GSD-2022-4116
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
A vulnerability was found in quarkus. This security flaw happens in Dev UI Config Editor which is vulnerable to drive-by localhost attacks leading to remote code execution.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-4116",
"description": "A vulnerability was found in quarkus. This security flaw happens in Dev UI Config Editor which is vulnerable to drive-by localhost attacks leading to remote code execution.",
"id": "GSD-2022-4116",
"references": [
"https://access.redhat.com/errata/RHSA-2022:8957",
"https://access.redhat.com/errata/RHSA-2022:9023"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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"details": "A vulnerability was found in quarkus. This security flaw happens in Dev UI Config Editor which is vulnerable to drive-by localhost attacks leading to remote code execution.",
"id": "GSD-2022-4116",
"modified": "2023-12-13T01:19:15.959112Z",
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{
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"cwe_ids": [
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"description": "A vulnerability was found in quarkus. This security flaw happens in Dev UI Config Editor which is vulnerable to drive-by localhost attacks leading to remote code execution.",
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"identifier": "CVE-2022-4116",
"identifiers": [
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"CVE-2022-4116"
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"not_impacted": "All versions starting from 2.13.5.final before 2.14.0, all versions starting from 2.14.2.final",
"package_slug": "maven/io.quarkus/quarkus-vertx-http-deployment",
"pubdate": "2022-11-22",
"solution": "Upgrade to versions 2.13.5.Final, 2.14.2.Final or above.",
"title": "Improper Control of Generation of Code (\u0027Code Injection\u0027)",
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"https://github.com/quarkusio/quarkus/discussions/29527",
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"impact": {
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"baseScore": 9.8,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
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"exploitabilityScore": 3.9,
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"lastModifiedDate": "2022-12-02T15:14Z",
"publishedDate": "2022-11-22T19:15Z"
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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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