Vulnerability-Lookup

CNVD-2022-56135

Vulnerability from cnvd - Published: 2022-08-10

Title

Magnolia CMS跨站脚本漏洞

Description

Magnolia CMS是瑞士Magnolia公司的一个应用程序，提供一个网站建设框架。 Magnolia CMS 6.2.19版本存在跨站脚本漏洞，该漏洞源于程序缺少对用户提供的数据和输出的数据校验过滤。攻击者可利用该漏洞在客户端执行JavaScript代码。

Severity

中

Formal description

厂商尚未提供漏洞修复方案，请关注厂商主页更新： https://www.magnolia-cms.com/

Reference

https://nvd.nist.gov/vuln/detail/CVE-2022-33098

Impacted products

Name
Magnolia Magnolia CMS 6.2.19

Show details on source website

JSON

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  "number": "CNVD-2022-56135",
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CVE-2022-33098 (GCVE-0-2022-33098)

Vulnerability from cvelistv5 – Published: 2022-07-07 18:58 – Updated: 2026-07-09 00:19

Summary

Magnolia CMS v6.2.19 was discovered to contain a cross-site scripting (XSS) vulnerability via the Edit Contact function. This vulnerability allows attackers to execute arbitrary web scripts or HTML via a crafted SVG document, with JavaScript, for a profile picture.

Severity

No CVSS data available.

CWE

Assigner

mitre mitre root

References

2 references

URL	Tags
http://packetstormsecurity.com/files/167705/Magno…
https://docs.magnolia-cms.com/product-docs/6.2/re…

Show details on NVD website

JSON

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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.

Detection rules are retrieved from Rulezet.

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.

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.