FKIE_CVE-2020-11268
Vulnerability from fkie_nvd - Published: 2021-05-07 09:15 - Updated: 2026-06-17 02:49
Severity
7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
7.5 (High) - CVSS:3.1/
Summary
Potential UE reset while decoding a crafted Sib1 or SIB1 that schedules unsupported SIBs and can lead to denial of service in Snapdragon Auto, Snapdragon Mobile
References
Impacted products
{
"affected": [
{
"affectedData": [
{
"product": "Snapdragon Auto, Snapdragon Mobile",
"vendor": "Qualcomm, Inc.",
"versions": [
{
"status": "affected",
"version": "APQ8009, APQ8016, APQ8074, APQ8084, APQ8094, AR6003, MDM8215, MDM8215M, MDM8615M, MDM9215, MDM9235M, MDM9310, MDM9609, MDM9615, MDM9615M, MDM9635M, MDM9640, MDM9645, MSM8108, MSM8208, MSM8209, MSM8216, MSM8274, MSM8608, MSM8674, MSM8916, MSM8929, MSM8939, MSM8974, MSM8974P, MSM8994, PM8018, PM8841, PM8909, PM8916, PM8941, PM8994, PMD9635, PMD9645, PMI8994, QCA1990, QCA6174, QCA6174A, QCA6584, QFE1035, QFE1040, QFE1045, QFE1100, QFE1101, QFE1520, QFE1550, QFE2101, QFE2310, QFE2320, QFE2330, QFE2340, QFE2520, QFE2550, QFE2720, QFE3100, QFE3320, QFE3335, QFE3340, QFE3345, SD210, SMB1360, WCD9306, WCD9330, WCN3610, WCN3620, WCN3660, WCN3660A, WCN3660B, WCN3680, WCN3680B, WFR1620, WGR7640, WTR1605, WTR1605L, WTR1625, WTR1625L, WTR2605, WTR2955, WTR3925, WTR4605, WTR4905"
}
]
}
],
"source": "product-security@qualcomm.com"
}
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"negate": false,
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],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Potential UE reset while decoding a crafted Sib1 or SIB1 that schedules unsupported SIBs and can lead to denial of service in Snapdragon Auto, Snapdragon Mobile"
},
{
"lang": "es",
"value": "Un potencial reinicio de UE al decodificar un Sib1 o SIB1 dise\u00f1ado que programa los SIB no compatibles y puede conllevar a una denegaci\u00f3n de servicio en los productos Snapdragon Auto, Snapdragon Mobile"
}
],
"id": "CVE-2020-11268",
"lastModified": "2026-06-17T02:49:51.687",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "MEDIUM",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 5.0,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:N/I:N/A:P",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": false
}
],
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6,
"source": "product-security@qualcomm.com",
"type": "Secondary"
},
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6,
"source": "nvd@nist.gov",
"type": "Primary"
}
]
},
"published": "2021-05-07T09:15:07.773",
"references": [
{
"source": "product-security@qualcomm.com",
"tags": [
"Vendor Advisory"
],
"url": "https://www.qualcomm.com/company/product-security/bulletins/may-2021-bulletin"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Vendor Advisory"
],
"url": "https://www.qualcomm.com/company/product-security/bulletins/may-2021-bulletin"
}
],
"sourceIdentifier": "product-security@qualcomm.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-20"
}
],
"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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