GCVE Workshop - 22 September 2026 (14:00-18:00), Luxembourg Before The Vulnopticon Conference - Registration

CNVD-2024-40462

Vulnerability from cnvd - Published: 2024-10-10
VLAI
Title
freeimage缓冲区溢出漏洞(CNVD-2024-40462)
Description
FreeImage是FreeImage开源的一个跨平台的用于支持流行的图形图像格式的开源库。 freeimage存在缓冲区溢出漏洞,该漏洞源于配置文件的大小没有经过清理,攻击者可利用该漏洞导致拒绝服务。
Severity
Patch Name
freeimage缓冲区溢出漏洞(CNVD-2024-40462)的补丁
Patch Description
FreeImage是FreeImage开源的一个跨平台的用于支持流行的图形图像格式的开源库。 freeimage存在缓冲区溢出漏洞,该漏洞源于配置文件的大小没有经过清理,攻击者可利用该漏洞导致拒绝服务。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

厂商已发布了漏洞修复程序,请及时关注更新: https://freeimage.sourceforge.io/download.html

Reference
https://bugzilla.redhat.com/show_bug.cgi?id=2313704
Impacted products
Name
FreeImage FreeImage
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2024-9029",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2024-9029"
    }
  },
  "description": "FreeImage\u662fFreeImage\u5f00\u6e90\u7684\u4e00\u4e2a\u8de8\u5e73\u53f0\u7684\u7528\u4e8e\u652f\u6301\u6d41\u884c\u7684\u56fe\u5f62\u56fe\u50cf\u683c\u5f0f\u7684\u5f00\u6e90\u5e93\u3002\n\nfreeimage\u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u914d\u7f6e\u6587\u4ef6\u7684\u5927\u5c0f\u6ca1\u6709\u7ecf\u8fc7\u6e05\u7406\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\u3002",
  "formalWay": "\u5382\u5546\u5df2\u53d1\u5e03\u4e86\u6f0f\u6d1e\u4fee\u590d\u7a0b\u5e8f\uff0c\u8bf7\u53ca\u65f6\u5173\u6ce8\u66f4\u65b0\uff1a\r\nhttps://freeimage.sourceforge.io/download.html",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2024-40462",
  "openTime": "2024-10-10",
  "patchDescription": "FreeImage\u662fFreeImage\u5f00\u6e90\u7684\u4e00\u4e2a\u8de8\u5e73\u53f0\u7684\u7528\u4e8e\u652f\u6301\u6d41\u884c\u7684\u56fe\u5f62\u56fe\u50cf\u683c\u5f0f\u7684\u5f00\u6e90\u5e93\u3002\r\n\r\nfreeimage\u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u914d\u7f6e\u6587\u4ef6\u7684\u5927\u5c0f\u6ca1\u6709\u7ecf\u8fc7\u6e05\u7406\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\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": "freeimage\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2024-40462\uff09\u7684\u8865\u4e01",
  "products": {
    "product": "FreeImage FreeImage"
  },
  "referenceLink": "https://bugzilla.redhat.com/show_bug.cgi?id=2313704",
  "serverity": "\u9ad8",
  "submitTime": "2024-09-30",
  "title": "freeimage\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff08CNVD-2024-40462\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…

Detection rules are retrieved from Rulezet.

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…