CNVD-2025-05698

Vulnerability from cnvd - Published: 2025-03-25
VLAI
Title
Apache StreamPipes权限提升漏洞(CNVD-2025-05698)
Description
Apache StreamPipes是一个开源的自助式(工业)物联网工具箱,使用户能够连接、分析和探索IIoT数据流。 Apache StreamPipes 0.95.1及之前版本存在安全漏洞,该漏洞源于未对资源ID的过滤和验证不严谨。攻击者可以利用该漏洞访问未经授权的资源。
Severity
中
Patch Name
Apache StreamPipes权限提升漏洞(CNVD-2025-05698)的补丁
Patch Description
Apache StreamPipes是一个开源的自助式(工业)物联网工具箱,使用户能够连接、分析和探索IIoT数据流。 Apache StreamPipes 0.95.1及之前版本存在安全漏洞,该漏洞源于未对资源ID的过滤和验证不严谨。攻击者可以利用该漏洞访问未经授权的资源。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

厂商已提供漏洞修复方案,请关注厂商主页更新: https://streampipes.apache.org/

Reference
https://lists.apache.org/thread/j14w6wghlwwrgfgc6hoz9f94fwxtlgzh
Impacted products
Name
Apache StreamPipes <=0.95.1
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2024-24778",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2024-24778"
    }
  },
  "description": "Apache StreamPipes\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u81ea\u52a9\u5f0f(\u5de5\u4e1a)\u7269\u8054\u7f51\u5de5\u5177\u7bb1\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u8fde\u63a5\u3001\u5206\u6790\u548c\u63a2\u7d22IIoT\u6570\u636e\u6d41\u3002\n\nApache StreamPipes 0.95.1\u53ca\u4e4b\u524d\u7248\u672c\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u672a\u5bf9\u8d44\u6e90ID\u7684\u8fc7\u6ee4\u548c\u9a8c\u8bc1\u4e0d\u4e25\u8c28\u3002\u653b\u51fb\u8005\u53ef\u4ee5\u5229\u7528\u8be5\u6f0f\u6d1e\u8bbf\u95ee\u672a\u7ecf\u6388\u6743\u7684\u8d44\u6e90\u3002",
  "formalWay": "\u5382\u5546\u5df2\u63d0\u4f9b\u6f0f\u6d1e\u4fee\u590d\u65b9\u6848\uff0c\u8bf7\u5173\u6ce8\u5382\u5546\u4e3b\u9875\u66f4\u65b0\uff1a\r\nhttps://streampipes.apache.org/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2025-05698",
  "openTime": "2025-03-25",
  "patchDescription": "Apache StreamPipes\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u81ea\u52a9\u5f0f(\u5de5\u4e1a)\u7269\u8054\u7f51\u5de5\u5177\u7bb1\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u8fde\u63a5\u3001\u5206\u6790\u548c\u63a2\u7d22IIoT\u6570\u636e\u6d41\u3002\r\n\r\nApache StreamPipes 0.95.1\u53ca\u4e4b\u524d\u7248\u672c\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u672a\u5bf9\u8d44\u6e90ID\u7684\u8fc7\u6ee4\u548c\u9a8c\u8bc1\u4e0d\u4e25\u8c28\u3002\u653b\u51fb\u8005\u53ef\u4ee5\u5229\u7528\u8be5\u6f0f\u6d1e\u8bbf\u95ee\u672a\u7ecf\u6388\u6743\u7684\u8d44\u6e90\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\u6743\u9650\u63d0\u5347\u6f0f\u6d1e\uff08CNVD-2025-05698\uff09\u7684\u8865\u4e01",
  "products": {
    "product": "Apache StreamPipes \u003c=0.95.1"
  },
  "referenceLink": "https://lists.apache.org/thread/j14w6wghlwwrgfgc6hoz9f94fwxtlgzh",
  "serverity": "\u4e2d",
  "submitTime": "2025-03-07",
  "title": "Apache StreamPipes\u6743\u9650\u63d0\u5347\u6f0f\u6d1e\uff08CNVD-2025-05698\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

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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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