CNVD-2018-26512
Vulnerability from cnvd - Published: 2018-12-26
VLAI
Title
Foxit Quick PDF Library越界内存访问漏洞(CNVD-2018-26512)
Description
Foxit Quick PDF Library是中国福昕(Foxit)软件公司的一款PDF SDK(软件开发工具包)。该产品主要用于创建、渲染和编辑PDF文件。
Foxit Quick PDF Library存在越界内存访问漏洞。攻击者可通过包含无效xref条目的畸形或恶意PDF利用该漏洞导致访问冲突。
Severity
中
Patch Name
Foxit Quick PDF Library越界内存访问漏洞(CNVD-2018-26512)的补丁
Patch Description
Foxit Quick PDF Library是中国福昕(Foxit)软件公司的一款PDF SDK(软件开发工具包)。该产品主要用于创建、渲染和编辑PDF文件。
Foxit Quick PDF Library存在越界内存访问漏洞。攻击者可通过包含无效xref条目的畸形或恶意PDF利用该漏洞导致访问冲突。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
厂商已发布漏洞修复程序,请及时关注更新: https://www.foxitsoftware.com/support/security-bulletins.php
Reference
https://nvd.nist.gov/vuln/detail/CVE-2018-20249
Impacted products
| Name | Foxit Quick PDF Library <16.12 |
|---|
{
"cves": {
"cve": {
"cveNumber": "CVE-2018-20249"
}
},
"description": "Foxit Quick PDF Library\u662f\u4e2d\u56fd\u798f\u6615\uff08Foxit\uff09\u8f6f\u4ef6\u516c\u53f8\u7684\u4e00\u6b3ePDF SDK\uff08\u8f6f\u4ef6\u5f00\u53d1\u5de5\u5177\u5305\uff09\u3002\u8be5\u4ea7\u54c1\u4e3b\u8981\u7528\u4e8e\u521b\u5efa\u3001\u6e32\u67d3\u548c\u7f16\u8f91PDF\u6587\u4ef6\u3002\n\nFoxit Quick PDF Library\u5b58\u5728\u8d8a\u754c\u5185\u5b58\u8bbf\u95ee\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u901a\u8fc7\u5305\u542b\u65e0\u6548xref\u6761\u76ee\u7684\u7578\u5f62\u6216\u6076\u610fPDF\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u8bbf\u95ee\u51b2\u7a81\u3002",
"discovererName": "unknwon",
"formalWay": "\u5382\u5546\u5df2\u53d1\u5e03\u6f0f\u6d1e\u4fee\u590d\u7a0b\u5e8f\uff0c\u8bf7\u53ca\u65f6\u5173\u6ce8\u66f4\u65b0\uff1a\r\nhttps://www.foxitsoftware.com/support/security-bulletins.php",
"isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
"number": "CNVD-2018-26512",
"openTime": "2018-12-26",
"patchDescription": "Foxit Quick PDF Library\u662f\u4e2d\u56fd\u798f\u6615\uff08Foxit\uff09\u8f6f\u4ef6\u516c\u53f8\u7684\u4e00\u6b3ePDF SDK\uff08\u8f6f\u4ef6\u5f00\u53d1\u5de5\u5177\u5305\uff09\u3002\u8be5\u4ea7\u54c1\u4e3b\u8981\u7528\u4e8e\u521b\u5efa\u3001\u6e32\u67d3\u548c\u7f16\u8f91PDF\u6587\u4ef6\u3002\r\n\r\nFoxit Quick PDF Library\u5b58\u5728\u8d8a\u754c\u5185\u5b58\u8bbf\u95ee\u6f0f\u6d1e\u3002\u653b\u51fb\u8005\u53ef\u901a\u8fc7\u5305\u542b\u65e0\u6548xref\u6761\u76ee\u7684\u7578\u5f62\u6216\u6076\u610fPDF\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u8bbf\u95ee\u51b2\u7a81\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": "Foxit Quick PDF Library\u8d8a\u754c\u5185\u5b58\u8bbf\u95ee\u6f0f\u6d1e\uff08CNVD-2018-26512\uff09\u7684\u8865\u4e01",
"products": {
"product": "Foxit Quick PDF Library \u003c16.12"
},
"referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2018-20249",
"serverity": "\u4e2d",
"submitTime": "2018-12-25",
"title": "Foxit Quick PDF Library\u8d8a\u754c\u5185\u5b58\u8bbf\u95ee\u6f0f\u6d1e\uff08CNVD-2018-26512\uff09"
}
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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 |
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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.