GSD-2022-36663
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
Gluu Oxauth before v4.4.1 allows attackers to execute blind SSRF (Server-Side Request Forgery) attacks via a crafted request_uri parameter.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-36663",
"description": "Gluu Oxauth before v4.4.1 allows attackers to execute blind SSRF (Server-Side Request Forgery) attacks via a crafted request_uri parameter.",
"id": "GSD-2022-36663"
},
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"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
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"details": "Gluu Oxauth before v4.4.1 allows attackers to execute blind SSRF (Server-Side Request Forgery) attacks via a crafted request_uri parameter.",
"id": "GSD-2022-36663",
"modified": "2023-12-13T01:19:21.425129Z",
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"cvss_v3": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"cwe_ids": [
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"CWE-918",
"CWE-937"
],
"date": "2022-09-16",
"description": "Gluu Oxauth before v4.4.1 allows attackers to execute blind SSRF (Server-Side Request Forgery) attacks via a crafted request_uri parameter.",
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"identifier": "CVE-2022-36663",
"identifiers": [
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],
"not_impacted": "All versions starting from 4.4.1",
"package_slug": "maven/org.gluu/oxauth-common",
"pubdate": "2022-09-07",
"solution": "Upgrade to version 4.4.1 or above.",
"title": "Server-Side Request Forgery (SSRF)",
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"https://github.com/GluuFederation/oxAuth/releases/tag/4.4.1",
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},
"impact": {
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 9.8,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 5.9
}
},
"lastModifiedDate": "2022-09-09T18:41Z",
"publishedDate": "2022-09-06T21:15Z"
}
}
}
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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 |
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Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
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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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