FKIE_CVE-2025-20983
Vulnerability from fkie_nvd - Published: 2025-07-08 11:15 - Updated: 2026-06-17 08:42
Severity
6.4 (Medium) - CVSS:3.1/AV:L/AC:H/PR:H/UI:N/S:U/C:H/I:H/A:H
6.7 (Medium) - CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H
6.7 (Medium) - CVSS:3.1/
Summary
Out-of-bounds write in checking auth secret in KnoxVault trustlet prior to SMR Jul-2025 Release 1 allows local privileged attackers to write out-of-bounds memory.
References
Impacted products
{
"affected": [
{
"affectedData": [
{
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"vendor": "Samsung Mobile",
"versions": [
{
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"version": "SMR Jul-2025 Release in Android 14, 15"
}
]
}
],
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}
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"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Out-of-bounds write in checking auth secret in KnoxVault trustlet prior to SMR Jul-2025 Release 1 allows local privileged attackers to write out-of-bounds memory."
},
{
"lang": "es",
"value": "La escritura fuera de los l\u00edmites en la verificaci\u00f3n de secretos de autenticaci\u00f3n en el trustlet de KnoxVault antes de la versi\u00f3n 1 de SMR de julio de 2025 permite que atacantes privilegiados locales escriban en memoria fuera de los l\u00edmites."
}
],
"id": "CVE-2025-20983",
"lastModified": "2026-06-17T08:42:20.627",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "HIGH",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 6.4,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "HIGH",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:H/PR:H/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 0.5,
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"source": "mobile.security@samsung.com",
"type": "Secondary"
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{
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"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 6.7,
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"source": "nvd@nist.gov",
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"options": [
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{
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{
"technicalImpact": "total"
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],
"role": "CISA Coordinator",
"timestamp": "2025-07-11T03:55:24.649731Z",
"version": "2.0.3"
}
}
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},
"published": "2025-07-08T11:15:24.873",
"references": [
{
"source": "mobile.security@samsung.com",
"tags": [
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],
"url": "https://security.samsungmobile.com/securityUpdate.smsb?year=2025\u0026month=07"
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"sourceIdentifier": "mobile.security@samsung.com",
"vulnStatus": "Analyzed",
"weaknesses": [
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"value": "CWE-787"
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"source": "nvd@nist.gov",
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]
}
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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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