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CNVD-2024-35188

Vulnerability from cnvd - Published: 2024-08-14
VLAI
Title
Apache StreamPipes安全绕过漏洞
Description
Apache StreamPipes是美国阿帕奇(Apache)基金会的一个自助式(工业)物联网工具箱,使非技术用户能够连接、分析和探索IIoT数据流。 Apache StreamPipes存在安全绕过漏洞,攻击者可利用该漏洞创建具有相同电子邮件地址的多个帐户,并破坏StreamPipe的用户管理。
Severity
Patch Name
Apache StreamPipes安全绕过漏洞的补丁
Patch Description
Apache StreamPipes是美国阿帕奇(Apache)基金会的一个自助式(工业)物联网工具箱,使非技术用户能够连接、分析和探索IIoT数据流。 Apache StreamPipes存在安全绕过漏洞,攻击者可利用该漏洞创建具有相同电子邮件地址的多个帐户,并破坏StreamPipe的用户管理。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://streampipes.apache.org/

Reference
https://exchange.xforce.ibmcloud.com/vulnerabilities/298030
Impacted products
Name
Apache StreamPipes <0.95.0
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2024-30471"
    }
  },
  "description": "Apache StreamPipes\u662f\u7f8e\u56fd\u963f\u5e15\u5947\uff08Apache\uff09\u57fa\u91d1\u4f1a\u7684\u4e00\u4e2a\u81ea\u52a9\u5f0f\uff08\u5de5\u4e1a\uff09\u7269\u8054\u7f51\u5de5\u5177\u7bb1\uff0c\u4f7f\u975e\u6280\u672f\u7528\u6237\u80fd\u591f\u8fde\u63a5\u3001\u5206\u6790\u548c\u63a2\u7d22IIoT\u6570\u636e\u6d41\u3002\n\nApache StreamPipes\u5b58\u5728\u5b89\u5168\u7ed5\u8fc7\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u521b\u5efa\u5177\u6709\u76f8\u540c\u7535\u5b50\u90ae\u4ef6\u5730\u5740\u7684\u591a\u4e2a\u5e10\u6237\uff0c\u5e76\u7834\u574fStreamPipe\u7684\u7528\u6237\u7ba1\u7406\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://streampipes.apache.org/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2024-35188",
  "openTime": "2024-08-14",
  "patchDescription": "Apache StreamPipes\u662f\u7f8e\u56fd\u963f\u5e15\u5947\uff08Apache\uff09\u57fa\u91d1\u4f1a\u7684\u4e00\u4e2a\u81ea\u52a9\u5f0f\uff08\u5de5\u4e1a\uff09\u7269\u8054\u7f51\u5de5\u5177\u7bb1\uff0c\u4f7f\u975e\u6280\u672f\u7528\u6237\u80fd\u591f\u8fde\u63a5\u3001\u5206\u6790\u548c\u63a2\u7d22IIoT\u6570\u636e\u6d41\u3002\r\n\r\nApache StreamPipes\u5b58\u5728\u5b89\u5168\u7ed5\u8fc7\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u521b\u5efa\u5177\u6709\u76f8\u540c\u7535\u5b50\u90ae\u4ef6\u5730\u5740\u7684\u591a\u4e2a\u5e10\u6237\uff0c\u5e76\u7834\u574fStreamPipe\u7684\u7528\u6237\u7ba1\u7406\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": "Apache StreamPipes\u5b89\u5168\u7ed5\u8fc7\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "Apache StreamPipes \u003c0.95.0"
  },
  "referenceLink": "https://exchange.xforce.ibmcloud.com/vulnerabilities/298030",
  "serverity": "\u4e2d",
  "submitTime": "2024-07-17",
  "title": "Apache StreamPipes\u5b89\u5168\u7ed5\u8fc7\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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Detection rules are retrieved from Rulezet.

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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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