GSD-2023-39447
Vulnerability from gsd - Updated: 2023-12-13 01:20Details
When BIG-IP APM Guided Configurations are configured, undisclosed sensitive information may be logged in restnoded log.
Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2023-39447",
"id": "GSD-2023-39447"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2023-39447"
],
"details": "\nWhen BIG-IP APM Guided Configurations are configured, undisclosed sensitive information may be logged in restnoded log.\u00a0\u00a0\n\nNote: Software versions which have reached End of Technical Support (EoTS) are not evaluated.\n\n\n\n",
"id": "GSD-2023-39447",
"modified": "2023-12-13T01:20:33.468506Z",
"schema_version": "1.4.0"
}
},
"namespaces": {
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"CVE_data_meta": {
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"ID": "CVE-2023-39447",
"STATE": "PUBLIC"
},
"affects": {
"vendor": {
"vendor_data": [
{
"product": {
"product_data": [
{
"product_name": "BIG-IP",
"version": {
"version_data": [
{
"version_value": "not down converted",
"x_cve_json_5_version_data": {
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{
"version_affected": "\u003c",
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"value": "\nWhen BIG-IP APM Guided Configurations are configured, undisclosed sensitive information may be logged in restnoded log.\u00a0\u00a0\n\nNote: Software versions which have reached End of Technical Support (EoTS) are not evaluated.\n\n\n\n"
}
]
},
"generator": {
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},
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"availabilityImpact": "NONE",
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"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "HIGH",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:H/I:N/A:N",
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"problemtype_data": [
{
"description": [
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"lang": "eng",
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}
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}
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},
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}
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"versionEndIncluding": "7.7",
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"operator": "OR"
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]
},
"cve": {
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"ID": "CVE-2023-39447"
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"tags": [
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},
"impact": {
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"cvssV3": {
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"attackVector": "LOCAL",
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"version": "3.1"
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"exploitabilityScore": 0.8,
"impactScore": 3.6
}
},
"lastModifiedDate": "2023-10-16T18:40Z",
"publishedDate": "2023-10-10T13:15Z"
}
}
}
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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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