Vulnerability-Lookup

CNVD-2019-00816

Vulnerability from cnvd - Published: 2019-01-10

Title

libsndfile缓冲区越界读取漏洞（CNVD-2019-00816）

Description

libsndfile是软件开发者Erik de Castro Lopo所研发的一个通过标准接口读写AIFF、AU和WAV等格式的音频文件的C库。 libsndfile 1.0.28版本中的ulaw.c文件的‘i2ulaw_array’函数存在缓冲区越界读取漏洞，攻击者可利用该漏洞造成拒绝服务。

Severity

中

Formal description

厂商尚未提供漏洞修复方案，请关注厂商主页更新： http://www.mega-nerd.com/libsndfile/

Reference

https://nvd.nist.gov/vuln/detail/CVE-2018-19661

Impacted products

Name
Libsndfile libsndfile 1.0.28

Show details on source website

JSON

To clipboard

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2018-19661"
    }
  },
  "description": "libsndfile\u662f\u8f6f\u4ef6\u5f00\u53d1\u8005Erik de Castro Lopo\u6240\u7814\u53d1\u7684\u4e00\u4e2a\u901a\u8fc7\u6807\u51c6\u63a5\u53e3\u8bfb\u5199AIFF\u3001AU\u548cWAV\u7b49\u683c\u5f0f\u7684\u97f3\u9891\u6587\u4ef6\u7684C\u5e93\u3002\n\nlibsndfile 1.0.28\u7248\u672c\u4e2d\u7684ulaw.c\u6587\u4ef6\u7684\u2018i2ulaw_array\u2019\u51fd\u6570\u5b58\u5728\u7f13\u51b2\u533a\u8d8a\u754c\u8bfb\u53d6\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u9020\u6210\u62d2\u7edd\u670d\u52a1\u3002",
  "discovererName": "YourButterfly",
  "formalWay": "\u5382\u5546\u5c1a\u672a\u63d0\u4f9b\u6f0f\u6d1e\u4fee\u590d\u65b9\u6848\uff0c\u8bf7\u5173\u6ce8\u5382\u5546\u4e3b\u9875\u66f4\u65b0\uff1a\r\nhttp://www.mega-nerd.com/libsndfile/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2019-00816",
  "openTime": "2019-01-10",
  "products": {
    "product": "Libsndfile libsndfile 1.0.28"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2018-19661",
  "serverity": "\u4e2d",
  "submitTime": "2018-11-29",
  "title": "libsndfile\u7f13\u51b2\u533a\u8d8a\u754c\u8bfb\u53d6\u6f0f\u6d1e\uff08CNVD-2019-00816\uff09"
}

CVE-2018-19661 (GCVE-0-2018-19661)

Vulnerability from cvelistv5 – Published: 2018-11-29 07:00 – Updated: 2024-08-05 11:44

Summary

An issue was discovered in libsndfile 1.0.28. There is a buffer over-read in the function i2ulaw_array in ulaw.c that will lead to a denial of service.

Severity

No CVSS data available.

CWE

Assigner

mitre mitre root

References

4 references

URL	Tags
https://lists.debian.org/debian-lts-announce/2018…	mailing-listx_refsource_MLIST
https://github.com/erikd/libsndfile/issues/429	x_refsource_MISC
https://usn.ubuntu.com/4013-1/	vendor-advisoryx_refsource_UBUNTU
https://lists.debian.org/debian-lts-announce/2020…	mailing-listx_refsource_MLIST

Date Public

2018-11-29 00:00

Show details on NVD website

JSON

To clipboard

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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.