Vulnerability-Lookup

CNVD-2017-32913

Vulnerability from cnvd - Published: 2017-11-07

Title

Microsoft Windows Hyper-V信息泄露漏洞（CNVD-2017-32913）

Description

Microsoft Windows 10和Windows Server 2016都是美国微软（Microsoft）公司的产品。前者是一套供个人电脑使用的操作系统，后者是一套服务器操作系统。Hyper-V component是其中的一个虚拟化组件。 Microsoft Windows 10版本1607和Windows Server 2016中的Hyper-V组件存在信息泄露漏洞，该漏洞源于程序未能正确的验证用户输入。攻击者可借助特制的应用程序利用该漏洞获取内存信息。

Severity

低

Patch Name

Microsoft Windows Hyper-V信息泄露漏洞（CNVD-2017-32913）的补丁

Patch Description

Formal description

目前厂商已发布升级补丁以修复漏洞，补丁获取链接： https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2017-8711

Reference

https://nvd.nist.gov/vuln/detail/CVE-2017-8711 http://www.securityfocus.com/bid/100794

Impacted products

Name
['Microsoft Windows 10 1607', 'Microsoft Windows Server 2016']

Show details on source website

JSON

To clipboard

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    "bid": {
      "bidNumber": "100794"
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    "cve": {
      "cveNumber": "CVE-2017-8711"
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  "discovererName": "Jordan Rabet, Microsoft Offensive Security Research Team.",
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  "number": "CNVD-2017-32913",
  "openTime": "2017-11-07",
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  "patchName": "Microsoft Windows Hyper-V\u4fe1\u606f\u6cc4\u9732\u6f0f\u6d1e\uff08CNVD-2017-32913\uff09\u7684\u8865\u4e01",
  "products": {
    "product": [
      "Microsoft Windows 10 1607",
      "Microsoft Windows Server 2016"
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  "serverity": "\u4f4e",
  "submitTime": "2017-09-13",
  "title": "Microsoft Windows Hyper-V\u4fe1\u606f\u6cc4\u9732\u6f0f\u6d1e\uff08CNVD-2017-32913\uff09"
}

CVE-2017-8711 (GCVE-0-2017-8711)

Vulnerability from cvelistv5 – Published: 2017-09-13 01:00 – Updated: 2024-09-16 17:13

Summary

The Windows Hyper-V component on Microsoft Windows 10 1607 and Windows Server 2016 allows an information disclosure vulnerability when it fails to properly validate input from an authenticated user on a guest operating system, aka "Hyper-V Information Disclosure Vulnerability". This CVE ID is unique from CVE-2017-8707, CVE-2017-8706, CVE-2017-8712, and CVE-2017-8713.

Severity

No CVSS data available.

CWE

Information Disclosure

Assigner

microsoft mitre root

References

3 references

URL	Tags
https://portal.msrc.microsoft.com/en-US/security-…	x_refsource_CONFIRM
http://www.securitytracker.com/id/1039317	vdb-entryx_refsource_SECTRACK
http://www.securityfocus.com/bid/100794	vdb-entryx_refsource_BID

Impacted products

1 product

Vendor	Product	Version
Microsoft Corporation	Windows Hyper-V	Affected: Microsoft Windows 10 1607 and Windows Server 2016

Date Public

2017-09-12 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.