CNVD-2017-33044

Vulnerability from cnvd - Published: 2017-11-07
VLAI
Title
Redis cluster.c clusterLoadConfig数据索引越界漏洞
Description
Redis是一个开源的使用ANSI C语言编写、支持网络、可基于内存亦可持久化的日志型、Key-Value数据库,并提供多种语言的API。 Redis cluster.c clusterLoadConfig存在数据索引越界漏洞,允许远程攻击者利用漏洞提交特殊的请求,可进行拒绝服务攻击。
Severity
高
Patch Name
Redis cluster.c clusterLoadConfig数据索引越界漏洞的补丁
Patch Description
Redis是一个开源的使用ANSI C语言编写、支持网络、可基于内存亦可持久化的日志型、Key-Value数据库,并提供多种语言的API。 Redis cluster.c clusterLoadConfig存在数据索引越界漏洞,允许远程攻击者利用漏洞提交特殊的请求,可进行拒绝服务攻击。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

用户可参考如下厂商提供的安全补丁以修复该漏洞: https://github.com/antirez/redis/issues/4278

Reference
https://github.com/antirez/redis/issues/4278
Impacted products
Name
Pivotal Software Redis 4.0.2
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2017-15047"
    }
  },
  "description": "Redis\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u4f7f\u7528ANSI C\u8bed\u8a00\u7f16\u5199\u3001\u652f\u6301\u7f51\u7edc\u3001\u53ef\u57fa\u4e8e\u5185\u5b58\u4ea6\u53ef\u6301\u4e45\u5316\u7684\u65e5\u5fd7\u578b\u3001Key-Value\u6570\u636e\u5e93\uff0c\u5e76\u63d0\u4f9b\u591a\u79cd\u8bed\u8a00\u7684API\u3002\r\n\r\nRedis cluster.c clusterLoadConfig\u5b58\u5728\u6570\u636e\u7d22\u5f15\u8d8a\u754c\u6f0f\u6d1e\uff0c\u5141\u8bb8\u8fdc\u7a0b\u653b\u51fb\u8005\u5229\u7528\u6f0f\u6d1e\u63d0\u4ea4\u7279\u6b8a\u7684\u8bf7\u6c42\uff0c\u53ef\u8fdb\u884c\u62d2\u7edd\u670d\u52a1\u653b\u51fb\u3002",
  "discovererName": "kirit1193",
  "formalWay": "\u7528\u6237\u53ef\u53c2\u8003\u5982\u4e0b\u5382\u5546\u63d0\u4f9b\u7684\u5b89\u5168\u8865\u4e01\u4ee5\u4fee\u590d\u8be5\u6f0f\u6d1e\uff1a\r\nhttps://github.com/antirez/redis/issues/4278",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2017-33044",
  "openTime": "2017-11-07",
  "patchDescription": "Redis\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u4f7f\u7528ANSI C\u8bed\u8a00\u7f16\u5199\u3001\u652f\u6301\u7f51\u7edc\u3001\u53ef\u57fa\u4e8e\u5185\u5b58\u4ea6\u53ef\u6301\u4e45\u5316\u7684\u65e5\u5fd7\u578b\u3001Key-Value\u6570\u636e\u5e93\uff0c\u5e76\u63d0\u4f9b\u591a\u79cd\u8bed\u8a00\u7684API\u3002\r\n\r\nRedis cluster.c clusterLoadConfig\u5b58\u5728\u6570\u636e\u7d22\u5f15\u8d8a\u754c\u6f0f\u6d1e\uff0c\u5141\u8bb8\u8fdc\u7a0b\u653b\u51fb\u8005\u5229\u7528\u6f0f\u6d1e\u63d0\u4ea4\u7279\u6b8a\u7684\u8bf7\u6c42\uff0c\u53ef\u8fdb\u884c\u62d2\u7edd\u670d\u52a1\u653b\u51fb\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": "Redis cluster.c clusterLoadConfig\u6570\u636e\u7d22\u5f15\u8d8a\u754c\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "Pivotal Software Redis 4.0.2"
  },
  "referenceLink": "https://github.com/antirez/redis/issues/4278",
  "serverity": "\u9ad8",
  "submitTime": "2017-10-09",
  "title": "Redis cluster.c clusterLoadConfig\u6570\u636e\u7d22\u5f15\u8d8a\u754c\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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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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