Vulnerability-Lookup

CNVD-2025-06287

Vulnerability from cnvd - Published: 2025-04-01

Title

NVIDIA Windows GPU Display Driver输入验证错误漏洞

Description

Nvidia NVIDIA Windows GPU Display Driver是美国英伟达（Nvidia）公司的一款专用于Windows平台的图形处理器（GPU）显卡驱动程序。 NVIDIA Windows GPU Display Driver存在输入验证错误漏洞，该漏洞源于网络系统或产品对系统资源的管理不当。攻击者可利用该漏洞导致拒绝服务。

Severity

中

Patch Name

NVIDIA Windows GPU Display Driver输入验证错误漏洞的补丁

Patch Description

Formal description

目前厂商已发布升级程序修复该安全问题，详情见厂商官网: https://www.cybersecurity-help.cz/vdb/SB2021102701

Reference

https://nvd.nist.gov/vuln/detail/CVE-2021-1117

Impacted products

Name
['NVIDIA Windows GPU Display Driver >=390，<392.68', 'NVIDIA Windows GPU Display Driver >=460，<463.15', 'NVIDIA Windows GPU Display Driver >=470，<472.39', 'NVIDIA Windows GPU Display Driver >=495，<496.49']

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JSON

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  "number": "CNVD-2025-06287",
  "openTime": "2025-04-01",
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  "products": {
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      "NVIDIA Windows GPU Display Driver \u003e=460\uff0c\u003c463.15",
      "NVIDIA Windows GPU Display Driver \u003e=470\uff0c\u003c472.39",
      "NVIDIA Windows GPU Display Driver \u003e=495\uff0c\u003c496.49"
    ]
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2021-1117",
  "serverity": "\u4e2d",
  "submitTime": "2021-10-31",
  "title": "NVIDIA Windows GPU Display Driver\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e"
}

CVE-2021-1117 (GCVE-0-2021-1117)

Vulnerability from cvelistv5 – Published: 2021-10-27 20:35 – Updated: 2024-08-03 15:55

Summary

Windows contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape, where an attacker through specific configuration and with local unprivileged system access may cause improper input validation, which may lead to denial of service.

Severity

4.7 (Medium)


                        
                          CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:H

CWE

CWE-129 - Improper Validation of Array Index

Assigner

nvidia mitre root

References

1 reference

URL	Tags
https://nvidia.custhelp.com/app/answers/detail/a_…	x_refsource_CONFIRM

Impacted products

1 product

Vendor	Product	Version
NVIDIA	NVIDIA GPU Display Driver	Affected: All GPU Driver versions

Show details on NVD website

JSON

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        "providerMetadata": {
          "dateUpdated": "2024-08-03T15:55:18.525Z",
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            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 4.7,
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            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
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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.