CNVD-2017-06742

Vulnerability from cnvd - Published: 2017-05-17
VLAI
Title
Linux Kernel堆缓冲区溢出漏洞(CNVD-2017-06742)
Description
Linux kernel是美国Linux基金会发布的操作系统Linux所使用的内核。 Linux内核存在堆缓冲区溢出漏洞,本地攻击者可利用该漏洞在受影响的系统中执行任意代码。 失败的利用尝试可能导致拒绝服务攻击。
Severity
Formal description

目前没有详细的解决方案提供: http://www.linux.com/

Reference
http://www.securityfocus.com/bid/98014
Impacted products
Name
Linux kernel 4.10.12
Show details on source website

{
  "bids": {
    "bid": {
      "bidNumber": "98014"
    }
  },
  "cves": {
    "cve": {
      "cveNumber": "CVE-2017-7477"
    }
  },
  "description": "Linux kernel\u662f\u7f8e\u56fdLinux\u57fa\u91d1\u4f1a\u53d1\u5e03\u7684\u64cd\u4f5c\u7cfb\u7edfLinux\u6240\u4f7f\u7528\u7684\u5185\u6838\u3002\r\n\r\nLinux\u5185\u6838\u5b58\u5728\u5806\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u672c\u5730\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5728\u53d7\u5f71\u54cd\u7684\u7cfb\u7edf\u4e2d\u6267\u884c\u4efb\u610f\u4ee3\u7801\u3002 \u5931\u8d25\u7684\u5229\u7528\u5c1d\u8bd5\u53ef\u80fd\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\u653b\u51fb\u3002",
  "discovererName": "Andrej Nemec",
  "formalWay": "\u76ee\u524d\u6ca1\u6709\u8be6\u7ec6\u7684\u89e3\u51b3\u65b9\u6848\u63d0\u4f9b\uff1a\r\nhttp://www.linux.com/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2017-06742",
  "openTime": "2017-05-17",
  "products": {
    "product": "Linux kernel 4.10.12"
  },
  "referenceLink": "http://www.securityfocus.com/bid/98014",
  "serverity": "\u4e2d",
  "submitTime": "2017-04-26",
  "title": "Linux Kernel\u5806\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2017-06742\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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Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
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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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