FKIE_CVE-2018-15320
Vulnerability from fkie_nvd - Published: 2018-10-31 14:29 - Updated: 2026-06-17 01:42
Severity
Summary
On BIG-IP 14.0.0-14.0.0.2 or 13.0.0-13.1.1.1, undisclosed traffic patterns may lead to denial of service conditions for the BIG-IP system. The configuration which exposes this condition is the BIG-IP self IP address which is part of a VLAN group and has the Port Lockdown setting configured with anything other than "allow-all".
References
| URL | Tags | ||
|---|---|---|---|
| f5sirt@f5.com | https://support.f5.com/csp/article/K72442354 | Mitigation, Vendor Advisory | |
| af854a3a-2127-422b-91ae-364da2661108 | https://support.f5.com/csp/article/K72442354 | Mitigation, Vendor Advisory |
Impacted products
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{
"affectedData": [
{
"product": "BIG-IP (LTM, AAM, AFM, Analytics, APM, ASM, DNS, Edge Gateway, FPS, GTM, Link Controller, PEM, WebAccelerator)",
"vendor": "F5 Networks, Inc.",
"versions": [
{
"status": "affected",
"version": "14.0.0-14.0.0.2, 13.0.0-13.1.1.1"
}
]
}
],
"source": "f5sirt@f5.com"
}
],
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"cveTags": [],
"descriptions": [
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"lang": "en",
"value": "On BIG-IP 14.0.0-14.0.0.2 or 13.0.0-13.1.1.1, undisclosed traffic patterns may lead to denial of service conditions for the BIG-IP system. The configuration which exposes this condition is the BIG-IP self IP address which is part of a VLAN group and has the Port Lockdown setting configured with anything other than \"allow-all\"."
},
{
"lang": "es",
"value": "En BIG-IP 14.0.0-14.0.0.2 o 13.0.0-13.1.1.1, los patrones de tr\u00e1fico no divulgados podr\u00edan conducir a denegaciones de servicio (DoS) para el sistema BIG-IP. La configuraci\u00f3n que expone esta condici\u00f3n es la autodirecci\u00f3n IP de BIG-IP, que forma parte de un grupo VLAN y tiene la opci\u00f3n Port Lockdown configurada con cualquier cosa que no sea \"allow-all\"."
}
],
"id": "CVE-2018-15320",
"lastModified": "2026-06-17T01:42:15.400",
"metrics": {
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{
"acInsufInfo": false,
"baseSeverity": "MEDIUM",
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"accessVector": "NETWORK",
"authentication": "NONE",
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"confidentialityImpact": "NONE",
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"published": "2018-10-31T14:29:00.423",
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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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