GSD-2022-39881
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
Improper input validation vulnerability for processing SIB12 PDU in Exynos modems prior to SMR Sep-2022 Release allows remote attacker to read out of bounds memory.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-39881",
"description": "Improper input validation vulnerability for processing SIB12 PDU in Exynos modems prior to SMR Sep-2022 Release allows remote attacker to read out of bounds memory.",
"id": "GSD-2022-39881"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2022-39881"
],
"details": "Improper input validation vulnerability for processing SIB12 PDU in Exynos modems prior to SMR Sep-2022 Release allows remote attacker to read out of bounds memory.",
"id": "GSD-2022-39881",
"modified": "2023-12-13T01:19:20.575013Z",
"schema_version": "1.4.0"
}
},
"namespaces": {
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"CVE_data_meta": {
"ASSIGNER": "mobile.security@samsung.com",
"ID": "CVE-2022-39881",
"STATE": "PUBLIC"
},
"affects": {
"vendor": {
"vendor_data": [
{
"product": {
"product_data": [
{
"product_name": "Samsung Mobile Devices",
"version": {
"version_data": [
{
"version_affected": "\u003c",
"version_name": "Select devices using Exynos CP chipsets",
"version_value": "SMR Nov-2022 Release 1"
}
]
}
}
]
},
"vendor_name": "Samsung Mobile"
}
]
}
},
"data_format": "MITRE",
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"data_version": "4.0",
"description": {
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{
"lang": "eng",
"value": "Improper input validation vulnerability for processing SIB12 PDU in Exynos modems prior to SMR Sep-2022 Release allows remote attacker to read out of bounds memory."
}
]
},
"impact": {
"cvss": {
"attackComplexity": "HIGH",
"attackVector": "ADJACENT_NETWORK",
"availabilityImpact": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
}
},
"problemtype": {
"problemtype_data": [
{
"description": [
{
"lang": "eng",
"value": "CWE-20 Improper Input Validation"
}
]
}
]
},
"references": {
"reference_data": [
{
"name": "https://security.samsungmobile.com/securityUpdate.smsb?year=2022\u0026month=11",
"refsource": "MISC",
"url": "https://security.samsungmobile.com/securityUpdate.smsb?year=2022\u0026month=11"
}
]
},
"source": {
"discovery": "UNKNOWN"
}
},
"nvd.nist.gov": {
"configurations": {
"CVE_data_version": "4.0",
"nodes": [
{
"children": [
{
"children": [],
"cpe_match": [
{
"cpe23Uri": "cpe:2.3:o:samsung:exynos_firmware:-:*:*:*:*:*:*:*",
"cpe_name": [],
"vulnerable": true
}
],
"operator": "OR"
},
{
"children": [],
"cpe_match": [
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"cpe_name": [],
"vulnerable": false
}
],
"operator": "OR"
}
],
"cpe_match": [],
"operator": "AND"
}
]
},
"cve": {
"CVE_data_meta": {
"ASSIGNER": "mobile.security@samsung.com",
"ID": "CVE-2022-39881"
},
"data_format": "MITRE",
"data_type": "CVE",
"data_version": "4.0",
"description": {
"description_data": [
{
"lang": "en",
"value": "Improper input validation vulnerability for processing SIB12 PDU in Exynos modems prior to SMR Sep-2022 Release allows remote attacker to read out of bounds memory."
}
]
},
"problemtype": {
"problemtype_data": [
{
"description": [
{
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"value": "CWE-125"
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},
"references": {
"reference_data": [
{
"name": "https://security.samsungmobile.com/securityUpdate.smsb?year=2022\u0026month=11",
"refsource": "MISC",
"tags": [
"Vendor Advisory"
],
"url": "https://security.samsungmobile.com/securityUpdate.smsb?year=2022\u0026month=11"
}
]
}
},
"impact": {
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 9.1,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 5.2
}
},
"lastModifiedDate": "2022-11-10T15:16Z",
"publishedDate": "2022-11-09T22:15Z"
}
}
}
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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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