Vulnerability-Lookup

CNVD-2026-25134

Vulnerability from cnvd - Published: 2026-06-25

Title

Textpattern CMS文件上传漏洞

Description

Textpattern CMS是一个基于Php的内容管理系统。 Textpattern CMS 4.8.7版本存在文件上传漏洞。该漏洞源于文件上传功能未对用户上传文件进行严格校验或过滤不严，攻击者可利用该漏洞通过上传恶意PHP文件执行任意命令。

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://textpattern.com/security

Reference

https://nvd.nist.gov/vuln/detail/CVE-2021-47943

Impacted products

Name
Textpattern CMS Textpattern CMS 4.8.7

Show details on source website

JSON

To clipboard

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    "cve": {
      "cveNumber": "CVE-2021-47943",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2021-47943"
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  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2026-25134",
  "openTime": "2026-06-25",
  "products": {
    "product": "Textpattern CMS Textpattern CMS 4.8.7"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2021-47943",
  "serverity": "\u9ad8",
  "submitTime": "2026-05-18",
  "title": "Textpattern CMS\u6587\u4ef6\u4e0a\u4f20\u6f0f\u6d1e"
}

CVE-2021-47943 (GCVE-0-2021-47943)

Vulnerability from cvelistv5 – Published: 2026-05-10 12:43 – Updated: 2026-05-11 15:17

Title

TextPattern CMS 4.8.7 Remote Code Execution via File Upload

Summary

TextPattern CMS 4.8.7 contains a remote code execution vulnerability that allows authenticated attackers to execute arbitrary commands by uploading malicious PHP files through the file upload functionality. Attackers can upload a PHP shell via the Files section in the content area and execute commands by accessing the uploaded file at /textpattern/files/ with GET parameters passed to the system function.

Severity

8.7 (High)


                        
                          CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

8.8 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

SSVC

Exploitation: poc Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-05-11 15:17 UTC

CWE

CWE-434 - Unrestricted Upload of File with Dangerous Type

Assigner

VulnCheck mitre root

References

3 references

URL	Tags
https://www.exploit-db.com/exploits/49996	exploit
https://www.exploit-db.com/exploits/50415	exploit
https://www.vulncheck.com/advisories/textpattern-…	third-party-advisory

Impacted products

1 product

Vendor	Product	Version
Textpattern	TextPattern CMS	Affected: 4.8.7 cpe:2.3:a:textpattern:textpattern:4.8.7:-::::::

Credits

Mert Daş merterpreter@gmail.com

Show details on NVD website

JSON

To clipboard

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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.