GSD-2018-19859
Vulnerability from gsd - Updated: 2023-12-13 01:22Details
OpenRefine before 3.2 beta allows directory traversal via a relative pathname in a ZIP archive.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2018-19859",
"description": "OpenRefine before 3.2 beta allows directory traversal via a relative pathname in a ZIP archive.",
"id": "GSD-2018-19859"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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],
"details": "OpenRefine before 3.2 beta allows directory traversal via a relative pathname in a ZIP archive.",
"id": "GSD-2018-19859",
"modified": "2023-12-13T01:22:38.703981Z",
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"name": "https://github.com/OpenRefine/OpenRefine/issues/1840",
"refsource": "MISC",
"url": "https://github.com/OpenRefine/OpenRefine/issues/1840"
},
{
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"gitlab.com": {
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{
"affected_range": "(,3.2)",
"affected_versions": "All versions before 3.2",
"cvss_v2": "AV:N/AC:L/Au:S/C:N/I:P/A:N",
"cvss_v3": "CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:N",
"cwe_ids": [
"CWE-1035",
"CWE-22",
"CWE-937"
],
"date": "2023-07-25",
"description": "OpenRefine before 3.2 beta allows directory traversal via a relative pathname in a ZIP archive.",
"fixed_versions": [
"3.2"
],
"identifier": "CVE-2018-19859",
"identifiers": [
"GHSA-f6fv-fjg8-4m6w",
"CVE-2018-19859"
],
"not_impacted": "All versions starting from 3.2",
"package_slug": "maven/org.openrefine/openrefine",
"pubdate": "2022-05-14",
"solution": "Upgrade to version 3.2 or above.",
"title": "Improper Limitation of a Pathname to a Restricted Directory (\u0027Path Traversal\u0027)",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2018-19859",
"https://github.com/OpenRefine/OpenRefine/issues/1840",
"https://github.com/OpenRefine/OpenRefine/pull/1901",
"https://github.com/advisories/GHSA-f6fv-fjg8-4m6w"
],
"uuid": "7cfebdfa-2d25-4cdc-abde-8949bda8ecc3"
}
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},
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"authentication": "SINGLE",
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"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:L/Au:S/C:N/I:P/A:N",
"version": "2.0"
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"lastModifiedDate": "2019-03-28T20:04Z",
"publishedDate": "2018-12-05T11:29Z"
}
}
}
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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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