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CNVD-2022-00612

Vulnerability from cnvd - Published: 2022-01-05
VLAI
Title
Nch Software UTI Mutual fund Android存在未明漏洞
Description
Nch Software Uti Mutual Fund是澳大利亚Nch Software公司的一个投资共同基金的应用程序。 Nch Software UTI Mutual fund Android 5.4.18之前版本存在安全漏洞,攻击者可利用该漏洞强行枚举由尝试无效凭据后返回的错误消息确定的用户名。
Severity
Patch Name
Nch Software UTI Mutual fund Android存在未明漏洞的补丁
Patch Description
Nch Software Uti Mutual Fund是澳大利亚Nch Software公司的一个投资共同基金的应用程序。 Nch Software UTI Mutual fund Android 5.4.18之前版本存在安全漏洞,攻击者可利用该漏洞强行枚举由尝试无效凭据后返回的错误消息确定的用户名。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://cvewalkthrough.com/cve-2020-35398-uti-mutual-fund-android-application-username-enumeration/

Reference
https://nvd.nist.gov/vuln/detail/CVE-2020-35398
Impacted products
Name
NCH Software UTI Mutual fund Android <=5.4.18
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-35398"
    }
  },
  "description": "Nch Software Uti Mutual Fund\u662f\u6fb3\u5927\u5229\u4e9aNch Software\u516c\u53f8\u7684\u4e00\u4e2a\u6295\u8d44\u5171\u540c\u57fa\u91d1\u7684\u5e94\u7528\u7a0b\u5e8f\u3002\n\nNch Software UTI Mutual fund Android 5.4.18\u4e4b\u524d\u7248\u672c\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5f3a\u884c\u679a\u4e3e\u7531\u5c1d\u8bd5\u65e0\u6548\u51ed\u636e\u540e\u8fd4\u56de\u7684\u9519\u8bef\u6d88\u606f\u786e\u5b9a\u7684\u7528\u6237\u540d\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://cvewalkthrough.com/cve-2020-35398-uti-mutual-fund-android-application-username-enumeration/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2022-00612",
  "openTime": "2022-01-05",
  "patchDescription": "Nch Software Uti Mutual Fund\u662f\u6fb3\u5927\u5229\u4e9aNch Software\u516c\u53f8\u7684\u4e00\u4e2a\u6295\u8d44\u5171\u540c\u57fa\u91d1\u7684\u5e94\u7528\u7a0b\u5e8f\u3002\r\n\r\nNch Software UTI Mutual fund Android 5.4.18\u4e4b\u524d\u7248\u672c\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5f3a\u884c\u679a\u4e3e\u7531\u5c1d\u8bd5\u65e0\u6548\u51ed\u636e\u540e\u8fd4\u56de\u7684\u9519\u8bef\u6d88\u606f\u786e\u5b9a\u7684\u7528\u6237\u540d\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": "Nch Software UTI Mutual fund Android\u5b58\u5728\u672a\u660e\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "NCH Software UTI Mutual fund Android \u003c=5.4.18"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2020-35398",
  "serverity": "\u4e2d",
  "submitTime": "2021-12-27",
  "title": "Nch Software UTI Mutual fund Android\u5b58\u5728\u672a\u660e\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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