CNVD-2017-32682

Vulnerability from cnvd - Published: 2017-11-03
VLAI
Title
Adobe RoboHelp for Windows跨站脚本代码漏洞
Description
Adobe RoboHelp for Windows是一套基于Windows平台的专业创作工具。 Adobe RoboHelp for Windows存在跨站脚本漏洞,允许远程攻击者利用漏洞注入恶意脚本或HTML代码,当恶意数据被查看时,可获取敏感信息或劫持用户会话。
Severity
中
Patch Name
Adobe RoboHelp for Windows跨站脚本代码漏洞的补丁
Patch Description
Adobe RoboHelp for Windows是一套基于Windows平台的专业创作工具。 Adobe RoboHelp for Windows存在跨站脚本漏洞,允许远程攻击者利用漏洞注入恶意脚本或HTML代码,当恶意数据被查看时,可获取敏感信息或劫持用户会话。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

用户可参考如下厂商提供的安全补丁以修复该漏洞: https://helpx.adobe.com/security/products/robohelp/apsb17-25.html

Reference
https://helpx.adobe.com/security/products/robohelp/apsb17-25.html
Impacted products
Name
['Adobe RoboHelp <=RH2017.0.1', 'Adobe RoboHelp <=RH12.0.4.460']
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2017-3104"
    }
  },
  "description": "Adobe RoboHelp for Windows\u662f\u4e00\u5957\u57fa\u4e8eWindows\u5e73\u53f0\u7684\u4e13\u4e1a\u521b\u4f5c\u5de5\u5177\u3002\r\n\r\nAdobe RoboHelp for Windows\u5b58\u5728\u8de8\u7ad9\u811a\u672c\u6f0f\u6d1e\uff0c\u5141\u8bb8\u8fdc\u7a0b\u653b\u51fb\u8005\u5229\u7528\u6f0f\u6d1e\u6ce8\u5165\u6076\u610f\u811a\u672c\u6216HTML\u4ee3\u7801\uff0c\u5f53\u6076\u610f\u6570\u636e\u88ab\u67e5\u770b\u65f6\uff0c\u53ef\u83b7\u53d6\u654f\u611f\u4fe1\u606f\u6216\u52ab\u6301\u7528\u6237\u4f1a\u8bdd\u3002",
  "discovererName": "Adobe",
  "formalWay": "\u7528\u6237\u53ef\u53c2\u8003\u5982\u4e0b\u5382\u5546\u63d0\u4f9b\u7684\u5b89\u5168\u8865\u4e01\u4ee5\u4fee\u590d\u8be5\u6f0f\u6d1e\uff1a\r\nhttps://helpx.adobe.com/security/products/robohelp/apsb17-25.html",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2017-32682",
  "openTime": "2017-11-03",
  "patchDescription": "Adobe RoboHelp for Windows\u662f\u4e00\u5957\u57fa\u4e8eWindows\u5e73\u53f0\u7684\u4e13\u4e1a\u521b\u4f5c\u5de5\u5177\u3002\r\n\r\nAdobe RoboHelp for Windows\u5b58\u5728\u8de8\u7ad9\u811a\u672c\u6f0f\u6d1e\uff0c\u5141\u8bb8\u8fdc\u7a0b\u653b\u51fb\u8005\u5229\u7528\u6f0f\u6d1e\u6ce8\u5165\u6076\u610f\u811a\u672c\u6216HTML\u4ee3\u7801\uff0c\u5f53\u6076\u610f\u6570\u636e\u88ab\u67e5\u770b\u65f6\uff0c\u53ef\u83b7\u53d6\u654f\u611f\u4fe1\u606f\u6216\u52ab\u6301\u7528\u6237\u4f1a\u8bdd\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": "Adobe RoboHelp for Windows\u8de8\u7ad9\u811a\u672c\u4ee3\u7801\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": [
      "Adobe RoboHelp \u003c=RH2017.0.1",
      "Adobe RoboHelp \u003c=RH12.0.4.460"
    ]
  },
  "referenceLink": "https://helpx.adobe.com/security/products/robohelp/apsb17-25.html",
  "serverity": "\u4e2d",
  "submitTime": "2017-09-13",
  "title": "Adobe RoboHelp for Windows\u8de8\u7ad9\u811a\u672c\u4ee3\u7801\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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