Vulnerability-Lookup

CNVD-2017-01868

Vulnerability from cnvd - Published: 2017-02-23

Title

NVIDIA Windows GPU Display Driver本地权限提升漏洞（CNVD-2017-01868）

Description

NVIDIA Windows GPU Display Driver是美国英伟达（NVIDIA）公司的一套专用于Windows的图形处理器(GPU)显卡驱动程序。 NVIDIA Windows GPU显示驱动的nvlddmkm.sys中存在安全漏洞。该漏洞源于程序未能正确的验证用户输入的值。攻击者可利用该漏洞造成拒绝服务或获取提升的权限。

Severity

高

Formal description

目前厂商暂未发布修复措施解决此安全问题，建议使用此软件的用户随时关注厂商主页或参考网址以获取解决办法: http://nvidia.custhelp.com/app/answers/detail/a_id/4398

Reference

http://nvidia.custhelp.com/app/answers/detail/a_id/4398

Impacted products

Name
['NVIDIA Quadro', 'NVIDIA NVS', 'NVIDIA GeForce', 'NVIDIA Tesla']

Show details on source website

JSON

To clipboard

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    "cve": {
      "cveNumber": "CVE-2017-0322",
      "cveUrl": "http://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2017-0322"
    }
  },
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  "discovererName": "NVIDIA",
  "formalWay": "\u76ee\u524d\u5382\u5546\u6682\u672a\u53d1\u5e03\u4fee\u590d\u63aa\u65bd\u89e3\u51b3\u6b64\u5b89\u5168\u95ee\u9898\uff0c\u5efa\u8bae\u4f7f\u7528\u6b64\u8f6f\u4ef6\u7684\u7528\u6237\u968f\u65f6\u5173\u6ce8\u5382\u5546\u4e3b\u9875\u6216\u53c2\u8003\u7f51\u5740\u4ee5\u83b7\u53d6\u89e3\u51b3\u529e\u6cd5:\r\nhttp://nvidia.custhelp.com/app/answers/detail/a_id/4398",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2017-01868",
  "openTime": "2017-02-23",
  "products": {
    "product": [
      "NVIDIA Quadro",
      "NVIDIA NVS",
      "NVIDIA GeForce",
      "NVIDIA Tesla"
    ]
  },
  "referenceLink": "http://nvidia.custhelp.com/app/answers/detail/a_id/4398",
  "serverity": "\u9ad8",
  "submitTime": "2017-02-16",
  "title": "NVIDIA Windows GPU Display Driver\u672c\u5730\u6743\u9650\u63d0\u5347\u6f0f\u6d1e\uff08CNVD-2017-01868\uff09"
}

CVE-2017-0322 (GCVE-0-2017-0322)

Vulnerability from cvelistv5 – Published: 2017-02-15 23:00 – Updated: 2024-08-05 13:03

Summary

All versions of NVIDIA Windows GPU Display Driver contain a vulnerability in the kernel mode layer (nvlddmkm.sys) handler where a value passed from a user to the driver is not correctly validated and used as the index to an array, leading to denial of service or potential escalation of privileges.

Severity

No CVSS data available.

CWE

Denial of Service (BSOD), Escalation of Privileges

Assigner

nvidia mitre root

References

1 reference

URL	Tags
http://nvidia.custhelp.com/app/answers/detail/a_id/4398	x_refsource_CONFIRM

Impacted products

1 product

Vendor	Product	Version
Nvidia Corporation	Windows GPU Display Driver	Affected: All

Date Public

2017-02-14 00:00

Show details on NVD website

JSON

To clipboard

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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.

Detection rules are retrieved from Rulezet.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

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.