CNVD-2019-28288

Vulnerability from cnvd - Published: 2019-08-21
VLAI
Title
NVIDIA Windows GPU Display Driver kernel mode layer handler输入验证错误漏洞
Description
NVIDIA Windows GPU显示驱动程序是用于Windows系统的显示驱动程序。 NVIDIA Windows GPU显示驱动程序所有版本的DxgkDdiEscape的内核模式层(nvlddmkm.sys)处理程序存在拒绝服务漏洞。该漏洞源于软件对API函数或数据结构的使用依赖于并非始终保证有效的属性。攻击者可利用该漏洞导致拒绝服务。
Severity
Patch Name
NVIDIA Windows GPU Display Driver kernel mode layer handler输入验证错误漏洞的补丁
Patch Description
NVIDIA Windows GPU显示驱动程序是用于Windows系统的显示驱动程序。 NVIDIA Windows GPU显示驱动程序所有版本的DxgkDdiEscape的内核模式层(nvlddmkm.sys)处理程序存在拒绝服务漏洞。该漏洞源于软件对API函数或数据结构的使用依赖于并非始终保证有效的属性。攻击者可利用该漏洞导致拒绝服务。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://nvidia.custhelp.com/app/answers/detail/a_id/4841

Reference
https://nvd.nist.gov/vuln/detail/CVE-2019-5686
Impacted products
Name
NVIDIA Windows GPU显示驱动程序
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2019-5686",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2019-5686"
    }
  },
  "description": "NVIDIA Windows GPU\u663e\u793a\u9a71\u52a8\u7a0b\u5e8f\u662f\u7528\u4e8eWindows\u7cfb\u7edf\u7684\u663e\u793a\u9a71\u52a8\u7a0b\u5e8f\u3002\n\nNVIDIA Windows GPU\u663e\u793a\u9a71\u52a8\u7a0b\u5e8f\u6240\u6709\u7248\u672c\u7684DxgkDdiEscape\u7684\u5185\u6838\u6a21\u5f0f\u5c42(nvlddmkm.sys)\u5904\u7406\u7a0b\u5e8f\u5b58\u5728\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e\u3002\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u8f6f\u4ef6\u5bf9API\u51fd\u6570\u6216\u6570\u636e\u7ed3\u6784\u7684\u4f7f\u7528\u4f9d\u8d56\u4e8e\u5e76\u975e\u59cb\u7ec8\u4fdd\u8bc1\u6709\u6548\u7684\u5c5e\u6027\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\u3002",
  "discovererName": "Piotr Bania",
  "formalWay": "\u5382\u5546\u5df2\u53d1\u5e03\u4e86\u6f0f\u6d1e\u4fee\u590d\u7a0b\u5e8f\uff0c\u8bf7\u53ca\u65f6\u5173\u6ce8\u66f4\u65b0\uff1ahttps://nvidia.custhelp.com/app/answers/detail/a_id/4841",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2019-28288",
  "openTime": "2019-08-21",
  "patchDescription": "NVIDIA Windows GPU\u663e\u793a\u9a71\u52a8\u7a0b\u5e8f\u662f\u7528\u4e8eWindows\u7cfb\u7edf\u7684\u663e\u793a\u9a71\u52a8\u7a0b\u5e8f\u3002\r\n\r\nNVIDIA Windows GPU\u663e\u793a\u9a71\u52a8\u7a0b\u5e8f\u6240\u6709\u7248\u672c\u7684DxgkDdiEscape\u7684\u5185\u6838\u6a21\u5f0f\u5c42(nvlddmkm.sys)\u5904\u7406\u7a0b\u5e8f\u5b58\u5728\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e\u3002\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u8f6f\u4ef6\u5bf9API\u51fd\u6570\u6216\u6570\u636e\u7ed3\u6784\u7684\u4f7f\u7528\u4f9d\u8d56\u4e8e\u5e76\u975e\u59cb\u7ec8\u4fdd\u8bc1\u6709\u6548\u7684\u5c5e\u6027\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\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": "NVIDIA Windows GPU Display Driver kernel mode layer handler\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "NVIDIA Windows GPU\u663e\u793a\u9a71\u52a8\u7a0b\u5e8f"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2019-5686",
  "serverity": "\u4e2d",
  "submitTime": "2019-08-07",
  "title": "NVIDIA Windows GPU Display Driver kernel mode layer handler\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\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

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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.
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  • 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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