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CNVD-2018-02914

Vulnerability from cnvd - Published: 2018-02-07
VLAI
Title
SUPERAntiSpyware Professional Trial拒绝服务漏洞(CNVD-2018-02914)
Description
SUPERAntiSpyware Professional Trial是美国Support.com公司的一套主要用于扫描间谍软件的杀毒工具。 SUPERAntiSpyware Professional Trial 6.0.1254版本中的SASKUTIL.SYS驱动程序文件存在安全漏洞,该漏洞源于程序未能验证IOCtl 0x9C402148的输入值。本地攻击者可利用该漏洞造成拒绝服务。
Severity
Formal description

目前没有详细的解决方案提供: http://www.superantispyware.com/

Reference
https://github.com/ZhiyuanWang-Chengdu-Qihoo360/SUPERAntiSpyware_POC/tree/master/0x9C402148
Impacted products
Name
Support.com SUPERAntiSpyware Professional Trial 6.0.1254
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2018-6474"
    }
  },
  "description": "SUPERAntiSpyware Professional Trial\u662f\u7f8e\u56fdSupport.com\u516c\u53f8\u7684\u4e00\u5957\u4e3b\u8981\u7528\u4e8e\u626b\u63cf\u95f4\u8c0d\u8f6f\u4ef6\u7684\u6740\u6bd2\u5de5\u5177\u3002\r\n\r\nSUPERAntiSpyware Professional Trial 6.0.1254\u7248\u672c\u4e2d\u7684SASKUTIL.SYS\u9a71\u52a8\u7a0b\u5e8f\u6587\u4ef6\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7a0b\u5e8f\u672a\u80fd\u9a8c\u8bc1IOCtl 0x9C402148\u7684\u8f93\u5165\u503c\u3002\u672c\u5730\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u9020\u6210\u62d2\u7edd\u670d\u52a1\u3002",
  "discovererName": "unknown",
  "formalWay": "\u76ee\u524d\u6ca1\u6709\u8be6\u7ec6\u7684\u89e3\u51b3\u65b9\u6848\u63d0\u4f9b\uff1a\r\nhttp://www.superantispyware.com/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2018-02914",
  "openTime": "2018-02-07",
  "products": {
    "product": "Support.com SUPERAntiSpyware Professional Trial 6.0.1254"
  },
  "referenceLink": "https://github.com/ZhiyuanWang-Chengdu-Qihoo360/SUPERAntiSpyware_POC/tree/master/0x9C402148",
  "serverity": "\u4e2d",
  "submitTime": "2018-02-02",
  "title": "SUPERAntiSpyware Professional Trial\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e\uff08CNVD-2018-02914\uff09"
}



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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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