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CNVD-2019-31361

Vulnerability from cnvd - Published: 2019-09-15
VLAI
Title
Limesurvey完整性检查漏洞
Description
limesurvey是一款开源在线问卷调查程序,具有问卷的设计、修改、发布、回收和统计等多项功能。 Limesurvey 3.17.14之前版本存在完整性检查漏洞。攻击者可利用该漏洞在没有适当权限的情况下运行完整性检查。
Severity
Patch Name
Limesurvey完整性检查漏洞的补丁
Patch Description
limesurvey是一款开源在线问卷调查程序,具有问卷的设计、修改、发布、回收和统计等多项功能。 Limesurvey 3.17.14之前版本存在完整性检查漏洞。攻击者可利用该漏洞在没有适当权限的情况下运行完整性检查。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

厂商已发布了漏洞修复程序,请及时关注更新: https://github.com/LimeSurvey/LimeSurvey/commit/5870fd1037058bc4e43cccf893b576c72293371e#diff-d539f3f8185667ee48db78e1bf65a3b4R50

Reference
https://nvd.nist.gov/vuln/detail/CVE-2019-16183
Impacted products
Name
Limesurvey LimeSurvey <3.17.14
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2019-16183",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2019-16183"
    }
  },
  "description": "limesurvey\u662f\u4e00\u6b3e\u5f00\u6e90\u5728\u7ebf\u95ee\u5377\u8c03\u67e5\u7a0b\u5e8f\uff0c\u5177\u6709\u95ee\u5377\u7684\u8bbe\u8ba1\u3001\u4fee\u6539\u3001\u53d1\u5e03\u3001\u56de\u6536\u548c\u7edf\u8ba1\u7b49\u591a\u9879\u529f\u80fd\u3002\n\nLimesurvey 3.17.14\u4e4b\u524d\u7248\u672c\u5b58\u5728\u5b8c\u6574\u6027\u68c0\u67e5\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5728\u6ca1\u6709\u9002\u5f53\u6743\u9650\u7684\u60c5\u51b5\u4e0b\u8fd0\u884c\u5b8c\u6574\u6027\u68c0\u67e5\u3002",
  "discovererName": "unknwon",
  "formalWay": "\u5382\u5546\u5df2\u53d1\u5e03\u4e86\u6f0f\u6d1e\u4fee\u590d\u7a0b\u5e8f\uff0c\u8bf7\u53ca\u65f6\u5173\u6ce8\u66f4\u65b0\uff1a\r\nhttps://github.com/LimeSurvey/LimeSurvey/commit/5870fd1037058bc4e43cccf893b576c72293371e#diff-d539f3f8185667ee48db78e1bf65a3b4R50",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2019-31361",
  "openTime": "2019-09-15",
  "patchDescription": "limesurvey\u662f\u4e00\u6b3e\u5f00\u6e90\u5728\u7ebf\u95ee\u5377\u8c03\u67e5\u7a0b\u5e8f\uff0c\u5177\u6709\u95ee\u5377\u7684\u8bbe\u8ba1\u3001\u4fee\u6539\u3001\u53d1\u5e03\u3001\u56de\u6536\u548c\u7edf\u8ba1\u7b49\u591a\u9879\u529f\u80fd\u3002\r\n\r\nLimesurvey 3.17.14\u4e4b\u524d\u7248\u672c\u5b58\u5728\u5b8c\u6574\u6027\u68c0\u67e5\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5728\u6ca1\u6709\u9002\u5f53\u6743\u9650\u7684\u60c5\u51b5\u4e0b\u8fd0\u884c\u5b8c\u6574\u6027\u68c0\u67e5\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": "Limesurvey\u5b8c\u6574\u6027\u68c0\u67e5\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "Limesurvey LimeSurvey \u003c3.17.14"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2019-16183",
  "serverity": "\u4e2d",
  "submitTime": "2019-09-10",
  "title": "Limesurvey\u5b8c\u6574\u6027\u68c0\u67e5\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

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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Detection rules are retrieved from Rulezet.

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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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