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CNVD-2017-13797

Vulnerability from cnvd - Published: 2017-07-11
VLAI
Title
Microsoft Skype栈缓冲区溢出漏洞
Description
Microsoft Skype是美国微软(Microsoft)公司的一套即时通讯软件。 Microsoft Skype中存在栈缓冲区溢出漏洞。攻击者可利用该漏洞造成拒绝服务(软件崩溃),执行代码。
Severity
Patch Name
Microsoft Skype栈缓冲区溢出漏洞的补丁
Patch Description
Microsoft Skype是美国微软(Microsoft)公司的一套即时通讯软件。 Microsoft Skype中存在栈缓冲区溢出漏洞。攻击者可利用该漏洞造成拒绝服务(软件崩溃),执行代码。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,补丁获取链接: https://www.skype.com/de/download-skype/skype-for-windows/downloading/

Reference
https://www.vulnerability-lab.com/get_content.php?id=2071 https://www.vulnerability-db.com/?q=articles/2017/05/28/stack-buffer-overflow-zero-day-vulnerability-uncovered-microsoft-skype-v72-v735
Impacted products
Name
['Microsoft skype 7.2,<7.37', 'Microsoft skype 7.35,<7.37', 'Microsoft skype 7.36,<7.37']
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2017-9948"
    }
  },
  "description": "Microsoft Skype\u662f\u7f8e\u56fd\u5fae\u8f6f\uff08Microsoft\uff09\u516c\u53f8\u7684\u4e00\u5957\u5373\u65f6\u901a\u8baf\u8f6f\u4ef6\u3002\r\n\r\nMicrosoft Skype\u4e2d\u5b58\u5728\u6808\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u9020\u6210\u62d2\u7edd\u670d\u52a1\uff08\u8f6f\u4ef6\u5d29\u6e83\uff09\uff0c\u6267\u884c\u4ee3\u7801\u3002",
  "discovererName": "Benjamin Kunz-Mejri",
  "formalWay": "\u76ee\u524d\u5382\u5546\u5df2\u53d1\u5e03\u5347\u7ea7\u8865\u4e01\u4ee5\u4fee\u590d\u6f0f\u6d1e\uff0c\u8865\u4e01\u83b7\u53d6\u94fe\u63a5\uff1a\r\nhttps://www.skype.com/de/download-skype/skype-for-windows/downloading/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2017-13797",
  "openTime": "2017-07-11",
  "patchDescription": "Microsoft Skype\u662f\u7f8e\u56fd\u5fae\u8f6f\uff08Microsoft\uff09\u516c\u53f8\u7684\u4e00\u5957\u5373\u65f6\u901a\u8baf\u8f6f\u4ef6\u3002\r\n\r\nMicrosoft Skype\u4e2d\u5b58\u5728\u6808\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u9020\u6210\u62d2\u7edd\u670d\u52a1\uff08\u8f6f\u4ef6\u5d29\u6e83\uff09\uff0c\u6267\u884c\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": "Microsoft Skype\u6808\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": [
      "Microsoft skype 7.2\uff0c\u003c7.37",
      "Microsoft skype 7.35\uff0c\u003c7.37",
      "Microsoft skype 7.36\uff0c\u003c7.37"
    ]
  },
  "referenceLink": "https://www.vulnerability-lab.com/get_content.php?id=2071\r\nhttps://www.vulnerability-db.com/?q=articles/2017/05/28/stack-buffer-overflow-zero-day-vulnerability-uncovered-microsoft-skype-v72-v735",
  "serverity": "\u9ad8",
  "submitTime": "2017-06-29",
  "title": "Microsoft Skype\u6808\u7f13\u51b2\u533a\u6ea2\u51fa\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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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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