CNVD-2015-00644

Vulnerability from cnvd - Published: 2015-01-27
VLAI
Title
EMC M&R/ViPR SRM文件上传漏洞
Description
EMC M&R (Watch4Net)是一款IT绩效管理应用。EMC ViPR SRM是一个存储资源管理应用。 EMC M&R 6.5u1之前版本及ViPR SRM 3.6.1之前版本存在文件上传漏洞,允许远程认证用户上传文件,然后访问上传的执行文件来执行任意代码。
Severity
Patch Name
EMC M&R/ViPR SRM文件上传漏洞的补丁
Patch Description
EMC M&R (Watch4Net)是一款IT绩效管理应用。EMC ViPR SRM是一个存储资源管理应用。EMC M&R 6.5u1之前版本及ViPR SRM 3.6.1之前版本存在文件上传漏洞,允许远程认证用户上传文件,然后访问上传的执行文件来执行任意代码。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

用户可联系供应商获得补丁信息: http://www.emc.com/index.htm?fromGlobalSiteSelect

Reference
http://www.securityfocus.com/bid/72256
Impacted products
Name
['EMC M&R <6.5u1', 'EMC ViPR SRM <3.6.1']
Show details on source website

{
  "bids": {
    "bid": {
      "bidNumber": "72256"
    }
  },
  "cves": {
    "cve": {
      "cveNumber": "CVE-2015-0515"
    }
  },
  "description": "EMC M\u0026R (Watch4Net)\u662f\u4e00\u6b3eIT\u7ee9\u6548\u7ba1\u7406\u5e94\u7528\u3002EMC ViPR SRM\u662f\u4e00\u4e2a\u5b58\u50a8\u8d44\u6e90\u7ba1\u7406\u5e94\u7528\u3002 \r\n\r\nEMC M\u0026R 6.5u1\u4e4b\u524d\u7248\u672c\u53caViPR SRM 3.6.1\u4e4b\u524d\u7248\u672c\u5b58\u5728\u6587\u4ef6\u4e0a\u4f20\u6f0f\u6d1e\uff0c\u5141\u8bb8\u8fdc\u7a0b\u8ba4\u8bc1\u7528\u6237\u4e0a\u4f20\u6587\u4ef6\uff0c\u7136\u540e\u8bbf\u95ee\u4e0a\u4f20\u7684\u6267\u884c\u6587\u4ef6\u6765\u6267\u884c\u4efb\u610f\u4ee3\u7801\u3002",
  "discovererName": "EMC",
  "formalWay": "\u7528\u6237\u53ef\u8054\u7cfb\u4f9b\u5e94\u5546\u83b7\u5f97\u8865\u4e01\u4fe1\u606f\uff1a\r\nhttp://www.emc.com/index.htm?fromGlobalSiteSelect",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2015-00644",
  "openTime": "2015-01-27",
  "patchDescription": "EMC M\u0026R (Watch4Net)\u662f\u4e00\u6b3eIT\u7ee9\u6548\u7ba1\u7406\u5e94\u7528\u3002EMC ViPR SRM\u662f\u4e00\u4e2a\u5b58\u50a8\u8d44\u6e90\u7ba1\u7406\u5e94\u7528\u3002EMC M\u0026R 6.5u1\u4e4b\u524d\u7248\u672c\u53caViPR SRM 3.6.1\u4e4b\u524d\u7248\u672c\u5b58\u5728\u6587\u4ef6\u4e0a\u4f20\u6f0f\u6d1e\uff0c\u5141\u8bb8\u8fdc\u7a0b\u8ba4\u8bc1\u7528\u6237\u4e0a\u4f20\u6587\u4ef6\uff0c\u7136\u540e\u8bbf\u95ee\u4e0a\u4f20\u7684\u6267\u884c\u6587\u4ef6\u6765\u6267\u884c\u4efb\u610f\u4ee3\u7801\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": "EMC M\u0026R/ViPR SRM\u6587\u4ef6\u4e0a\u4f20\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": [
      "EMC M\u0026R \u003c6.5u1",
      "EMC ViPR SRM \u003c3.6.1"
    ]
  },
  "referenceLink": "http://www.securityfocus.com/bid/72256",
  "serverity": "\u4e2d",
  "submitTime": "2015-01-22",
  "title": "EMC M\u0026R/ViPR SRM\u6587\u4ef6\u4e0a\u4f20\u6f0f\u6d1e"
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

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.

Loading…

Detection rules are retrieved from Rulezet.

Loading…

Loading…

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.


Loading…