CNVD-2021-102421
Vulnerability from cnvd - Published: 2021-12-24
VLAI
Title
Michael R Sweet HTMLDOC缓冲区溢出漏洞
Description
HTMLDOC是一个HTML文件格式转换编辑器,可读取HTML和Markdown源文件或网页,并生成带有可选目录的相应EPUB、HTML、PostScript或PDF文件。
HTMLDOC 1.9.12之前版本中的image_load_bmp存在缓冲区溢出漏洞。攻击者可通过特制BMP图像利用该漏洞导致拒绝服务。
Severity
中
Patch Name
Michael R Sweet HTMLDOC缓冲区溢出漏洞的补丁
Patch Description
HTMLDOC是一个HTML文件格式转换编辑器,可读取HTML和Markdown源文件或网页,并生成带有可选目录的相应EPUB、HTML、PostScript或PDF文件。
HTMLDOC 1.9.12之前版本中的image_load_bmp存在缓冲区溢出漏洞。攻击者可通过特制BMP图像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://github.com/michaelrsweet/htmldoc/commit/f12b9666e582a8e7b70f11b28e5ffc49ad625d43
Reference
https://nvd.nist.gov/vuln/detail/CVE-2021-40985
Impacted products
| Name | Michael R Sweet HTMLDOC <1.9.12 |
|---|
{
"cves": {
"cve": {
"cveNumber": "CVE-2021-40985",
"cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2021-40985"
}
},
"description": "HTMLDOC\u662f\u4e00\u4e2aHTML\u6587\u4ef6\u683c\u5f0f\u8f6c\u6362\u7f16\u8f91\u5668\uff0c\u53ef\u8bfb\u53d6HTML\u548cMarkdown\u6e90\u6587\u4ef6\u6216\u7f51\u9875\uff0c\u5e76\u751f\u6210\u5e26\u6709\u53ef\u9009\u76ee\u5f55\u7684\u76f8\u5e94EPUB\u3001HTML\u3001PostScript\u6216PDF\u6587\u4ef6\u3002\n\nHTMLDOC 1.9.12\u4e4b\u524d\u7248\u672c\u4e2d\u7684image_load_bmp\u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u901a\u8fc7\u7279\u5236BMP\u56fe\u50cf\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://github.com/michaelrsweet/htmldoc/commit/f12b9666e582a8e7b70f11b28e5ffc49ad625d43",
"isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
"number": "CNVD-2021-102421",
"openTime": "2021-12-24",
"patchDescription": "HTMLDOC\u662f\u4e00\u4e2aHTML\u6587\u4ef6\u683c\u5f0f\u8f6c\u6362\u7f16\u8f91\u5668\uff0c\u53ef\u8bfb\u53d6HTML\u548cMarkdown\u6e90\u6587\u4ef6\u6216\u7f51\u9875\uff0c\u5e76\u751f\u6210\u5e26\u6709\u53ef\u9009\u76ee\u5f55\u7684\u76f8\u5e94EPUB\u3001HTML\u3001PostScript\u6216PDF\u6587\u4ef6\u3002\r\n\r\nHTMLDOC 1.9.12\u4e4b\u524d\u7248\u672c\u4e2d\u7684image_load_bmp\u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u901a\u8fc7\u7279\u5236BMP\u56fe\u50cf\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": "Michael R Sweet HTMLDOC\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\u7684\u8865\u4e01",
"products": {
"product": "Michael R Sweet HTMLDOC \u003c1.9.12"
},
"referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2021-40985",
"serverity": "\u4e2d",
"submitTime": "2021-11-04",
"title": "Michael R Sweet HTMLDOC\u7f13\u51b2\u533a\u6ea2\u51fa\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
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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.
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.
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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.