GSD-2015-1369
Vulnerability from gsd - Updated: 2023-12-13 01:20Details
SQL injection vulnerability in Sequelize before 2.0.0-rc7 for Node.js allows remote attackers to execute arbitrary SQL commands via the order parameter.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2015-1369",
"description": "SQL injection vulnerability in Sequelize before 2.0.0-rc7 for Node.js allows remote attackers to execute arbitrary SQL commands via the order parameter.",
"id": "GSD-2015-1369"
},
"gsd": {
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"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
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"modified": "2023-12-13T01:20:05.399946Z",
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"name": "https://github.com/sequelize/sequelize/pull/2919",
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"name": "[oss-security] 20150122 CVE requests for nodejs marked VBScript Content Injection and sequelize SQL Injection in Order",
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"gitlab.com": {
"advisories": [
{
"affected_range": "\u003c=2.0.0-rc7",
"affected_versions": "All versions up to 2.0.0-rc7",
"credit": "Levan Basharuli",
"cvss_v2": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
"cwe_ids": [
"CWE-1035",
"CWE-89",
"CWE-937"
],
"date": "2015-01-28",
"description": "SQL Injection is possible in an application using the npm module sequelize if untrusted user input is passed into the order parameter. Example: ``` Test.findAndCountAll({ where: { id :1 }, order : [[\u0027id\u0027, \u0027UNTRUSTED USER INPUT\u0027]] }) ```",
"fixed_versions": [
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],
"identifier": "CVE-2015-1369",
"identifiers": [
"CVE-2015-1369"
],
"not_impacted": "All versions after 2.0.0-rc7",
"package_slug": "npm/sequelize",
"pubdate": "2015-01-27",
"solution": "Upgrade to version 2.0.0-rc8 or above.",
"title": "SQL Injection in Order",
"urls": [
"https://github.com/sequelize/sequelize/issues/2906"
],
"uuid": "1211b18d-819c-4f68-bb6d-f2dcbdae0566"
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"impact": {
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"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 7.5,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 6.4,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "HIGH",
"userInteractionRequired": false
}
},
"lastModifiedDate": "2015-01-28T19:35Z",
"publishedDate": "2015-01-27T20:04Z"
}
}
}
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Sightings
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Nomenclature
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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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