GSD-2022-34844
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
In BIG-IP Versions 16.1.x before 16.1.3.1 and 15.1.x before 15.1.6.1, and all versions of BIG-IQ 8.x, when the Data Plane Development Kit (DPDK)/Elastic Network Adapter (ENA) driver is used with BIG-IP or BIG-IQ on Amazon Web Services (AWS) systems, undisclosed traffic can cause the Traffic Management Microkernel (TMM) to terminate. Successful exploitation relies on conditions outside of the attacker's control. Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-34844",
"description": "In BIG-IP Versions 16.1.x before 16.1.3.1 and 15.1.x before 15.1.6.1, and all versions of BIG-IQ 8.x, when the Data Plane Development Kit (DPDK)/Elastic Network Adapter (ENA) driver is used with BIG-IP or BIG-IQ on Amazon Web Services (AWS) systems, undisclosed traffic can cause the Traffic Management Microkernel (TMM) to terminate. Successful exploitation relies on conditions outside of the attacker\u0027s control. Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.",
"id": "GSD-2022-34844"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2022-34844"
],
"details": "In BIG-IP Versions 16.1.x before 16.1.3.1 and 15.1.x before 15.1.6.1, and all versions of BIG-IQ 8.x, when the Data Plane Development Kit (DPDK)/Elastic Network Adapter (ENA) driver is used with BIG-IP or BIG-IQ on Amazon Web Services (AWS) systems, undisclosed traffic can cause the Traffic Management Microkernel (TMM) to terminate. Successful exploitation relies on conditions outside of the attacker\u0027s control. Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.",
"id": "GSD-2022-34844",
"modified": "2023-12-13T01:19:18.612781Z",
"schema_version": "1.4.0"
}
},
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"DATE_PUBLIC": "2022-08-03T14:00:00.000Z",
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"TITLE": "BIG-IP and BIG-IQ AWS vulnerability CVE-2022-34844"
},
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}
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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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