CVE-2020-3636 (GCVE-0-2020-3636)
Vulnerability from cvelistv5 – Published: 2020-09-08 09:31 – Updated: 2024-08-04 07:37
VLAI
EPSS
VEX
Summary
u'Out of bound writes happen when accessing usage_table header entry beyond the memory allocated for the header' in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking in Kamorta, QCS404, QCS610, Rennell, SC7180, SDX55, SM6150, SM7150, SM8250, SXR2130
Severity
7.8 (High)
CWE
- Usage of Out-of-range Pointer Offset in Content Protection
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 Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking |
Affected:
Kamorta, QCS404, QCS610, Rennell, SC7180, SDX55, SM6150, SM7150, SM8250, SXR2130
|
guessed |
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"lang": "en",
"value": "u\u0027Out of bound writes happen when accessing usage_table header entry beyond the memory allocated for the header\u0027 in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking in Kamorta, QCS404, QCS610, Rennell, SC7180, SDX55, SM6150, SM7150, SM8250, SXR2130"
},
{
"lang": "es",
"value": "Una escritura fuera de l\u00edmites ocurre cuando se accede a un encabezado usage_table m\u00e1s all\u00e1 de la memoria asignada para el encabezado en los productos Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking en versiones Kamorta, QCS404, QCS610, Rennell, SC7180, SDX55, SM6150, SM7150, SM8250, SXR2130."
}
],
"id": "CVE-2020-3636",
"lastModified": "2024-11-21T05:31:27.343",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "MEDIUM",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "LOCAL",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 4.6,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:L/AC:L/Au:N/C:P/I:P/A:P",
"version": "2.0"
},
"exploitabilityScore": 3.9,
"impactScore": 6.4,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": false
}
],
"cvssMetricV31": [
{
"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": "2020-09-08T10:15:15.403",
"references": [
{
"source": "product-security@qualcomm.com",
"tags": [
"Broken Link"
],
"url": "https://www.qualcomm.com/company/product-security/bulletins/august-2020-bulletin"
},
{
"source": "nvd@nist.gov",
"tags": [
"Vendor Advisory"
],
"url": "https://www.qualcomm.com/company/product-security/bulletins/august-2020-security-bulletin"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Broken Link"
],
"url": "https://www.qualcomm.com/company/product-security/bulletins/august-2020-bulletin"
}
],
"sourceIdentifier": "product-security@qualcomm.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "NVD-CWE-Other"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"current_release_date": "2025-05-22T15:34:37+00:00",
"cve": "CVE-2020-3636",
"id": "CVE-2020-3636",
"initial_release_date": "2020-01-01T00:00:00+00:00",
"product_status:known_not_affected": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "CVE-2020-3636",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2020/cve-2020-3636.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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