GHSA-9M82-F3WX-P625
Vulnerability from github – Published: 2022-05-14 01:53 – Updated: 2023-07-25 17:54
VLAI
Summary
LibreNMS XSS Vulnerability
Details
Persistent Cross-Site Scripting (XSS) issues in LibreNMS before 1.44 allow remote attackers to inject arbitrary web script or HTML via the dashboard_name parameter in the /ajax_form.php resource, related to html/includes/forms/add-dashboard.inc.php, html/includes/forms/delete-dashboard.inc.php, and html/includes/forms/edit-dashboard.inc.php.
Severity
6.1 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "Packagist",
"name": "librenms/librenms"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "1.44"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2018-18478"
],
"database_specific": {
"cwe_ids": [
"CWE-79"
],
"github_reviewed": true,
"github_reviewed_at": "2023-07-25T17:54:38Z",
"nvd_published_at": "2018-10-18T17:29:00Z",
"severity": "MODERATE"
},
"details": "Persistent Cross-Site Scripting (XSS) issues in LibreNMS before 1.44 allow remote attackers to inject arbitrary web script or HTML via the dashboard_name parameter in the /ajax_form.php resource, related to html/includes/forms/add-dashboard.inc.php, html/includes/forms/delete-dashboard.inc.php, and html/includes/forms/edit-dashboard.inc.php.",
"id": "GHSA-9m82-f3wx-p625",
"modified": "2023-07-25T17:54:38Z",
"published": "2022-05-14T01:53:39Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2018-18478"
},
{
"type": "WEB",
"url": "https://github.com/librenms/librenms/issues/9170"
},
{
"type": "WEB",
"url": "https://github.com/librenms/librenms/pull/9171"
},
{
"type": "WEB",
"url": "https://github.com/librenms/librenms/releases/tag/1.44"
},
{
"type": "WEB",
"url": "https://hackpuntes.com/cve-2018-18478-libre-nms-1-43-cross-site-scripting-persistente"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N",
"type": "CVSS_V3"
}
],
"summary": "LibreNMS XSS Vulnerability"
}
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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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