CNVD-2021-01998

Vulnerability from cnvd - Published: 2021-01-11
VLAI
Title
FFmpeg缓冲区溢出漏洞(CNVD-2021-01998)
Description
FFmpeg是FFmpeg(Ffmpeg)团队的一套可录制、转换以及流化音视频的完整解决方案。 FFmpeg 4.3.1版本存在缓冲区溢出漏洞,该漏洞源于计算何时执行memset零操作时出现了错误。目前没有详细的漏洞细节提供。
Severity
中
Patch Name
FFmpeg缓冲区溢出漏洞(CNVD-2021-01998)的补丁
Patch Description
FFmpeg是FFmpeg(Ffmpeg)团队的一套可录制、转换以及流化音视频的完整解决方案。 FFmpeg 4.3.1版本存在缓冲区溢出漏洞,该漏洞源于计算何时执行memset零操作时出现了错误。目前没有详细的漏洞细节提供。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

目前厂商已发布升级补丁以修复漏洞,补丁获取链接: https://github.com/FFmpeg/FFmpeg/commit/b0a8b40294ea212c1938348ff112ef1b9bf16bb3

Reference
https://nvd.nist.gov/vuln/detail/CVE-2020-35965
Impacted products
Name
FFmpeg Ffmpeg 4.3.1
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-35965",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2020-35965"
    }
  },
  "description": "FFmpeg\u662fFFmpeg\uff08Ffmpeg\uff09\u56e2\u961f\u7684\u4e00\u5957\u53ef\u5f55\u5236\u3001\u8f6c\u6362\u4ee5\u53ca\u6d41\u5316\u97f3\u89c6\u9891\u7684\u5b8c\u6574\u89e3\u51b3\u65b9\u6848\u3002\n\nFFmpeg 4.3.1\u7248\u672c\u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u8ba1\u7b97\u4f55\u65f6\u6267\u884cmemset\u96f6\u64cd\u4f5c\u65f6\u51fa\u73b0\u4e86\u9519\u8bef\u3002\u76ee\u524d\u6ca1\u6709\u8be6\u7ec6\u7684\u6f0f\u6d1e\u7ec6\u8282\u63d0\u4f9b\u3002",
  "formalWay": "\u76ee\u524d\u5382\u5546\u5df2\u53d1\u5e03\u5347\u7ea7\u8865\u4e01\u4ee5\u4fee\u590d\u6f0f\u6d1e\uff0c\u8865\u4e01\u83b7\u53d6\u94fe\u63a5\uff1a\r\nhttps://github.com/FFmpeg/FFmpeg/commit/b0a8b40294ea212c1938348ff112ef1b9bf16bb3",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2021-01998",
  "openTime": "2021-01-11",
  "patchDescription": "FFmpeg\u662fFFmpeg\uff08Ffmpeg\uff09\u56e2\u961f\u7684\u4e00\u5957\u53ef\u5f55\u5236\u3001\u8f6c\u6362\u4ee5\u53ca\u6d41\u5316\u97f3\u89c6\u9891\u7684\u5b8c\u6574\u89e3\u51b3\u65b9\u6848\u3002\r\n\r\nFFmpeg 4.3.1\u7248\u672c\u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u8ba1\u7b97\u4f55\u65f6\u6267\u884cmemset\u96f6\u64cd\u4f5c\u65f6\u51fa\u73b0\u4e86\u9519\u8bef\u3002\u76ee\u524d\u6ca1\u6709\u8be6\u7ec6\u7684\u6f0f\u6d1e\u7ec6\u8282\u63d0\u4f9b\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": "FFmpeg\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2021-01998\uff09\u7684\u8865\u4e01",
  "products": {
    "product": "FFmpeg Ffmpeg 4.3.1"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2020-35965",
  "serverity": "\u4e2d",
  "submitTime": "2021-01-08",
  "title": "FFmpeg\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2021-01998\uff09"
}



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…