GHSA-CH2M-MJ23-V6W4
Vulnerability from github – Published: 2026-08-26 18:31 – Updated: 2026-08-26 18:31
VLAI
Details
The Tutor LMS WordPress plugin before 4.0.6 does not validate values used to build a database query, and does not restrict which template file a request may load, allowing unauthenticated users to inject SQL and to read question and answer content belonging to courses that are not publicly available.
The injected text reaches the query as grammar rather than as data, and on the database engines tested it does not yield extraction of arbitrary data, so the confidentiality impact is the disclosed course content rather than the database at large.
Severity
5.3 (Medium)
{
"affected": [],
"aliases": [
"CVE-2026-19094"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-08-26T06:16:25Z",
"severity": "MODERATE"
},
"details": "The Tutor LMS WordPress plugin before 4.0.6 does not validate values used to build a database query, and does not restrict which template file a request may load, allowing unauthenticated users to inject SQL and to read question and answer content belonging to courses that are not publicly available.\n\nThe injected text reaches the query as grammar rather than as data, and on the database engines tested it does not yield extraction of arbitrary data, so the confidentiality impact is the disclosed course content rather than the database at large.",
"id": "GHSA-ch2m-mj23-v6w4",
"modified": "2026-08-26T18:31:52Z",
"published": "2026-08-26T18:31:52Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-19094"
},
{
"type": "WEB",
"url": "https://wpscan.com/vulnerability/8830ba91-c316-4823-90a8-0cf0e527468f"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N",
"type": "CVSS_V3"
}
]
}
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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.
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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