Vulnerability-Lookup

CVE-2025-58159 (GCVE-0-2025-58159)

Vulnerability from cvelistv5 – Published: 2025-08-29 22:15 – Updated: 2025-09-02 19:24

Title

WeGIA Authenticated Arbitrary File Upload Leading To Remote Code Execution (RCE)

Summary

WeGIA is a Web manager for charitable institutions. Prior to version 3.4.11, a remote code execution vulnerability was identified, caused by improper validation of uploaded files. The application allows an attacker to upload files with arbitrary filenames, including those with a .php extension. Because the uploaded file is written directly to disk without adequate sanitization or extension restrictions, a spreadsheet file followed by PHP code can be uploaded and executed on the server, leading to arbitrary code execution. This is due to insufficient mitigation of CVE-2025-22133. This issue has been patched in version 3.4.11.

Severity

10 (Critical)


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

SSVC

Exploitation: poc Automatable: yes Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-09-02 19:24 UTC

CWE

CWE-434 - Unrestricted Upload of File with Dangerous Type
CWE-94 - Improper Control of Generation of Code ('Code Injection')

Assigner

GitHub_M mitre root

References

1 reference

URL	Tags
https://github.com/LabRedesCefetRJ/WeGIA/security…	x_refsource_CONFIRM

Impacted products

1 product

Vendor	Product	Version	CPE status
LabRedesCefetRJ	WeGIA	Affected: < 3.4.11	guessed

Show details on NVD website

JSON

To clipboard

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FKIE_CVE-2025-58159

Vulnerability from fkie_nvd - Published: 2025-08-29 23:15 - Updated: 2026-06-17 09:43

Severity

9.9 (Critical) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H
8.8 (High) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Summary

References

	URL	Tags
	security-advisories@github.com	https://github.com/LabRedesCefetRJ/WeGIA/security/advisories/GHSA-wj2c-237g-cgqp	Exploit, Vendor Advisory

Impacted products

	Vendor	Product	Version
	wegia	wegia	*

JSON

To clipboard

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "WeGIA",
          "vendor": "LabRedesCefetRJ",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 3.4.11"
            }
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      "source": "security-advisories@github.com"
    }
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    }
  ],
  "id": "CVE-2025-58159",
  "lastModified": "2026-06-17T09:43:59.923",
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          "attackVector": "NETWORK",
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          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
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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.