FKIE_CVE-2022-31176
Vulnerability from fkie_nvd - Published: 2022-09-02 21:15 - Updated: 2026-06-17 04:44
Severity
8.3 (High) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:H
8.1 (High) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H
8.1 (High) - CVSS:3.1/
Summary
Grafana Image Renderer is a Grafana backend plugin that handles rendering of panels & dashboards to PNGs using a headless browser (Chromium/Chrome). An internal security review identified an unauthorized file disclosure vulnerability. It is possible for a malicious user to retrieve unauthorized files under some network conditions or via a fake datasource (if user has admin permissions in Grafana). All Grafana installations should be upgraded to version 3.6.1 as soon as possible. As a workaround it is possible to [disable HTTP remote rendering](https://grafana.com/docs/grafana/latest/setup-grafana/configure-grafana/#plugingrafana-image-renderer).
References
| URL | Tags | ||
|---|---|---|---|
| security-advisories@github.com | https://github.com/grafana/grafana-image-renderer/pull/364 | Issue Tracking, Patch, Third Party Advisory | |
| security-advisories@github.com | https://github.com/grafana/grafana-image-renderer/security/advisories/GHSA-2cfh-233g-m4c5 | Mitigation, Third Party Advisory | |
| security-advisories@github.com | https://security.netapp.com/advisory/ntap-20221209-0004/ | Third Party Advisory | |
| af854a3a-2127-422b-91ae-364da2661108 | https://github.com/grafana/grafana-image-renderer/pull/364 | Issue Tracking, Patch, Third Party Advisory | |
| af854a3a-2127-422b-91ae-364da2661108 | https://github.com/grafana/grafana-image-renderer/security/advisories/GHSA-2cfh-233g-m4c5 | Mitigation, Third Party Advisory | |
| af854a3a-2127-422b-91ae-364da2661108 | https://security.netapp.com/advisory/ntap-20221209-0004/ | Third Party Advisory |
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| grafana | grafana-image-renderer | * |
{
"affected": [
{
"affectedData": [
{
"product": "grafana-image-renderer",
"vendor": "grafana",
"versions": [
{
"status": "affected",
"version": "\u003c 3.6.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:grafana:grafana-image-renderer:*:*:*:*:*:grafana:*:*",
"matchCriteriaId": "B4487084-ACD5-4E0E-97C8-D3421BE54A35",
"versionEndExcluding": "3.6.1",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Grafana Image Renderer is a Grafana backend plugin that handles rendering of panels \u0026 dashboards to PNGs using a headless browser (Chromium/Chrome). An internal security review identified an unauthorized file disclosure vulnerability. It is possible for a malicious user to retrieve unauthorized files under some network conditions or via a fake datasource (if user has admin permissions in Grafana). All Grafana installations should be upgraded to version 3.6.1 as soon as possible. As a workaround it is possible to [disable HTTP remote rendering](https://grafana.com/docs/grafana/latest/setup-grafana/configure-grafana/#plugingrafana-image-renderer)."
},
{
"lang": "es",
"value": "Grafana Image Renderer es un plugin del backend de Grafana que es encargado de renderizar los paneles y cuadros de mando en PNGs utilizando un navegador headless (Chromium/Chrome). Una revisi\u00f3n de seguridad interna identific\u00f3 una vulnerabilidad de divulgaci\u00f3n de archivos no autorizada. Es posible que un usuario malicioso recupere archivos no autorizados bajo algunas condiciones de red o por medio de una fuente de datos falsa (si el usuario presenta permisos de administrador en Grafana). Todas las instalaciones de Grafana deber\u00edan actualizarse a versi\u00f3n 3.6.1 lo antes posible. Como mitigaci\u00f3n es posible [deshabilitar el renderizado remoto HTTP](https://grafana.com/docs/grafana/latest/setup-grafana/configure-grafana/#plugingrafana-image-renderer)"
}
],
"id": "CVE-2022-31176",
"lastModified": "2026-06-17T04:44:57.880",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.3,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "LOW",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 5.5,
"source": "security-advisories@github.com",
"type": "Secondary"
},
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.1,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 5.2,
"source": "nvd@nist.gov",
"type": "Primary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2022-31176",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-04-23T14:01:35.650108Z",
"version": "2.0.3"
}
}
]
},
"published": "2022-09-02T21:15:16.237",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Issue Tracking",
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/grafana/grafana-image-renderer/pull/364"
},
{
"source": "security-advisories@github.com",
"tags": [
"Mitigation",
"Third Party Advisory"
],
"url": "https://github.com/grafana/grafana-image-renderer/security/advisories/GHSA-2cfh-233g-m4c5"
},
{
"source": "security-advisories@github.com",
"tags": [
"Third Party Advisory"
],
"url": "https://security.netapp.com/advisory/ntap-20221209-0004/"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Issue Tracking",
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/grafana/grafana-image-renderer/pull/364"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Mitigation",
"Third Party Advisory"
],
"url": "https://github.com/grafana/grafana-image-renderer/security/advisories/GHSA-2cfh-233g-m4c5"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Third Party Advisory"
],
"url": "https://security.netapp.com/advisory/ntap-20221209-0004/"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-200"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
},
{
"description": [
{
"lang": "en",
"value": "CWE-306"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
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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 |
|---|
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.
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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.