GCVE Workshop - 22 September 2026 (14:00-18:00), Luxembourg Before The Vulnopticon Conference - Registration

CNVD-2025-16773

Vulnerability from cnvd - Published: 2025-07-24
VLAI
Title
Microsoft SQL Server存在未明漏洞(CNVD-2025-16773)
Description
Microsoft SQL Server是美国微软(Microsoft)公司的一套应用在Microsoft Windows系统下的大型商业数据库系统。 Microsoft SQL Server存在安全漏洞。攻击者利用该漏洞可以获取敏感信息。
Severity
Patch Name
Microsoft SQL Server存在未明漏洞(CNVD-2025-16773)的补丁
Patch Description
Microsoft SQL Server是美国微软(Microsoft)公司的一套应用在Microsoft Windows系统下的大型商业数据库系统。 Microsoft SQL Server存在安全漏洞。攻击者利用该漏洞可以获取敏感信息。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://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-49718

Reference
https://nvd.nist.gov/vuln/detail/CVE-2025-49718
Impacted products
Name
['Microsoft SQL Server 2019 (GDR)', 'Microsoft SQL Server 2022 (GDR)', 'Microsoft SQL Server 2019 (CU 32)', 'Microsoft SQL Server 2022 (CU 19)']
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2025-49718",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2025-49718"
    }
  },
  "description": "Microsoft SQL Server\u662f\u7f8e\u56fd\u5fae\u8f6f\uff08Microsoft\uff09\u516c\u53f8\u7684\u4e00\u5957\u5e94\u7528\u5728Microsoft Windows\u7cfb\u7edf\u4e0b\u7684\u5927\u578b\u5546\u4e1a\u6570\u636e\u5e93\u7cfb\u7edf\u3002\n\nMicrosoft SQL Server\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u5229\u7528\u8be5\u6f0f\u6d1e\u53ef\u4ee5\u83b7\u53d6\u654f\u611f\u4fe1\u606f\u3002",
  "formalWay": "\u5382\u5546\u5df2\u53d1\u5e03\u4e86\u6f0f\u6d1e\u4fee\u590d\u7a0b\u5e8f\uff0c\u8bf7\u53ca\u65f6\u5173\u6ce8\u66f4\u65b0\uff1a\r\nhttps://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-49718",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2025-16773",
  "openTime": "2025-07-24",
  "patchDescription": "Microsoft SQL Server\u662f\u7f8e\u56fd\u5fae\u8f6f\uff08Microsoft\uff09\u516c\u53f8\u7684\u4e00\u5957\u5e94\u7528\u5728Microsoft Windows\u7cfb\u7edf\u4e0b\u7684\u5927\u578b\u5546\u4e1a\u6570\u636e\u5e93\u7cfb\u7edf\u3002\r\n\r\nMicrosoft SQL Server\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u5229\u7528\u8be5\u6f0f\u6d1e\u53ef\u4ee5\u83b7\u53d6\u654f\u611f\u4fe1\u606f\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": "Microsoft SQL Server\u5b58\u5728\u672a\u660e\u6f0f\u6d1e\uff08CNVD-2025-16773\uff09\u7684\u8865\u4e01",
  "products": {
    "product": [
      "Microsoft SQL Server 2019 (GDR)",
      "Microsoft SQL Server 2022 (GDR)",
      "Microsoft SQL Server 2019 (CU 32)",
      "Microsoft SQL Server 2022 (CU 19)"
    ]
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2025-49718",
  "serverity": "\u9ad8",
  "submitTime": "2025-07-21",
  "title": "Microsoft SQL Server\u5b58\u5728\u672a\u660e\u6f0f\u6d1e\uff08CNVD-2025-16773\uff09"
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

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

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.

Loading…

Detection rules are retrieved from Rulezet.

Loading…

Loading…

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.


Loading…