GSD-2019-13614
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
CMD_SET_CONFIG_COUNTRY in the TP-Link Device Debug protocol in TP-Link Archer C1200 1.0.0 Build 20180502 rel.45702 and earlier is prone to a stack-based buffer overflow, which allows a remote attacker to achieve code execution or denial of service by sending a crafted payload to the listening server.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2019-13614",
"description": "CMD_SET_CONFIG_COUNTRY in the TP-Link Device Debug protocol in TP-Link Archer C1200 1.0.0 Build 20180502 rel.45702 and earlier is prone to a stack-based buffer overflow, which allows a remote attacker to achieve code execution or denial of service by sending a crafted payload to the listening server.",
"id": "GSD-2019-13614"
},
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"remediation": "unknown",
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"modified": "2023-12-13T01:23:41.374911Z",
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"lastModifiedDate": "2020-08-24T17:37Z",
"publishedDate": "2019-07-17T18:15Z"
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}
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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
| Author | Source | Type | Date | Other |
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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.
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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