GSD-2022-43409
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
Jenkins Pipeline: Supporting APIs Plugin 838.va_3a_087b_4055b and earlier does not sanitize or properly encode URLs of hyperlinks sending POST requests in build logs, resulting in a stored cross-site scripting (XSS) vulnerability exploitable by attackers able to create Pipelines.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-43409",
"id": "GSD-2022-43409",
"references": [
"https://access.redhat.com/errata/RHSA-2023:0560",
"https://access.redhat.com/errata/RHSA-2023:0777",
"https://access.redhat.com/errata/RHSA-2023:1064"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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],
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"id": "GSD-2022-43409",
"modified": "2023-12-13T01:19:31.890801Z",
"schema_version": "1.4.0"
}
},
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"affects": {
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"product": {
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"gitlab.com": {
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"affected_range": "(,839.v35e2736cfd5c)",
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"cwe_ids": [
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"date": "2022-10-20",
"description": "Jenkins Pipeline: Supporting APIs Plugin 838.va_3a_087b_4055b and earlier does not sanitize or properly encode URLs of hyperlinks sending POST requests in build logs, resulting in a stored cross-site scripting (XSS) vulnerability exploitable by attackers able to create Pipelines.",
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"identifiers": [
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"package_slug": "maven/org.jenkins-ci.plugins.workflow/workflow-support",
"pubdate": "2022-10-19",
"solution": "Upgrade to version 839.v35e2736cfd5c or above.",
"title": "Improper Neutralization of Input During Web Page Generation (\u0027Cross-site Scripting\u0027)",
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"impact": {
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"cvssV3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 5.4,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.3,
"impactScore": 2.7
}
},
"lastModifiedDate": "2023-11-01T20:54Z",
"publishedDate": "2022-10-19T16:15Z"
}
}
}
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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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