CNVD-2019-05291
Vulnerability from cnvd - Published: 2019-02-26
VLAI
Title
Laravel Framework Illuminate组件反序列化漏洞
Description
Laravel Framework是Taylor Otwell软件开发者开发的一款基于PHP的Web应用程序开发框架。Illuminate是其中的一个组件。
Laravel Framework 5.7.x版本中的Illuminate组件存在反序列化漏洞，远程攻击者可利用该漏洞执行代码。
Severity
高
Formal description
厂商尚未提供漏洞修复方案，请关注厂商主页更新： https://laravel.com/
Reference
https://laworigin.github.io/2019/02/21/laravelv5-7%E5%8F%8D%E5%BA%8F%E5%88%97%E5%8C%96rce/
https://github.com/Laworigin/Laworigin.github.io/blob/master/2019/02/21/laravelv5-7%E5%8F%8D%E5%BA%8F%E5%88%97%E5%8C%96rce/index.html
https://nvd.nist.gov/vuln/detail/CVE-2019-9081
Impacted products
| Name | Taylor Otwell Laravel Framework 5.7.x |
|---|
{
"cves": {
"cve": {
"cveNumber": "CVE-2019-9081"
}
},
"description": "Laravel Framework\u662fTaylor Otwell\u8f6f\u4ef6\u5f00\u53d1\u8005\u5f00\u53d1\u7684\u4e00\u6b3e\u57fa\u4e8ePHP\u7684Web\u5e94\u7528\u7a0b\u5e8f\u5f00\u53d1\u6846\u67b6\u3002Illuminate\u662f\u5176\u4e2d\u7684\u4e00\u4e2a\u7ec4\u4ef6\u3002\n\nLaravel Framework 5.7.x\u7248\u672c\u4e2d\u7684Illuminate\u7ec4\u4ef6\u5b58\u5728\u53cd\u5e8f\u5217\u5316\u6f0f\u6d1e\uff0c\u8fdc\u7a0b\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u6267\u884c\u4ee3\u7801\u3002",
"discovererName": "WisdomTree",
"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\nhttps://laravel.com/",
"isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
"number": "CNVD-2019-05291",
"openTime": "2019-02-26",
"products": {
"product": "Taylor Otwell Laravel Framework 5.7.x"
},
"referenceLink": "https://laworigin.github.io/2019/02/21/laravelv5-7%E5%8F%8D%E5%BA%8F%E5%88%97%E5%8C%96rce/\r\nhttps://github.com/Laworigin/Laworigin.github.io/blob/master/2019/02/21/laravelv5-7%E5%8F%8D%E5%BA%8F%E5%88%97%E5%8C%96rce/index.html\r\nhttps://nvd.nist.gov/vuln/detail/CVE-2019-9081",
"serverity": "\u9ad8",
"submitTime": "2019-02-25",
"title": "Laravel Framework Illuminate\u7ec4\u4ef6\u53cd\u5e8f\u5217\u5316\u6f0f\u6d1e"
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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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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.
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