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CNVD-2020-44628

Vulnerability from cnvd - Published: 2020-08-06
VLAI
Title
October CMS信息泄露漏洞
Description
October CMS是一套基于PHP和Laravel Web应用程序框架的开源内容管理系统(CMS)。 October CMS 1.0.468之前版本中存在信息泄露漏洞,该漏洞源于程序没有将加密的cookie值绑定到该值的cookie名上。攻击者可利用该漏洞获取敏感信息。
Severity
Patch Name
October CMS信息泄露漏洞的补丁
Patch Description
October CMS是一套基于PHP和Laravel Web应用程序框架的开源内容管理系统(CMS)。 October CMS 1.0.468之前版本中存在信息泄露漏洞,该漏洞源于程序没有将加密的cookie值绑定到该值的cookie名上。攻击者可利用该漏洞获取敏感信息。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/octobercms/october/security/advisories/GHSA-55mm-5399-7r63

Reference
https://github.com/octobercms/library/commit/28310d4fb336a1741b39498f4474497644a6875c
Impacted products
Name
October CMS October CMS <1.0.468
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-15128",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2020-15128"
    }
  },
  "description": "October CMS\u662f\u4e00\u5957\u57fa\u4e8ePHP\u548cLaravel Web\u5e94\u7528\u7a0b\u5e8f\u6846\u67b6\u7684\u5f00\u6e90\u5185\u5bb9\u7ba1\u7406\u7cfb\u7edf\uff08CMS\uff09\u3002\n\nOctober CMS 1.0.468\u4e4b\u524d\u7248\u672c\u4e2d\u5b58\u5728\u4fe1\u606f\u6cc4\u9732\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7a0b\u5e8f\u6ca1\u6709\u5c06\u52a0\u5bc6\u7684cookie\u503c\u7ed1\u5b9a\u5230\u8be5\u503c\u7684cookie\u540d\u4e0a\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u83b7\u53d6\u654f\u611f\u4fe1\u606f\u3002",
  "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/octobercms/october/security/advisories/GHSA-55mm-5399-7r63",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2020-44628",
  "openTime": "2020-08-06",
  "patchDescription": "October CMS\u662f\u4e00\u5957\u57fa\u4e8ePHP\u548cLaravel Web\u5e94\u7528\u7a0b\u5e8f\u6846\u67b6\u7684\u5f00\u6e90\u5185\u5bb9\u7ba1\u7406\u7cfb\u7edf\uff08CMS\uff09\u3002\r\n\r\nOctober CMS 1.0.468\u4e4b\u524d\u7248\u672c\u4e2d\u5b58\u5728\u4fe1\u606f\u6cc4\u9732\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7a0b\u5e8f\u6ca1\u6709\u5c06\u52a0\u5bc6\u7684cookie\u503c\u7ed1\u5b9a\u5230\u8be5\u503c\u7684cookie\u540d\u4e0a\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u83b7\u53d6\u654f\u611f\u4fe1\u606f\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": "October CMS\u4fe1\u606f\u6cc4\u9732\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "October CMS October CMS \u003c1.0.468"
  },
  "referenceLink": "https://github.com/octobercms/library/commit/28310d4fb336a1741b39498f4474497644a6875c",
  "serverity": "\u4f4e",
  "submitTime": "2020-08-03",
  "title": "October CMS\u4fe1\u606f\u6cc4\u9732\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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