CNVD-2026-17372

Vulnerability from cnvd - Published: 2026-04-10
VLAI
Title
Apache IoTDB输入验证不当漏洞
Description
Apache IoTDB是Apache Software Foundation开发的一款开源时间序列数据库,用于物联网场景中的大规模时间序列数据存储与分析。 Apache IoTDB存在输入验证不当漏洞。该漏洞产生的原因是系统在处理用户输入数据时未进行充分校验。攻击者可利用该漏洞构造恶意请求触发远程代码执行。
Severity
高
Patch Name
Apache IoTDB输入验证不当漏洞的补丁
Patch Description
Apache IoTDB是Apache Software Foundation开发的一款开源时间序列数据库,用于物联网场景中的大规模时间序列数据存储与分析。 Apache IoTDB存在输入验证不当漏洞。该漏洞产生的原因是系统在处理用户输入数据时未进行充分校验。攻击者可利用该漏洞构造恶意请求触发远程代码执行。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: http://www.openwall.com/lists/oss-security/2026/03/09/4

Reference
https://nvd.nist.gov/vuln/detail/CVE-2026-24713
Impacted products
Name
['Apache IoTDB >=1.0.0,<1.3.7', 'Apache IoTDB >=2.0.0,<2.0.7']
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2026-24713",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2026-24713"
    }
  },
  "description": "Apache IoTDB\u662fApache Software Foundation\u5f00\u53d1\u7684\u4e00\u6b3e\u5f00\u6e90\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5e93\uff0c\u7528\u4e8e\u7269\u8054\u7f51\u573a\u666f\u4e2d\u7684\u5927\u89c4\u6a21\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5b58\u50a8\u4e0e\u5206\u6790\u3002\n\nApache IoTDB\u5b58\u5728\u8f93\u5165\u9a8c\u8bc1\u4e0d\u5f53\u6f0f\u6d1e\u3002\u8be5\u6f0f\u6d1e\u4ea7\u751f\u7684\u539f\u56e0\u662f\u7cfb\u7edf\u5728\u5904\u7406\u7528\u6237\u8f93\u5165\u6570\u636e\u65f6\u672a\u8fdb\u884c\u5145\u5206\u6821\u9a8c\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u6784\u9020\u6076\u610f\u8bf7\u6c42\u89e6\u53d1\u8fdc\u7a0b\u4ee3\u7801\u6267\u884c\u3002",
  "formalWay": "\u5382\u5546\u5df2\u53d1\u5e03\u4e86\u6f0f\u6d1e\u4fee\u590d\u7a0b\u5e8f\uff0c\u8bf7\u53ca\u65f6\u5173\u6ce8\u66f4\u65b0\uff1a\r\nhttp://www.openwall.com/lists/oss-security/2026/03/09/4",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2026-17372",
  "openTime": "2026-04-10",
  "patchDescription": "Apache IoTDB\u662fApache Software Foundation\u5f00\u53d1\u7684\u4e00\u6b3e\u5f00\u6e90\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5e93\uff0c\u7528\u4e8e\u7269\u8054\u7f51\u573a\u666f\u4e2d\u7684\u5927\u89c4\u6a21\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5b58\u50a8\u4e0e\u5206\u6790\u3002\n\nApache IoTDB\u5b58\u5728\u8f93\u5165\u9a8c\u8bc1\u4e0d\u5f53\u6f0f\u6d1e\u3002\u8be5\u6f0f\u6d1e\u4ea7\u751f\u7684\u539f\u56e0\u662f\u7cfb\u7edf\u5728\u5904\u7406\u7528\u6237\u8f93\u5165\u6570\u636e\u65f6\u672a\u8fdb\u884c\u5145\u5206\u6821\u9a8c\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u6784\u9020\u6076\u610f\u8bf7\u6c42\u89e6\u53d1\u8fdc\u7a0b\u4ee3\u7801\u6267\u884c\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 IoTDB\u8f93\u5165\u9a8c\u8bc1\u4e0d\u5f53\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": [
      "Apache IoTDB \u003e=1.0.0\uff0c\u003c1.3.7",
      "Apache IoTDB \u003e=2.0.0\uff0c\u003c2.0.7"
    ]
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2026-24713",
  "serverity": "\u9ad8",
  "submitTime": "2026-03-16",
  "title": "Apache IoTDB\u8f93\u5165\u9a8c\u8bc1\u4e0d\u5f53\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

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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.
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  • 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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