CVE-2026-105920 (GCVE-0-2026-105920)
Vulnerability from cvelistv5 – Published: 2026-10-06 13:30 – Updated: 2026-10-09 00:59
VLAI
EPSS
VEX
Title
Kusalkasilva Learning-Management-System Student Registration Endpoint student_signup.php sql injection
Summary
A vulnerability was determined in Kusalkasilva Learning-Management-System up to ffeb873f8803f1e9664384ff75000c7da45466d2. The impacted element is an unknown function of the file student_signup.php of the component Student Registration Endpoint. This manipulation causes sql injection. The attack may be initiated remotely. The exploit has been publicly disclosed and may be utilized. This product uses a rolling release model to deliver continuous updates. As a result, specific version information for affected or updated releases is not available. The project was informed of the problem early through an issue report but has not responded yet.
Severity
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-10-09 00:58 UTC
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/413894 | vdb-entry |
| https://vuldb.com/vuln/413894/cti | signaturepermissions-required |
| https://vuldb.com/cve/CVE-2026-105920 | third-party-advisory |
| https://vuldb.com/submit/992656 | third-party-advisory |
| https://github.com/KusalkaSilva/Learning-Manageme… | exploitissue-tracking |
| https://github.com/KusalkaSilva/Learning-Manageme… | product |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Kusalkasilva | Learning-Management-System |
Affected:
ffeb873f8803f1e9664384ff75000c7da45466d2
cpe:2.3:a:kusalkasilva:learning-management-system:*:*:*:*:*:*:*:* |
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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.
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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.
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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.
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