GSD-2020-2123
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
Jenkins RadarGun Plugin 1.7 and earlier does not configure its YAML parser to prevent the instantiation of arbitrary types, resulting in a remote code execution vulnerability.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2020-2123",
"description": "Jenkins RadarGun Plugin 1.7 and earlier does not configure its YAML parser to prevent the instantiation of arbitrary types, resulting in a remote code execution vulnerability.",
"id": "GSD-2020-2123"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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],
"details": "Jenkins RadarGun Plugin 1.7 and earlier does not configure its YAML parser to prevent the instantiation of arbitrary types, resulting in a remote code execution vulnerability.",
"id": "GSD-2020-2123",
"modified": "2023-12-13T01:21:50.915811Z",
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"name": "https://jenkins.io/security/advisory/2020-02-12/#SECURITY-1733",
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},
"gitlab.com": {
"advisories": [
{
"affected_range": "(,1.7]",
"affected_versions": "All versions up to 1.7",
"cvss_v2": "AV:N/AC:L/Au:S/C:P/I:P/A:P",
"cvss_v3": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"cwe_ids": [
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"CWE-502",
"CWE-937"
],
"date": "2020-02-14",
"description": "Jenkins RadarGun Plugin does not configure its YAML parser to prevent the instantiation of arbitrary types, resulting in a remote code execution vulnerability.",
"fixed_versions": [],
"identifier": "CVE-2020-2123",
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"not_impacted": "",
"package_slug": "maven/org.jenkins-ci.plugins/radargun",
"pubdate": "2020-02-12",
"solution": "Unfortunately, there is no solution available yet.",
"title": "Deserialization of Untrusted Data",
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"https://jenkins.io/security/advisory/2020-02-12/#SECURITY-1733"
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{
"name": "[oss-security] 20200212 Multiple vulnerabilities in Jenkins plugins",
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},
"impact": {
"baseMetricV2": {
"acInsufInfo": false,
"cvssV2": {
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"accessVector": "NETWORK",
"authentication": "SINGLE",
"availabilityImpact": "PARTIAL",
"baseScore": 6.5,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:L/Au:S/C:P/I:P/A:P",
"version": "2.0"
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"exploitabilityScore": 8.0,
"impactScore": 6.4,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
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"severity": "MEDIUM",
"userInteractionRequired": false
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"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
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},
"lastModifiedDate": "2023-10-25T18:16Z",
"publishedDate": "2020-02-12T15:15Z"
}
}
}
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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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