CNVD-2022-44351
Vulnerability from cnvd - Published: 2022-07-23
VLAI
Title
Laravel存在命令执行漏洞（CNVD-2022-44351）
Description
Laravel是一套PHP Web开发框架（PHP Web Framework）。
Laravel存在命令执行漏洞，攻击者可利用漏洞进行远程代码执行（RCE）。
Severity
高
Patch Name
Laravel存在命令执行漏洞（CNVD-2022-44351）的补丁
Patch Description
Laravel是一套PHP Web开发框架（PHP Web Framework）。
Laravel存在命令执行漏洞，攻击者可利用漏洞进行远程代码执行（RCE）。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，详情请关注厂商主页： https://laravel.com/
Impacted products
| Name | Laravel Laravel 9.1.8 |
|---|
{
"cves": {
"cve": {
"cveNumber": "CVE-2022-31279"
}
},
"description": "Laravel\u662f\u4e00\u5957PHP Web\u5f00\u53d1\u6846\u67b6\uff08PHP Web Framework\uff09\u3002\n\nLaravel\u5b58\u5728\u547d\u4ee4\u6267\u884c\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u6f0f\u6d1e\u8fdb\u884c\u8fdc\u7a0b\u4ee3\u7801\u6267\u884c\uff08RCE\uff09\u3002",
"discovererName": "\u4e09\u516d\u96f6\u6570\u5b57\u5b89\u5168\u79d1\u6280\u96c6\u56e2\u6709\u9650\u516c\u53f8",
"formalWay": "\u76ee\u524d\u5382\u5546\u5df2\u53d1\u5e03\u5347\u7ea7\u8865\u4e01\u4ee5\u4fee\u590d\u6f0f\u6d1e\uff0c\u8be6\u60c5\u8bf7\u5173\u6ce8\u5382\u5546\u4e3b\u9875\uff1a\r\nhttps://laravel.com/",
"isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
"number": "CNVD-2022-44351",
"openTime": "2022-07-23",
"patchDescription": "Laravel\u662f\u4e00\u5957PHP Web\u5f00\u53d1\u6846\u67b6\uff08PHP Web Framework\uff09\u3002\r\n\r\nLaravel\u5b58\u5728\u547d\u4ee4\u6267\u884c\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u6f0f\u6d1e\u8fdb\u884c\u8fdc\u7a0b\u4ee3\u7801\u6267\u884c\uff08RCE\uff09\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": "Laravel\u5b58\u5728\u547d\u4ee4\u6267\u884c\u6f0f\u6d1e\uff08CNVD-2022-44351\uff09\u7684\u8865\u4e01",
"products": {
"product": "Laravel Laravel 9.1.8"
},
"serverity": "\u9ad8",
"submitTime": "2022-06-08",
"title": "Laravel\u5b58\u5728\u547d\u4ee4\u6267\u884c\u6f0f\u6d1e\uff08CNVD-2022-44351\uff09"
}
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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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