GSD-2022-36914
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
Jenkins Files Found Trigger Plugin 1.5 and earlier does not perform a permission check in a method implementing form validation, allowing attackers with Overall/Read permission to check for the existence of an attacker-specified file path on the Jenkins controller file system.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-36914",
"description": "Jenkins Files Found Trigger Plugin 1.5 and earlier does not perform a permission check in a method implementing form validation, allowing attackers with Overall/Read permission to check for the existence of an attacker-specified file path on the Jenkins controller file system.",
"id": "GSD-2022-36914"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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],
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"id": "GSD-2022-36914",
"modified": "2023-12-13T01:19:21.697475Z",
"schema_version": "1.4.0"
}
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"name": "https://www.jenkins.io/security/advisory/2022-07-27/#SECURITY-2210",
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"cwe_ids": [
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"CWE-862",
"CWE-937"
],
"date": "2022-08-11",
"description": "Jenkins Files Found Trigger Plugin 1.5 and earlier does not perform a permission check in a method implementing form validation, allowing attackers with Overall/Read permission to check for the existence of an attacker-specified file path on the Jenkins controller file system.",
"fixed_versions": [],
"identifier": "CVE-2022-36914",
"identifiers": [
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"not_impacted": "",
"package_slug": "maven/org.jenkins-ci.plugins/files-found-trigger",
"pubdate": "2022-07-28",
"solution": "Unfortunately, there is no solution available yet.",
"title": "Missing Authorization",
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"impact": {
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"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 4.3,
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"confidentialityImpact": "LOW",
"integrityImpact": "NONE",
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"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N",
"version": "3.1"
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"exploitabilityScore": 2.8,
"impactScore": 1.4
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},
"lastModifiedDate": "2023-11-22T21:16Z",
"publishedDate": "2022-07-27T15:15Z"
}
}
}
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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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