FKIE_CVE-2022-38102
Vulnerability from fkie_nvd - Published: 2023-08-11 03:15 - Updated: 2026-06-17 04:56
Severity
7.2 (High) - CVSS:3.1/AV:L/AC:H/PR:H/UI:N/S:C/C:N/I:H/A:H
4.4 (Medium) - CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H
4.4 (Medium) - CVSS:3.1/
Summary
Improper Input validation in firmware for some Intel(R) Converged Security and Management Engine before versions 15.0.45, and 16.1.27 may allow a privileged user to potentially enable denial of service via local access.
References
Impacted products
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"criteria": "cpe:2.3:h:intel:qm580e:-:*:*:*:*:*:*:*",
"matchCriteriaId": "8CDD634A-B6BA-452C-A6E0-18ADEFF81529",
"vulnerable": false
},
{
"criteria": "cpe:2.3:h:intel:rm590e:-:*:*:*:*:*:*:*",
"matchCriteriaId": "E1C4E46E-5828-4EE6-9A95-8F4FE56AC156",
"vulnerable": false
},
{
"criteria": "cpe:2.3:h:intel:w580:-:*:*:*:*:*:*:*",
"matchCriteriaId": "EE366EB3-BE96-4A01-9BD5-792B59163938",
"vulnerable": false
},
{
"criteria": "cpe:2.3:h:intel:wm590:-:*:*:*:*:*:*:*",
"matchCriteriaId": "4158B9A6-ACBE-4A62-AA2C-9790F73C4C3C",
"vulnerable": false
},
{
"criteria": "cpe:2.3:h:intel:z590:-:*:*:*:*:*:*:*",
"matchCriteriaId": "89563A0F-CE7A-41C5-A8C7-9FC2D40F000A",
"vulnerable": false
}
],
"negate": false,
"operator": "OR"
}
],
"operator": "AND"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Improper Input validation in firmware for some Intel(R) Converged Security and Management Engine before versions 15.0.45, and 16.1.27 may allow a privileged user to potentially enable denial of service via local access."
},
{
"lang": "es",
"value": "La validaci\u00f3n de entrada inadecuada en el firmware de algunos Intel(R) Converged Security and Management Engine anteriores a las versiones 15.0.45 y 16.1.27 puede permitir que un usuario con privilegios habilite potencialmente la denegaci\u00f3n de servicio mediante acceso local."
}
],
"id": "CVE-2022-38102",
"lastModified": "2026-06-17T04:56:06.577",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "HIGH",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 7.2,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "HIGH",
"privilegesRequired": "HIGH",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:H/PR:H/UI:N/S:C/C:N/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 0.8,
"impactScore": 5.8,
"source": "secure@intel.com",
"type": "Secondary"
},
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 4.4,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "HIGH",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 0.8,
"impactScore": 3.6,
"source": "nvd@nist.gov",
"type": "Primary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2022-38102",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2024-10-02T13:58:08.283746Z",
"version": "2.0.3"
}
}
]
},
"published": "2023-08-11T03:15:14.070",
"references": [
{
"source": "secure@intel.com",
"tags": [
"Vendor Advisory"
],
"url": "http://www.intel.com/content/www/us/en/security-center/advisory/intel-sa-00783.html"
},
{
"source": "secure@intel.com",
"url": "https://security.netapp.com/advisory/ntap-20230824-0002/"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Vendor Advisory"
],
"url": "http://www.intel.com/content/www/us/en/security-center/advisory/intel-sa-00783.html"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "https://security.netapp.com/advisory/ntap-20230824-0002/"
}
],
"sourceIdentifier": "secure@intel.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-20"
}
],
"source": "secure@intel.com",
"type": "Secondary"
},
{
"description": [
{
"lang": "en",
"value": "NVD-CWE-noinfo"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
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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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