GSD-2023-23617
Vulnerability from gsd - Updated: 2023-12-13 01:20Details
OpenMage LTS is an e-commerce platform. Versions prior to 19.4.22 and 20.0.19 contain an infinite loop in malicious code filter in certain conditions. Versions 19.4.22 and 20.0.19 have a fix for this issue. There are no known workarounds.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2023-23617",
"id": "GSD-2023-23617"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
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"id": "GSD-2023-23617",
"modified": "2023-12-13T01:20:49.881074Z",
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}
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"impact": {
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"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 4.9,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "HIGH",
"scope": "UNCHANGED",
"userInteraction": "NONE",
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"name": "https://github.com/OpenMage/magento-lts/releases/tag/v19.4.22",
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"url": "https://github.com/OpenMage/magento-lts/releases/tag/v19.4.22"
},
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"source": {
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"discovery": "UNKNOWN"
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"gitlab.com": {
"advisories": [
{
"affected_range": "\u003c19.4.22||\u003e=20.0.0,\u003c20.0.19",
"affected_versions": "All versions before 19.4.22, all versions starting from 20.0.0 before 20.0.19",
"cwe_ids": [
"CWE-1035",
"CWE-937"
],
"date": "2023-01-27",
"description": "### Impact\nInfinite loop in malicious code filter in certain conditions.\n\n### Workarounds\n\nNone",
"fixed_versions": [
"19.4.22",
"20.0.19"
],
"identifier": "GMS-2023-153",
"identifiers": [
"GHSA-3p73-mm7v-4f6m",
"GMS-2023-153",
"CVE-2023-23617"
],
"not_impacted": "All versions starting from 19.4.22 before 20.0.0, all versions starting from 20.0.19",
"package_slug": "packagist/openmage/magento-lts",
"pubdate": "2023-01-27",
"solution": "Upgrade to versions 19.4.22, 20.0.19 or above.",
"title": " DoS vulnerability in MaliciousCode filter",
"urls": [
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"https://github.com/OpenMage/magento-lts/releases/tag/v19.4.22",
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"attackVector": "NETWORK",
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"baseScore": 7.5,
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"lastModifiedDate": "2023-02-07T19:02Z",
"publishedDate": "2023-01-28T00:15Z"
}
}
}
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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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