugin 737.vdf9dc06105be and earlier allows attackers to connect to an attacker-specified URL using attacker-specified credentials IDs obtained through another method, capturing credentials stored in Jenkins.
Severity
7.1 (High)
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://www.jenkins.io/security/advisory/2022-01-… | x_refsource_CONFIRM |
| http://www.openwall.com/lists/oss-security/2022/01/12/6 | mailing-listx_refsource_MLIST |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Jenkins project | Jenkins Bitbucket Branch Source Plugin |
Affected:
unspecified , ≤ 737.vdf9dc06105be
(custom)
Unaffected: 725.vd9f8be0fa250 Unaffected: 2.9.11.2 Unaffected: 2.9.7.2 |
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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