GSD-2019-12588
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
The client 802.11 mac implementation in Espressif ESP8266_NONOS_SDK 2.2.0 through 3.1.0 does not validate correctly the RSN AuthKey suite list count in beacon frames, probe responses, and association responses, which allows attackers in radio range to cause a denial of service (crash) via a crafted message.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2019-12588",
"description": "The client 802.11 mac implementation in Espressif ESP8266_NONOS_SDK 2.2.0 through 3.1.0 does not validate correctly the RSN AuthKey suite list count in beacon frames, probe responses, and association responses, which allows attackers in radio range to cause a denial of service (crash) via a crafted message.",
"id": "GSD-2019-12588"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
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],
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"id": "GSD-2019-12588",
"modified": "2023-12-13T01:23:43.718396Z",
"schema_version": "1.4.0"
}
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"refsource": "MISC",
"url": "https://github.com/espressif"
},
{
"name": "https://github.com/Matheus-Garbelini/esp32_esp8266_attacks",
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"url": "https://github.com/Matheus-Garbelini/esp32_esp8266_attacks"
},
{
"name": "https://matheus-garbelini.github.io/home/post/esp8266-beacon-frame-crash/",
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"versionEndIncluding": "3.1.0",
"versionStartIncluding": "2.2.0",
"vulnerable": true
},
{
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},
"impact": {
"baseMetricV2": {
"acInsufInfo": false,
"cvssV2": {
"accessComplexity": "LOW",
"accessVector": "ADJACENT_NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 3.3,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:A/AC:L/Au:N/C:N/I:N/A:P",
"version": "2.0"
},
"exploitabilityScore": 6.5,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "LOW",
"userInteractionRequired": false
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"baseMetricV3": {
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"attackVector": "ADJACENT_NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 6.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
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"exploitabilityScore": 2.8,
"impactScore": 3.6
}
},
"lastModifiedDate": "2019-09-09T16:56Z",
"publishedDate": "2019-09-04T12:15Z"
}
}
}
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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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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
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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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