CVE-2026-12529 (GCVE-0-2026-12529)
Vulnerability from cvelistv5 – Published: 2026-06-17 18:30 – Updated: 2026-06-18 15:30 X_Freeware
VLAI
EPSS
VEX
Title
SourceCodester CET Automated Grading System with AI Predictive Analytics Student Self-Registration Endpoint index.php access control
Summary
A security vulnerability has been detected in SourceCodester CET Automated Grading System with AI Predictive Analytics 1.0. Affected is an unknown function of the file /index.php of the component Student Self-Registration Endpoint. The manipulation leads to improper access controls. Remote exploitation of the attack is possible.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-18 15:30 UTC
Assigner
VulDB
CNA
References
5 references
| URL | Tags |
|---|---|
| https://vuldb.com/vuln/371976 | vdb-entry |
| https://vuldb.com/vuln/371976/cti | signaturepermissions-required |
| https://vuldb.com/cve/CVE-2026-12529 | third-party-advisory |
| https://vuldb.com/submit/837783 | third-party-advisory |
| https://www.sourcecodester.com/ | product |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| SourceCodester | CET Automated Grading System with AI Predictive Analytics |
Affected:
1.0
cpe:2.3:a:sourcecodester:cet_automated_grading_system_with_ai_predictive_analytics:*:*:*:*:*:*:*:* |
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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.
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- 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.