CNVD-2016-10589

Vulnerability from cnvd - Published: 2016-11-04
VLAI
Title
Django硬编码密码安全绕过漏洞
Description
Django是Django软件基金会的一套基于Python语言的开源Web应用框架。该框架包括面向对象的映射器、视图系统、模板系统等。 Django存在硬编码密码安全绕过漏洞,攻击者可利用该漏洞绕过安全限制,执行未授权操作。
Severity
中
Patch Name
Django硬编码密码安全绕过漏洞的补丁
Patch Description
Django是Django软件基金会的一套基于Python语言的开源Web应用框架。该框架包括面向对象的映射器、视图系统、模板系统等。 Django存在硬编码密码安全绕过漏洞,攻击者可利用该漏洞绕过安全限制,执行未授权操作。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

目前厂商已经发布了升级补丁以修复此安全问题,补丁获取链接: https://www.djangoproject.com/weblog/2016/nov/01/security-releases/

Reference
http://www.securityfocus.com/bid/94069/
Impacted products
Name
['Django Django 1.9', 'Django Django \r\n1.8', 'Django Django 1.10']
Show details on source website

{
  "bids": {
    "bid": {
      "bidNumber": "94069"
    }
  },
  "cves": {
    "cve": {
      "cveNumber": "CVE-2016-9013"
    }
  },
  "description": "Django\u662fDjango\u8f6f\u4ef6\u57fa\u91d1\u4f1a\u7684\u4e00\u5957\u57fa\u4e8ePython\u8bed\u8a00\u7684\u5f00\u6e90Web\u5e94\u7528\u6846\u67b6\u3002\u8be5\u6846\u67b6\u5305\u62ec\u9762\u5411\u5bf9\u8c61\u7684\u6620\u5c04\u5668\u3001\u89c6\u56fe\u7cfb\u7edf\u3001\u6a21\u677f\u7cfb\u7edf\u7b49\u3002\r\n\r\nDjango\u5b58\u5728\u786c\u7f16\u7801\u5bc6\u7801\u5b89\u5168\u7ed5\u8fc7\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u7ed5\u8fc7\u5b89\u5168\u9650\u5236\uff0c\u6267\u884c\u672a\u6388\u6743\u64cd\u4f5c\u3002",
  "discovererName": "Marti Raudsepp.",
  "formalWay": "\u76ee\u524d\u5382\u5546\u5df2\u7ecf\u53d1\u5e03\u4e86\u5347\u7ea7\u8865\u4e01\u4ee5\u4fee\u590d\u6b64\u5b89\u5168\u95ee\u9898\uff0c\u8865\u4e01\u83b7\u53d6\u94fe\u63a5\uff1a \r\nhttps://www.djangoproject.com/weblog/2016/nov/01/security-releases/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2016-10589",
  "openTime": "2016-11-04",
  "patchDescription": "Django\u662fDjango\u8f6f\u4ef6\u57fa\u91d1\u4f1a\u7684\u4e00\u5957\u57fa\u4e8ePython\u8bed\u8a00\u7684\u5f00\u6e90Web\u5e94\u7528\u6846\u67b6\u3002\u8be5\u6846\u67b6\u5305\u62ec\u9762\u5411\u5bf9\u8c61\u7684\u6620\u5c04\u5668\u3001\u89c6\u56fe\u7cfb\u7edf\u3001\u6a21\u677f\u7cfb\u7edf\u7b49\u3002\r\n\r\nDjango\u5b58\u5728\u786c\u7f16\u7801\u5bc6\u7801\u5b89\u5168\u7ed5\u8fc7\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u7ed5\u8fc7\u5b89\u5168\u9650\u5236\uff0c\u6267\u884c\u672a\u6388\u6743\u64cd\u4f5c\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": "Django\u786c\u7f16\u7801\u5bc6\u7801\u5b89\u5168\u7ed5\u8fc7\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": [
      "Django Django 1.9",
      "Django Django \r\n1.8",
      "Django Django 1.10"
    ]
  },
  "referenceLink": "http://www.securityfocus.com/bid/94069/",
  "serverity": "\u4e2d",
  "submitTime": "2016-11-03",
  "title": "Django\u786c\u7f16\u7801\u5bc6\u7801\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

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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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