CNVD-2015-07618

Vulnerability from cnvd - Published: 2015-11-17
VLAI
Title
Sensio Labs Twig displayBlock任意代码执行漏洞
Description
Sensio Labs Twig是法国Sensio Labs公司的一个PHP模板引擎,它允许开发人员自定义标签和过滤器,并创建DSL。 Sensio Labs Twig 1.20.0之前的版本存在任意代码执行漏洞,允许远程攻击者通过在模板中的变量 _self执行任意代码。
Severity
中
Patch Name
Sensio Labs Twig displayBlock任意代码执行漏洞的补丁
Patch Description
Sensio Labs Twig是法国Sensio Labs公司的一个PHP模板引擎,它允许开发人员自定义标签和过滤器,并创建DSL。Sensio Labs Twig 1.20.0之前的版本存在任意代码执行漏洞,允许远程攻击者通过在模板中的变量 _self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

用户可参考如下厂商提供的安全公告获取补丁以修复该漏洞: http://openwall.com/lists/oss-security/2015/08/21/3

Reference
http://openwall.com/lists/oss-security/2015/08/21/3
Impacted products
Name
Sensio Labs Twig <1.20.0
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2015-7809"
    }
  },
  "description": "Sensio Labs Twig\u662f\u6cd5\u56fdSensio Labs\u516c\u53f8\u7684\u4e00\u4e2aPHP\u6a21\u677f\u5f15\u64ce\uff0c\u5b83\u5141\u8bb8\u5f00\u53d1\u4eba\u5458\u81ea\u5b9a\u4e49\u6807\u7b7e\u548c\u8fc7\u6ee4\u5668\uff0c\u5e76\u521b\u5efaDSL\u3002\r\n\r\nSensio Labs Twig 1.20.0\u4e4b\u524d\u7684\u7248\u672c\u5b58\u5728\u4efb\u610f\u4ee3\u7801\u6267\u884c\u6f0f\u6d1e\uff0c\u5141\u8bb8\u8fdc\u7a0b\u653b\u51fb\u8005\u901a\u8fc7\u5728\u6a21\u677f\u4e2d\u7684\u53d8\u91cf _self\u6267\u884c\u4efb\u610f\u4ee3\u7801\u3002",
  "discovererName": "James Kettle",
  "formalWay": "\u7528\u6237\u53ef\u53c2\u8003\u5982\u4e0b\u5382\u5546\u63d0\u4f9b\u7684\u5b89\u5168\u516c\u544a\u83b7\u53d6\u8865\u4e01\u4ee5\u4fee\u590d\u8be5\u6f0f\u6d1e\uff1a\r\nhttp://openwall.com/lists/oss-security/2015/08/21/3",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2015-07618",
  "openTime": "2015-11-17",
  "patchDescription": "Sensio Labs Twig\u662f\u6cd5\u56fdSensio Labs\u516c\u53f8\u7684\u4e00\u4e2aPHP\u6a21\u677f\u5f15\u64ce\uff0c\u5b83\u5141\u8bb8\u5f00\u53d1\u4eba\u5458\u81ea\u5b9a\u4e49\u6807\u7b7e\u548c\u8fc7\u6ee4\u5668\uff0c\u5e76\u521b\u5efaDSL\u3002Sensio Labs Twig 1.20.0\u4e4b\u524d\u7684\u7248\u672c\u5b58\u5728\u4efb\u610f\u4ee3\u7801\u6267\u884c\u6f0f\u6d1e\uff0c\u5141\u8bb8\u8fdc\u7a0b\u653b\u51fb\u8005\u901a\u8fc7\u5728\u6a21\u677f\u4e2d\u7684\u53d8\u91cf _self\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": "Sensio Labs Twig displayBlock\u4efb\u610f\u4ee3\u7801\u6267\u884c\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "Sensio Labs Twig \u003c1.20.0"
  },
  "referenceLink": "http://openwall.com/lists/oss-security/2015/08/21/3",
  "serverity": "\u4e2d",
  "submitTime": "2015-11-13",
  "title": "Sensio Labs Twig displayBlock\u4efb\u610f\u4ee3\u7801\u6267\u884c\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…

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…