CNVD-2020-36747

Vulnerability from cnvd - Published: 2020-07-07
VLAI
Title
MiniShare缓冲区溢出漏洞(CNVD-2020-36747)
Description
MiniShare是一款主要用于文件共享的Web服务器。 MiniShare 1.4.2之前版本中存在缓冲区溢出漏洞,该漏洞源于程序未能进行正确的边界检查。远程攻击者可通过发送特制的HTTP PUT请求利用该漏洞在系统上执行任意代码。
Severity
Patch Name
MiniShare缓冲区溢出漏洞(CNVD-2020-36747)的补丁
Patch Description
MiniShare是一款主要用于文件共享的Web服务器。 MiniShare 1.4.2之前版本中存在缓冲区溢出漏洞,该漏洞源于程序未能进行正确的边界检查。远程攻击者可通过发送特制的HTTP PUT请求利用该漏洞在系统上执行任意代码。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,详情请关注厂商主页: https://sourceforge.net/projects/minishare/

Reference
https://nvd.nist.gov/vuln/detail/CVE-2020-13768
Impacted products
Name
MiniShare MiniShare <1.4.2
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-13768"
    }
  },
  "description": "MiniShare\u662f\u4e00\u6b3e\u4e3b\u8981\u7528\u4e8e\u6587\u4ef6\u5171\u4eab\u7684Web\u670d\u52a1\u5668\u3002\n\nMiniShare 1.4.2\u4e4b\u524d\u7248\u672c\u4e2d\u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7a0b\u5e8f\u672a\u80fd\u8fdb\u884c\u6b63\u786e\u7684\u8fb9\u754c\u68c0\u67e5\u3002\u8fdc\u7a0b\u653b\u51fb\u8005\u53ef\u901a\u8fc7\u53d1\u9001\u7279\u5236\u7684HTTP PUT\u8bf7\u6c42\u5229\u7528\u8be5\u6f0f\u6d1e\u5728\u7cfb\u7edf\u4e0a\u6267\u884c\u4efb\u610f\u4ee3\u7801\u3002",
  "formalWay": "\u76ee\u524d\u5382\u5546\u5df2\u53d1\u5e03\u5347\u7ea7\u8865\u4e01\u4ee5\u4fee\u590d\u6f0f\u6d1e\uff0c\u8be6\u60c5\u8bf7\u5173\u6ce8\u5382\u5546\u4e3b\u9875\uff1a\r\nhttps://sourceforge.net/projects/minishare/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2020-36747",
  "openTime": "2020-07-07",
  "patchDescription": "MiniShare\u662f\u4e00\u6b3e\u4e3b\u8981\u7528\u4e8e\u6587\u4ef6\u5171\u4eab\u7684Web\u670d\u52a1\u5668\u3002\r\n\r\nMiniShare 1.4.2\u4e4b\u524d\u7248\u672c\u4e2d\u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7a0b\u5e8f\u672a\u80fd\u8fdb\u884c\u6b63\u786e\u7684\u8fb9\u754c\u68c0\u67e5\u3002\u8fdc\u7a0b\u653b\u51fb\u8005\u53ef\u901a\u8fc7\u53d1\u9001\u7279\u5236\u7684HTTP PUT\u8bf7\u6c42\u5229\u7528\u8be5\u6f0f\u6d1e\u5728\u7cfb\u7edf\u4e0a\u6267\u884c\u4efb\u610f\u4ee3\u7801\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": "MiniShare\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2020-36747\uff09\u7684\u8865\u4e01",
  "products": {
    "product": "MiniShare MiniShare  \u003c1.4.2"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2020-13768",
  "serverity": "\u9ad8",
  "submitTime": "2020-06-05",
  "title": "MiniShare\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2020-36747\uff09"
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

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.

Loading…

Detection rules are retrieved from Rulezet.

Loading…

Loading…

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.


Loading…