GSD-2020-10876
Vulnerability from gsd - Updated: 2023-12-13 01:22Details
The OKLOK (3.1.1) mobile companion app for Fingerprint Bluetooth Padlock FB50 (2.3) does not correctly implement its timeout on the four-digit verification code that is required for resetting passwords, nor does it properly restrict excessive verification attempts. This allows an attacker to brute force the four-digit verification code in order to bypass email verification and change the password of a victim account.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2020-10876",
"description": "The OKLOK (3.1.1) mobile companion app for Fingerprint Bluetooth Padlock FB50 (2.3) does not correctly implement its timeout on the four-digit verification code that is required for resetting passwords, nor does it properly restrict excessive verification attempts. This allows an attacker to brute force the four-digit verification code in order to bypass email verification and change the password of a victim account.",
"id": "GSD-2020-10876"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
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],
"details": "The OKLOK (3.1.1) mobile companion app for Fingerprint Bluetooth Padlock FB50 (2.3) does not correctly implement its timeout on the four-digit verification code that is required for resetting passwords, nor does it properly restrict excessive verification attempts. This allows an attacker to brute force the four-digit verification code in order to bypass email verification and change the password of a victim account.",
"id": "GSD-2020-10876",
"modified": "2023-12-13T01:22:04.019248Z",
"schema_version": "1.4.0"
}
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"affects": {
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}
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"name": "https://apps.apple.com/us/app/oklok/id1392287771",
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"url": "https://apps.apple.com/us/app/oklok/id1392287771"
},
{
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}
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"impact": {
"baseMetricV2": {
"acInsufInfo": false,
"cvssV2": {
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"accessVector": "NETWORK",
"authentication": "NONE",
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"baseScore": 5.0,
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"integrityImpact": "PARTIAL",
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"version": "2.0"
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"severity": "MEDIUM",
"userInteractionRequired": false
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"attackVector": "NETWORK",
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"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N",
"version": "3.1"
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},
"lastModifiedDate": "2020-05-15T18:36Z",
"publishedDate": "2020-05-04T14:15Z"
}
}
}
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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
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Nomenclature
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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.
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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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