GSD-2020-2316
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
Jenkins Static Analysis Utilities Plugin 1.96 and earlier does not escape the annotation message in tooltips, resulting in a stored cross-site scripting (XSS) vulnerability exploitable by attackers with Job/Configure permission.
Aliases
Aliases
{
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"alias": "CVE-2020-2316",
"description": "Jenkins Static Analysis Utilities Plugin 1.96 and earlier does not escape the annotation message in tooltips, resulting in a stored cross-site scripting (XSS) vulnerability exploitable by attackers with Job/Configure permission.",
"id": "GSD-2020-2316"
},
"gsd": {
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"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
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"id": "GSD-2020-2316",
"modified": "2023-12-13T01:21:51.231733Z",
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"name": "https://www.jenkins.io/security/advisory/2020-11-04/#SECURITY-1907",
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"cwe_ids": [
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"date": "2022-12-22",
"description": "Jenkins Static Analysis Utilities Plugin 1.96 and earlier does not escape the annotation message in tooltips, resulting in a stored cross-site scripting (XSS) vulnerability exploitable by attackers with Job/Configure permission.",
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"identifier": "CVE-2020-2316",
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"package_slug": "maven/org.jvnet.hudson.plugins/analysis-core",
"pubdate": "2022-05-24",
"solution": "Unfortunately, there is no solution available yet.",
"title": "Improper Neutralization of Input During Web Page Generation (\u0027Cross-site Scripting\u0027)",
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"impact": {
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"cvssV2": {
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"accessVector": "NETWORK",
"authentication": "SINGLE",
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"lastModifiedDate": "2023-11-02T22:10Z",
"publishedDate": "2020-11-04T15:15Z"
}
}
}
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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.
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- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
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- 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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