GSD-2022-1162
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
A hardcoded password was set for accounts registered using an OmniAuth provider (e.g. OAuth, LDAP, SAML) in GitLab CE/EE versions 14.7 prior to 14.7.7, 14.8 prior to 14.8.5, and 14.9 prior to 14.9.2 allowing attackers to potentially take over accounts
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-1162",
"description": "A hardcoded password was set for accounts registered using an OmniAuth provider (e.g. OAuth, LDAP, SAML) in GitLab CE/EE versions 14.7 prior to 14.7.7, 14.8 prior to 14.8.5, and 14.9 prior to 14.9.2 allowing attackers to potentially take over accounts",
"id": "GSD-2022-1162",
"references": [
"https://packetstormsecurity.com/files/cve/CVE-2022-1162"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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],
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"id": "GSD-2022-1162",
"modified": "2023-12-13T01:19:28.558212Z",
"schema_version": "1.4.0"
}
},
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"affects": {
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}
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"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
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},
{
"name": "https://gitlab.com/gitlab-org/cves/-/blob/master/2022/CVE-2022-1162.json",
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},
{
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"refsource": "CONFIRM",
"tags": [
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"url": "https://gitlab.com/gitlab-org/cves/-/blob/master/2022/CVE-2022-1162.json"
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{
"name": "http://packetstormsecurity.com/files/166828/Gitlab-14.9-Authentication-Bypass.html",
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],
"url": "http://packetstormsecurity.com/files/166828/Gitlab-14.9-Authentication-Bypass.html"
}
]
}
},
"impact": {
"baseMetricV2": {
"acInsufInfo": false,
"cvssV2": {
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"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 7.5,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
"version": "2.0"
},
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"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "HIGH",
"userInteractionRequired": false
},
"baseMetricV3": {
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"version": "3.1"
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"exploitabilityScore": 3.9,
"impactScore": 5.9
}
},
"lastModifiedDate": "2022-04-27T20:35Z",
"publishedDate": "2022-04-04T20:15Z"
}
}
}
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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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