CNVD-2026-16657

Vulnerability from cnvd - Published: 2026-04-10
VLAI
Title
GFI MailEssentials AI Anti-Spoofing配置页面跨站脚本漏洞
Description
GFI MailEssentials AI是美国GFI开源的一个反垃圾邮件与数据泄露防护软件。 GFI MailEssentials AI Anti-Spoofing配置页面存在跨站脚本漏洞,攻击者可利用该漏洞在登录用户的上下文中执行脚本。
Severity
Patch Name
GFI MailEssentials AI Anti-Spoofing配置页面跨站脚本漏洞的补丁
Patch Description
GFI MailEssentials AI是美国GFI开源的一个反垃圾邮件与数据泄露防护软件。 GFI MailEssentials AI Anti-Spoofing配置页面存在跨站脚本漏洞,攻击者可利用该漏洞在登录用户的上下文中执行脚本。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://gfi.ai/products-and-solutions/network-security-solutions/mailessentials/resources/documentation/product-releases

Reference
https://gfi.ai/products-and-solutions/network-security-solutions/mailessentials/resources/documentation/product-releases
Impacted products
Name
GFI MailEssentials AI <22.4
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2026-23616",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2026-23616"
    }
  },
  "description": "GFI MailEssentials AI\u662f\u7f8e\u56fdGFI\u5f00\u6e90\u7684\u4e00\u4e2a\u53cd\u5783\u573e\u90ae\u4ef6\u4e0e\u6570\u636e\u6cc4\u9732\u9632\u62a4\u8f6f\u4ef6\u3002\n\nGFI MailEssentials AI Anti-Spoofing\u914d\u7f6e\u9875\u9762\u5b58\u5728\u8de8\u7ad9\u811a\u672c\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5728\u767b\u5f55\u7528\u6237\u7684\u4e0a\u4e0b\u6587\u4e2d\u6267\u884c\u811a\u672c\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://gfi.ai/products-and-solutions/network-security-solutions/mailessentials/resources/documentation/product-releases",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2026-16657",
  "openTime": "2026-04-10",
  "patchDescription": "GFI MailEssentials AI\u662f\u7f8e\u56fdGFI\u5f00\u6e90\u7684\u4e00\u4e2a\u53cd\u5783\u573e\u90ae\u4ef6\u4e0e\u6570\u636e\u6cc4\u9732\u9632\u62a4\u8f6f\u4ef6\u3002\r\n\r\nGFI MailEssentials AI Anti-Spoofing\u914d\u7f6e\u9875\u9762\u5b58\u5728\u8de8\u7ad9\u811a\u672c\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5728\u767b\u5f55\u7528\u6237\u7684\u4e0a\u4e0b\u6587\u4e2d\u6267\u884c\u811a\u672c\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": "GFI MailEssentials AI Anti-Spoofing\u914d\u7f6e\u9875\u9762\u8de8\u7ad9\u811a\u672c\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "GFI MailEssentials AI \u003c22.4"
  },
  "referenceLink": "https://gfi.ai/products-and-solutions/network-security-solutions/mailessentials/resources/documentation/product-releases",
  "serverity": "\u4e2d",
  "submitTime": "2026-03-02",
  "title": "GFI MailEssentials AI Anti-Spoofing\u914d\u7f6e\u9875\u9762\u8de8\u7ad9\u811a\u672c\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

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.

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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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