CNVD-2022-72104

Vulnerability from cnvd - Published: 2022-10-28
VLAI
Title
LibTIFF输入验证错误漏洞
Description
LibTIFF是一个读写TIFF(标签图像文件格式)文件的库。该库包含一些处理TIFF文件的命令行工具。 LibTIFF tiffcrop utility存在安全漏洞,该漏洞源于存在错误的输入验证缺陷,攻击者可利用该漏洞向tiffcrop提供精心制作的文件,进而导致越界读取并最终导致程序崩溃。
Severity
高
Patch Name
LibTIFF输入验证错误漏洞的补丁
Patch Description
LibTIFF是一个读写TIFF(标签图像文件格式)文件的库。该库包含一些处理TIFF文件的命令行工具。 LibTIFF tiffcrop utility存在安全漏洞,该漏洞源于存在错误的输入验证缺陷,攻击者可利用该漏洞向tiffcrop提供精心制作的文件,进而导致越界读取并最终导致程序崩溃。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://gitlab.com/libtiff/libtiff/-/issues/335

Reference
https://nvd.nist.gov/vuln/detail/CVE-2022-2868
Impacted products
Name
Libtiff LibTiff <4.4.0
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2022-2868"
    }
  },
  "description": "LibTIFF\u662f\u4e00\u4e2a\u8bfb\u5199TIFF\uff08\u6807\u7b7e\u56fe\u50cf\u6587\u4ef6\u683c\u5f0f\uff09\u6587\u4ef6\u7684\u5e93\u3002\u8be5\u5e93\u5305\u542b\u4e00\u4e9b\u5904\u7406TIFF\u6587\u4ef6\u7684\u547d\u4ee4\u884c\u5de5\u5177\u3002\n\nLibTIFF tiffcrop utility\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u5b58\u5728\u9519\u8bef\u7684\u8f93\u5165\u9a8c\u8bc1\u7f3a\u9677\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5411tiffcrop\u63d0\u4f9b\u7cbe\u5fc3\u5236\u4f5c\u7684\u6587\u4ef6\uff0c\u8fdb\u800c\u5bfc\u81f4\u8d8a\u754c\u8bfb\u53d6\u5e76\u6700\u7ec8\u5bfc\u81f4\u7a0b\u5e8f\u5d29\u6e83\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://gitlab.com/libtiff/libtiff/-/issues/335",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2022-72104",
  "openTime": "2022-10-28",
  "patchDescription": "LibTIFF\u662f\u4e00\u4e2a\u8bfb\u5199TIFF\uff08\u6807\u7b7e\u56fe\u50cf\u6587\u4ef6\u683c\u5f0f\uff09\u6587\u4ef6\u7684\u5e93\u3002\u8be5\u5e93\u5305\u542b\u4e00\u4e9b\u5904\u7406TIFF\u6587\u4ef6\u7684\u547d\u4ee4\u884c\u5de5\u5177\u3002\r\n\r\nLibTIFF tiffcrop utility\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u5b58\u5728\u9519\u8bef\u7684\u8f93\u5165\u9a8c\u8bc1\u7f3a\u9677\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5411tiffcrop\u63d0\u4f9b\u7cbe\u5fc3\u5236\u4f5c\u7684\u6587\u4ef6\uff0c\u8fdb\u800c\u5bfc\u81f4\u8d8a\u754c\u8bfb\u53d6\u5e76\u6700\u7ec8\u5bfc\u81f4\u7a0b\u5e8f\u5d29\u6e83\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": "LibTIFF\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "Libtiff LibTiff \u003c4.4.0"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2022-2868",
  "serverity": "\u9ad8",
  "submitTime": "2022-08-18",
  "title": "LibTIFF\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\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

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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