CNVD-2021-22845

Vulnerability from cnvd - Published: 2021-03-24
VLAI
Title
Limdu操作系统命令注入漏洞
Description
Limdu是Erel Segal-Halevi软件开发者的一款用于Node.js的机器学习框架。 Limdu 0.95之前版本中的‘trainBatch’函数存在操作系统命令注入漏洞。该漏洞源于外部输入数据构造操作系统可执行命令过程中,网络系统或产品未正确过滤其中的特殊字符、命令等。攻击者可利用该漏洞执行非法操作系统命令。
Severity
Patch Name
Limdu操作系统命令注入漏洞的补丁
Patch Description
Limdu是Erel Segal-Halevi软件开发者的一款用于Node.js的机器学习框架。 Limdu 0.95之前版本中的‘trainBatch’函数存在操作系统命令注入漏洞。该漏洞源于外部输入数据构造操作系统可执行命令过程中,网络系统或产品未正确过滤其中的特殊字符、命令等。攻击者可利用该漏洞执行非法操作系统命令。目前,供应商发布了安全公告及相关补丁信息,修复了此漏洞。
Formal description

目前厂商已发布升级补丁以修复漏洞,补丁获取链接:https://github.com/erelsgl/limdu/security/advisories/GHSA-77qv-gh6f-pgh4

Reference
https://nvd.nist.gov/vuln/detail/CVE-2020-4066
Impacted products
Name
Limdu Limdu <0.95
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-4066"
    }
  },
  "description": "Limdu\u662fErel Segal-Halevi\u8f6f\u4ef6\u5f00\u53d1\u8005\u7684\u4e00\u6b3e\u7528\u4e8eNode.js\u7684\u673a\u5668\u5b66\u4e60\u6846\u67b6\u3002\n\nLimdu 0.95\u4e4b\u524d\u7248\u672c\u4e2d\u7684\u2018trainBatch\u2019\u51fd\u6570\u5b58\u5728\u64cd\u4f5c\u7cfb\u7edf\u547d\u4ee4\u6ce8\u5165\u6f0f\u6d1e\u3002\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u5916\u90e8\u8f93\u5165\u6570\u636e\u6784\u9020\u64cd\u4f5c\u7cfb\u7edf\u53ef\u6267\u884c\u547d\u4ee4\u8fc7\u7a0b\u4e2d\uff0c\u7f51\u7edc\u7cfb\u7edf\u6216\u4ea7\u54c1\u672a\u6b63\u786e\u8fc7\u6ee4\u5176\u4e2d\u7684\u7279\u6b8a\u5b57\u7b26\u3001\u547d\u4ee4\u7b49\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u6267\u884c\u975e\u6cd5\u64cd\u4f5c\u7cfb\u7edf\u547d\u4ee4\u3002",
  "formalWay": "\u76ee\u524d\u5382\u5546\u5df2\u53d1\u5e03\u5347\u7ea7\u8865\u4e01\u4ee5\u4fee\u590d\u6f0f\u6d1e\uff0c\u8865\u4e01\u83b7\u53d6\u94fe\u63a5\uff1ahttps://github.com/erelsgl/limdu/security/advisories/GHSA-77qv-gh6f-pgh4",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2021-22845",
  "openTime": "2021-03-24",
  "patchDescription": "Limdu\u662fErel Segal-Halevi\u8f6f\u4ef6\u5f00\u53d1\u8005\u7684\u4e00\u6b3e\u7528\u4e8eNode.js\u7684\u673a\u5668\u5b66\u4e60\u6846\u67b6\u3002\r\n\r\nLimdu 0.95\u4e4b\u524d\u7248\u672c\u4e2d\u7684\u2018trainBatch\u2019\u51fd\u6570\u5b58\u5728\u64cd\u4f5c\u7cfb\u7edf\u547d\u4ee4\u6ce8\u5165\u6f0f\u6d1e\u3002\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u5916\u90e8\u8f93\u5165\u6570\u636e\u6784\u9020\u64cd\u4f5c\u7cfb\u7edf\u53ef\u6267\u884c\u547d\u4ee4\u8fc7\u7a0b\u4e2d\uff0c\u7f51\u7edc\u7cfb\u7edf\u6216\u4ea7\u54c1\u672a\u6b63\u786e\u8fc7\u6ee4\u5176\u4e2d\u7684\u7279\u6b8a\u5b57\u7b26\u3001\u547d\u4ee4\u7b49\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u6267\u884c\u975e\u6cd5\u64cd\u4f5c\u7cfb\u7edf\u547d\u4ee4\u3002\u76ee\u524d\uff0c\u4f9b\u5e94\u5546\u53d1\u5e03\u4e86\u5b89\u5168\u516c\u544a\u53ca\u76f8\u5173\u8865\u4e01\u4fe1\u606f\uff0c\u4fee\u590d\u4e86\u6b64\u6f0f\u6d1e\u3002",
  "patchName": "Limdu\u64cd\u4f5c\u7cfb\u7edf\u547d\u4ee4\u6ce8\u5165\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "Limdu Limdu \u003c0.95"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2020-4066",
  "serverity": "\u9ad8",
  "submitTime": "2020-06-23",
  "title": "Limdu\u64cd\u4f5c\u7cfb\u7edf\u547d\u4ee4\u6ce8\u5165\u6f0f\u6d1e"
}



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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

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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.
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  • 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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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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