CNVD-2023-66428
Vulnerability from cnvd - Published: 2023-09-01
VLAI
Title
ScienceLogic SL1 SQL注入漏洞
Description
ScienceLogic SL1是ScienceLogic公司的一个应用程序。将您的房地产连接在一起,实现多向数据流和工作流程的自动化。
ScienceLogic SL1 11.1.2及之前版本存在SQL注入漏洞,攻击者可利用该漏洞执行非法SQL命令窃取数据库敏感数据。
Severity
中
Patch Name
ScienceLogic SL1 SQL注入漏洞的补丁
Patch Description
ScienceLogic SL1是ScienceLogic公司的一个应用程序。将您的房地产连接在一起,实现多向数据流和工作流程的自动化。
ScienceLogic SL1 11.1.2及之前版本存在SQL注入漏洞,攻击者可利用该漏洞执行非法SQL命令窃取数据库敏感数据。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://sciencelogic.com/platform/overview
Reference
https://www.securifera.com/advisories/cve-2022-48592/
Impacted products
| Name | ScienceLogic ScienceLogic SL1 <=11.1.2 |
|---|
{
"cves": {
"cve": {
"cveNumber": "CVE-2022-48592",
"cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2022-48592"
}
},
"description": "ScienceLogic SL1\u662fScienceLogic\u516c\u53f8\u7684\u4e00\u4e2a\u5e94\u7528\u7a0b\u5e8f\u3002\u5c06\u60a8\u7684\u623f\u5730\u4ea7\u8fde\u63a5\u5728\u4e00\u8d77\uff0c\u5b9e\u73b0\u591a\u5411\u6570\u636e\u6d41\u548c\u5de5\u4f5c\u6d41\u7a0b\u7684\u81ea\u52a8\u5316\u3002\n\nScienceLogic SL1 11.1.2\u53ca\u4e4b\u524d\u7248\u672c\u5b58\u5728SQL\u6ce8\u5165\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u6267\u884c\u975e\u6cd5SQL\u547d\u4ee4\u7a83\u53d6\u6570\u636e\u5e93\u654f\u611f\u6570\u636e\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://sciencelogic.com/platform/overview",
"isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
"number": "CNVD-2023-66428",
"openTime": "2023-09-01",
"patchDescription": "ScienceLogic SL1\u662fScienceLogic\u516c\u53f8\u7684\u4e00\u4e2a\u5e94\u7528\u7a0b\u5e8f\u3002\u5c06\u60a8\u7684\u623f\u5730\u4ea7\u8fde\u63a5\u5728\u4e00\u8d77\uff0c\u5b9e\u73b0\u591a\u5411\u6570\u636e\u6d41\u548c\u5de5\u4f5c\u6d41\u7a0b\u7684\u81ea\u52a8\u5316\u3002\r\n\r\nScienceLogic SL1 11.1.2\u53ca\u4e4b\u524d\u7248\u672c\u5b58\u5728SQL\u6ce8\u5165\u6f0f\u6d1e\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u6267\u884c\u975e\u6cd5SQL\u547d\u4ee4\u7a83\u53d6\u6570\u636e\u5e93\u654f\u611f\u6570\u636e\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": "ScienceLogic SL1 SQL\u6ce8\u5165\u6f0f\u6d1e\u7684\u8865\u4e01",
"products": {
"product": "ScienceLogic ScienceLogic SL1 \u003c=11.1.2"
},
"referenceLink": "https://www.securifera.com/advisories/cve-2022-48592/",
"serverity": "\u4e2d",
"submitTime": "2023-08-12",
"title": "ScienceLogic SL1 SQL\u6ce8\u5165\u6f0f\u6d1e"
}
Loading…
Loading…
Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.
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.
Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
Loading timeline…
Loading…
Loading…
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.
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.