CNVD-2017-27943

Vulnerability from cnvd - Published: 2017-09-22
VLAI
Title
libsndfile psf_binheader_writef函数堆缓冲区溢出漏洞
Description
Libsndfile是一个用于通过标准库接口读取和写入包含采样声音的文件(如MS Windows WAV和Apple/SGI AIFF格式)的C库。 Libsndfile中的common.c中的psf_binheader_writef函数存在堆缓冲区溢出漏洞,远程攻击者可导致拒绝服务(应用程序崩溃)或可能造成其他影响。
Severity
Patch Name
libsndfile psf_binheader_writef函数堆缓冲区溢出漏洞的补丁
Patch Description
Libsndfile是一个用于通过标准库接口读取和写入包含采样声音的文件(如MS Windows WAV和Apple/SGI AIFF格式)的C库。 Libsndfile中的common.c中的psf_binheader_writef函数存在堆缓冲区溢出漏洞,远程攻击者可导致拒绝服务(应用程序崩溃)或可能造成其他影响。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

厂商已发布漏洞修复程序,请及时关注更新: https://github.com/erikd/libsndfile/issues/292

Reference
https://github.com/erikd/libsndfile/issues/292
Impacted products
Name
mega-nerd libsndfile <=1.0.28
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2017-12562"
    }
  },
  "description": "Libsndfile\u662f\u4e00\u4e2a\u7528\u4e8e\u901a\u8fc7\u6807\u51c6\u5e93\u63a5\u53e3\u8bfb\u53d6\u548c\u5199\u5165\u5305\u542b\u91c7\u6837\u58f0\u97f3\u7684\u6587\u4ef6\uff08\u5982MS Windows WAV\u548cApple/SGI AIFF\u683c\u5f0f\uff09\u7684C\u5e93\u3002\r\n\r\nLibsndfile\u4e2d\u7684common.c\u4e2d\u7684psf_binheader_writef\u51fd\u6570\u5b58\u5728\u5806\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8fdc\u7a0b\u653b\u51fb\u8005\u53ef\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\uff08\u5e94\u7528\u7a0b\u5e8f\u5d29\u6e83\uff09\u6216\u53ef\u80fd\u9020\u6210\u5176\u4ed6\u5f71\u54cd\u3002",
  "discovererName": "unknwon",
  "formalWay": "\u5382\u5546\u5df2\u53d1\u5e03\u6f0f\u6d1e\u4fee\u590d\u7a0b\u5e8f\uff0c\u8bf7\u53ca\u65f6\u5173\u6ce8\u66f4\u65b0\uff1a\r\nhttps://github.com/erikd/libsndfile/issues/292",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2017-27943",
  "openTime": "2017-09-22",
  "patchDescription": "Libsndfile\u662f\u4e00\u4e2a\u7528\u4e8e\u901a\u8fc7\u6807\u51c6\u5e93\u63a5\u53e3\u8bfb\u53d6\u548c\u5199\u5165\u5305\u542b\u91c7\u6837\u58f0\u97f3\u7684\u6587\u4ef6\uff08\u5982MS Windows WAV\u548cApple/SGI AIFF\u683c\u5f0f\uff09\u7684C\u5e93\u3002\r\n\r\nLibsndfile\u4e2d\u7684common.c\u4e2d\u7684psf_binheader_writef\u51fd\u6570\u5b58\u5728\u5806\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8fdc\u7a0b\u653b\u51fb\u8005\u53ef\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\uff08\u5e94\u7528\u7a0b\u5e8f\u5d29\u6e83\uff09\u6216\u53ef\u80fd\u9020\u6210\u5176\u4ed6\u5f71\u54cd\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": "libsndfile psf_binheader_writef\u51fd\u6570\u5806\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "mega-nerd libsndfile \u003c=1.0.28"
  },
  "referenceLink": "https://github.com/erikd/libsndfile/issues/292",
  "serverity": "\u9ad8",
  "submitTime": "2017-08-07",
  "title": "libsndfile psf_binheader_writef\u51fd\u6570\u5806\u7f13\u51b2\u533a\u6ea2\u51fa\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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