CVE-2022-22090 (GCVE-0-2022-22090)
Vulnerability from cvelistv5 – Published: 2022-06-14 09:40 – Updated: 2024-08-03 03:00
VLAI
EPSS
VEX
Summary
Memory corruption in audio due to use after free while managing buffers from internal cache in Snapdragon Compute, Snapdragon Connectivity, Snapdragon Mobile
Severity
8.4 (High)
CWE
- Use After Free in Audio
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://www.qualcomm.com/company/product-security… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Qualcomm, Inc. | Snapdragon Compute, Snapdragon Connectivity, Snapdragon Mobile |
Affected:
SD 8 Gen1 5G, SD865 5G, SD888 5G, SDX65, SM7450, SM8475, SM8475P, WCD9370, WCD9375, WCD9380, WCD9385, WCN6750, WCN6850, WCN6851, WCN6855, WCN6856, WCN7850, WCN7851, WSA8810, WSA8815, WSA8830, WSA8832, WSA8835
|
guessed |
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"matchCriteriaId": "6B36F4B2-BAA3-45AD-9967-0EB482C99708",
"vulnerable": false
}
],
"negate": false,
"operator": "OR"
}
],
"operator": "AND"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Memory corruption in audio due to use after free while managing buffers from internal cache in Snapdragon Compute, Snapdragon Connectivity, Snapdragon Mobile"
},
{
"lang": "es",
"value": "Una corrupci\u00f3n de memoria en audio debido a un uso de memoria previamente liberada mientras son administrados los b\u00faferes de la cach\u00e9 interna en Snapdragon Compute, Snapdragon Connectivity, Snapdragon Mobile"
}
],
"id": "CVE-2022-22090",
"lastModified": "2026-06-17T04:27:42.667",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "HIGH",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "LOCAL",
"authentication": "NONE",
"availabilityImpact": "COMPLETE",
"baseScore": 7.2,
"confidentialityImpact": "COMPLETE",
"integrityImpact": "COMPLETE",
"vectorString": "AV:L/AC:L/Au:N/C:C/I:C/A:C",
"version": "2.0"
},
"exploitabilityScore": 3.9,
"impactScore": 10.0,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": false
}
],
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 8.4,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.5,
"impactScore": 5.9,
"source": "product-security@qualcomm.com",
"type": "Secondary"
},
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 7.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 1.8,
"impactScore": 5.9,
"source": "nvd@nist.gov",
"type": "Primary"
}
]
},
"published": "2022-06-14T10:15:19.473",
"references": [
{
"source": "product-security@qualcomm.com",
"tags": [
"Vendor Advisory"
],
"url": "https://www.qualcomm.com/company/product-security/bulletins/june-2022-bulletin"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Vendor Advisory"
],
"url": "https://www.qualcomm.com/company/product-security/bulletins/june-2022-bulletin"
}
],
"sourceIdentifier": "product-security@qualcomm.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-416"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"current_release_date": "2026-03-27T12:53:15+00:00",
"cve": "CVE-2022-22090",
"id": "CVE-2022-22090",
"initial_release_date": "2022-01-01T00:00:00+00:00",
"product_status:known_not_affected": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "CVE-2022-22090",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2022/cve-2022-22090.json",
"version": "3"
}
}
}
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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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