CNVD-2016-09672

Vulnerability from cnvd - Published: 2016-10-21
VLAI
Title
Linux kernel本地权限提升漏洞(Dirty COW)
Description
Linux kernel是美国Linux基金会发布的操作系统Linux所使用的内核。 Linux Kernel本地权限提升漏洞,低权限的本地攻击者利用漏洞可获取其他只读内存映射的写权限,进而可导致权限提升执行进一步操作。
Severity
Patch Name
Linux kernel本地权限提升漏洞(Dirty COW)的补丁
Patch Description
Linux kernel是美国Linux基金会发布的操作系统Linux所使用的内核。 Linux Kernel本地权限提升漏洞,低权限的本地攻击者利用漏洞可获取其他只读内存映射的写权限,进而可导致权限提升执行进一步操作。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

厂商发布了升级程序修复该漏洞,请及时关注更新: https://git.kernel.org/cgit/linux/kernel/git/torvalds/linux.git/commit/?id=19be0eaffa3ac7d8eb6784ad9bdbc7d67ed8e619

Reference
https://github.com/dirtycow/dirtycow.github.io/wiki/VulnerabilityDetails
Impacted products
Name
Linux kernel >=2.6.22
Show details on source website

{
  "bids": {
    "bid": {
      "bidNumber": "93793"
    }
  },
  "cves": {
    "cve": {
      "cveNumber": "CVE-2016-5195"
    }
  },
  "description": "Linux kernel\u662f\u7f8e\u56fdLinux\u57fa\u91d1\u4f1a\u53d1\u5e03\u7684\u64cd\u4f5c\u7cfb\u7edfLinux\u6240\u4f7f\u7528\u7684\u5185\u6838\u3002 \r\n\r\nLinux Kernel\u672c\u5730\u6743\u9650\u63d0\u5347\u6f0f\u6d1e\uff0c\u4f4e\u6743\u9650\u7684\u672c\u5730\u653b\u51fb\u8005\u5229\u7528\u6f0f\u6d1e\u53ef\u83b7\u53d6\u5176\u4ed6\u53ea\u8bfb\u5185\u5b58\u6620\u5c04\u7684\u5199\u6743\u9650\uff0c\u8fdb\u800c\u53ef\u5bfc\u81f4\u6743\u9650\u63d0\u5347\u6267\u884c\u8fdb\u4e00\u6b65\u64cd\u4f5c\u3002",
  "discovererName": "Phil Oester",
  "formalWay": "\u5382\u5546\u53d1\u5e03\u4e86\u5347\u7ea7\u7a0b\u5e8f\u4fee\u590d\u8be5\u6f0f\u6d1e\uff0c\u8bf7\u53ca\u65f6\u5173\u6ce8\u66f4\u65b0\uff1a\r\nhttps://git.kernel.org/cgit/linux/kernel/git/torvalds/linux.git/commit/?id=19be0eaffa3ac7d8eb6784ad9bdbc7d67ed8e619",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2016-09672",
  "openTime": "2016-10-21",
  "patchDescription": "Linux kernel\u662f\u7f8e\u56fdLinux\u57fa\u91d1\u4f1a\u53d1\u5e03\u7684\u64cd\u4f5c\u7cfb\u7edfLinux\u6240\u4f7f\u7528\u7684\u5185\u6838\u3002 \r\n\r\nLinux Kernel\u672c\u5730\u6743\u9650\u63d0\u5347\u6f0f\u6d1e\uff0c\u4f4e\u6743\u9650\u7684\u672c\u5730\u653b\u51fb\u8005\u5229\u7528\u6f0f\u6d1e\u53ef\u83b7\u53d6\u5176\u4ed6\u53ea\u8bfb\u5185\u5b58\u6620\u5c04\u7684\u5199\u6743\u9650\uff0c\u8fdb\u800c\u53ef\u5bfc\u81f4\u6743\u9650\u63d0\u5347\u6267\u884c\u8fdb\u4e00\u6b65\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": "Linux kernel\u672c\u5730\u6743\u9650\u63d0\u5347\u6f0f\u6d1e\uff08Dirty COW\uff09\u7684\u8865\u4e01",
  "products": {
    "product": "Linux kernel \u003e=2.6.22"
  },
  "referenceLink": "https://github.com/dirtycow/dirtycow.github.io/wiki/VulnerabilityDetails",
  "serverity": "\u4e2d",
  "submitTime": "2016-10-21",
  "title": "Linux kernel\u672c\u5730\u6743\u9650\u63d0\u5347\u6f0f\u6d1e\uff08Dirty COW\uff09"
}



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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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