CVE-2025-32389 (GCVE-0-2025-32389)
Vulnerability from cvelistv5 – Published: 2025-04-18 15:56 – Updated: 2025-04-18 16:24
VLAI
EPSS
VEX
Title
NamelessMC Vulnerable to SQL Injections in /user/messaging and /panel/users/reports Pages
Summary
NamelessMC is a free, easy to use & powerful website software for Minecraft servers. Prior to version 2.1.4, NamelessMC is vulnerable to SQL injection by providing an unexpected square bracket GET parameter syntax. Square bracket GET parameter syntax refers to the structure `?param[0]=a¶m[1]=b¶m[2]=c` utilized by PHP, which is parsed by PHP as `$_GET['param']` being of type array. This issue has been patched in version 2.1.4.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-04-18 16:11 UTC
CWE
- CWE-89 - Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/NamelessMC/Nameless/security/a… | x_refsource_CONFIRM |
| https://github.com/NamelessMC/Nameless/commit/02c… | x_refsource_MISC |
| https://github.com/NamelessMC/Nameless/releases/t… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| NamelessMC | Nameless |
Affected:
< 2.1.4
|
guessed |
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Sightings
| Author | Source | Type | Date | Other |
|---|
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
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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.
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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.