Vulnerability-Lookup

CNVD-2022-70582

Vulnerability from cnvd - Published: 2022-10-13

Title

FFmpeg缓冲区溢出漏洞（CNVD-2022-70582）

Description

FFmpeg是由个人开发者研发的软件，可以执行音频和视频多种格式的录影、转换、串流功能。 FFmpeg中存在缓冲区溢出漏洞，build_open_gop_key_points()中的大小计算会遍历循环中的所有条目，并将sc->ctts_data[i].count添加到sc->sample_offsets_count，这可能导致整数溢出。攻击者可以利用该漏洞导致缓冲区溢出。

Severity

高

Patch Name

FFmpeg缓冲区溢出漏洞（CNVD-2022-70582）的补丁

Patch Description

Formal description

目前厂商已经发布了升级补丁以修复这个问题，请到厂商的主页下载： https://github.com/FFmpeg/FFmpeg/commit/c953baa084607dd1d84c3bfcce3cf6a87c3e6e05

Reference

https://nvd.nist.gov/vuln/detail/CVE-2022-2566

Impacted products

Name
FFmpeg Ffmpeg 5.1

Show details on source website

JSON

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{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2022-2566",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2022-2566"
    }
  },
  "description": "FFmpeg\u662f\u7531\u4e2a\u4eba\u5f00\u53d1\u8005\u7814\u53d1\u7684\u8f6f\u4ef6\uff0c\u53ef\u4ee5\u6267\u884c\u97f3\u9891\u548c\u89c6\u9891\u591a\u79cd\u683c\u5f0f\u7684\u5f55\u5f71\u3001\u8f6c\u6362\u3001\u4e32\u6d41\u529f\u80fd\u3002\n\nFFmpeg\u4e2d\u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0cbuild_open_gop_key_points()\u4e2d\u7684\u5927\u5c0f\u8ba1\u7b97\u4f1a\u904d\u5386\u5faa\u73af\u4e2d\u7684\u6240\u6709\u6761\u76ee\uff0c\u5e76\u5c06sc-\u003ectts_data[i].count\u6dfb\u52a0\u5230sc-\u003esample_offsets_count\uff0c\u8fd9\u53ef\u80fd\u5bfc\u81f4\u6574\u6570\u6ea2\u51fa\u3002\u653b\u51fb\u8005\u53ef\u4ee5\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u7f13\u51b2\u533a\u6ea2\u51fa\u3002",
  "formalWay": "\u76ee\u524d\u5382\u5546\u5df2\u7ecf\u53d1\u5e03\u4e86\u5347\u7ea7\u8865\u4e01\u4ee5\u4fee\u590d\u8fd9\u4e2a\u95ee\u9898\uff0c\u8bf7\u5230\u5382\u5546\u7684\u4e3b\u9875\u4e0b\u8f7d\uff1a\r\nhttps://github.com/FFmpeg/FFmpeg/commit/c953baa084607dd1d84c3bfcce3cf6a87c3e6e05",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2022-70582",
  "openTime": "2022-10-13",
  "patchDescription": "FFmpeg\u662f\u7531\u4e2a\u4eba\u5f00\u53d1\u8005\u7814\u53d1\u7684\u8f6f\u4ef6\uff0c\u53ef\u4ee5\u6267\u884c\u97f3\u9891\u548c\u89c6\u9891\u591a\u79cd\u683c\u5f0f\u7684\u5f55\u5f71\u3001\u8f6c\u6362\u3001\u4e32\u6d41\u529f\u80fd\u3002\r\n\r\nFFmpeg\u4e2d\u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0cbuild_open_gop_key_points()\u4e2d\u7684\u5927\u5c0f\u8ba1\u7b97\u4f1a\u904d\u5386\u5faa\u73af\u4e2d\u7684\u6240\u6709\u6761\u76ee\uff0c\u5e76\u5c06sc-\u003ectts_data[i].count\u6dfb\u52a0\u5230sc-\u003esample_offsets_count\uff0c\u8fd9\u53ef\u80fd\u5bfc\u81f4\u6574\u6570\u6ea2\u51fa\u3002\u653b\u51fb\u8005\u53ef\u4ee5\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u7f13\u51b2\u533a\u6ea2\u51fa\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-2022-70582\uff09\u7684\u8865\u4e01",
  "products": {
    "product": "FFmpeg Ffmpeg 5.1"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2022-2566",
  "serverity": "\u9ad8",
  "submitTime": "2022-09-28",
  "title": "FFmpeg\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2022-70582\uff09"
}

CVE-2022-2566 (GCVE-0-2022-2566)

Vulnerability from cvelistv5 – Published: 2022-09-23 11:10 – Updated: 2025-04-21 13:49

Title

Heap-memory write in FFMPEG

Summary

A heap out-of-bounds memory write exists in FFMPEG since version 5.1. The size calculation in `build_open_gop_key_points()` goes through all entries in the loop and adds `sc->ctts_data[i].count` to `sc->sample_offsets_count`. This can lead to an integer overflow resulting in a small allocation with `av_calloc()`. An attacker can cause remote code execution via a malicious mp4 file. We recommend upgrading past commit c953baa084607dd1d84c3bfcce3cf6a87c3e6e05

Severity

9 (Critical)


                        
                          CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H

SSVC

Exploitation: none Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-04-21 13:36 UTC

CWE

CWE-122 - Heap-based Buffer Overflow

Assigner

Google Google root

References

1 reference

URL	Tags
https://github.com/FFmpeg/FFmpeg/commit/c953baa08…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
FFMPEG	FFMPEG	Affected: 5.1 , < unspecified (custom) Affected: ab77b878f1205225c6de1370fb0e998dbcc8bc69 , < unspecified (custom) Affected: unspecified , < c953baa084607dd1d84c3bfcce3cf6a87c3e6e05 (custom)

Date Public

2022-08-27 00:00

Show details on NVD website

JSON

To clipboard

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        "metrics": [
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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.

Detection rules are retrieved from Rulezet.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

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.