CNVD-2026-26110

Vulnerability from cnvd - Published: 2026-07-02
VLAI
Title
Mozilla Firefox for iOS输入验证错误漏洞(CNVD-2026-26110)
Description
Mozilla Firefox for iOS是Mozilla基金会的移动端网页浏览器。 Mozilla Firefox for存在输入验证错误漏洞,该漏洞源于附加cookie到PDF请求时使用部分域匹配,攻击者可利用该漏洞导致后缀域中的恶意站点接收目标站点的cookie。
Severity
中
Patch Name
Mozilla Firefox for iOS输入验证错误漏洞(CNVD-2026-26110)的补丁
Patch Description
Mozilla Firefox for iOS是Mozilla基金会的移动端网页浏览器。 Mozilla Firefox for存在输入验证错误漏洞,该漏洞源于附加cookie到PDF请求时使用部分域匹配,攻击者可利用该漏洞导致后缀域中的恶意站点接收目标站点的cookie。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://bugzilla.mozilla.org/show_bug.cgi?id=2042909

Reference
https://bugzilla.mozilla.org/show_bug.cgi?id=2042909
Impacted products
Name
['Mozilla Firefox for iOS', 'Mozilla Firefox for iOS <152.0']
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2026-53899",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2026-53899"
    }
  },
  "description": "Mozilla Firefox for iOS\u662fMozilla\u57fa\u91d1\u4f1a\u7684\u79fb\u52a8\u7aef\u7f51\u9875\u6d4f\u89c8\u5668\u3002\n\nMozilla Firefox for\u5b58\u5728\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u9644\u52a0cookie\u5230PDF\u8bf7\u6c42\u65f6\u4f7f\u7528\u90e8\u5206\u57df\u5339\u914d\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u540e\u7f00\u57df\u4e2d\u7684\u6076\u610f\u7ad9\u70b9\u63a5\u6536\u76ee\u6807\u7ad9\u70b9\u7684cookie\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://bugzilla.mozilla.org/show_bug.cgi?id=2042909",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2026-26110",
  "openTime": "2026-07-02",
  "patchDescription": "Mozilla Firefox for iOS\u662fMozilla\u57fa\u91d1\u4f1a\u7684\u79fb\u52a8\u7aef\u7f51\u9875\u6d4f\u89c8\u5668\u3002\r\n\r\nMozilla Firefox for\u5b58\u5728\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u9644\u52a0cookie\u5230PDF\u8bf7\u6c42\u65f6\u4f7f\u7528\u90e8\u5206\u57df\u5339\u914d\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u540e\u7f00\u57df\u4e2d\u7684\u6076\u610f\u7ad9\u70b9\u63a5\u6536\u76ee\u6807\u7ad9\u70b9\u7684cookie\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": "Mozilla Firefox for iOS\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\uff08CNVD-2026-26110\uff09\u7684\u8865\u4e01",
  "products": {
    "product": [
      "Mozilla Firefox for iOS",
      "Mozilla Firefox for iOS \u003c152.0"
    ]
  },
  "referenceLink": "https://bugzilla.mozilla.org/show_bug.cgi?id=2042909",
  "serverity": "\u4e2d",
  "submitTime": "2026-06-22",
  "title": "Mozilla Firefox for iOS\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\uff08CNVD-2026-26110\uff09"
}



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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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