GSD-2022-31123
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
Grafana is an open source observability and data visualization platform. Versions prior to 9.1.8 and 8.5.14 are vulnerable to a bypass in the plugin signature verification. An attacker can convince a server admin to download and successfully run a malicious plugin even though unsigned plugins are not allowed. Versions 9.1.8 and 8.5.14 contain a patch for this issue. As a workaround, do not install plugins downloaded from untrusted sources.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-31123",
"description": "Grafana is an open source observability and data visualization platform. Versions prior to 9.1.8 and 8.5.14 are vulnerable to a bypass in the plugin signature verification. An attacker can convince a server admin to download and successfully run a malicious plugin even though unsigned plugins are not allowed. Versions 9.1.8 and 8.5.14 contain a patch for this issue. As a workaround, do not install plugins downloaded from untrusted sources.",
"id": "GSD-2022-31123",
"references": [
"https://www.suse.com/security/cve/CVE-2022-31123.html"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
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],
"details": "Grafana is an open source observability and data visualization platform. Versions prior to 9.1.8 and 8.5.14 are vulnerable to a bypass in the plugin signature verification. An attacker can convince a server admin to download and successfully run a malicious plugin even though unsigned plugins are not allowed. Versions 9.1.8 and 8.5.14 contain a patch for this issue. As a workaround, do not install plugins downloaded from untrusted sources.",
"id": "GSD-2022-31123",
"modified": "2023-12-13T01:19:17.984496Z",
"schema_version": "1.4.0"
}
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}
]
},
"impact": {
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"attackVector": "LOCAL",
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"baseScore": 6.1,
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"impact": {
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"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 7.8,
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"confidentialityImpact": "HIGH",
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"vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
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"exploitabilityScore": 1.8,
"impactScore": 5.9
}
},
"lastModifiedDate": "2022-12-03T15:03Z",
"publishedDate": "2022-10-13T22:15Z"
}
}
}
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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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