CVE-2026-32892 (GCVE-0-2026-32892)
Vulnerability from cvelistv5 – Published: 2026-04-10 17:56 – Updated: 2026-04-14 14:07
VLAI
EPSS
VEX
Title
OS Command Injection in Chamilo LMS 1.11.36
Summary
Chamilo LMS is a learning management system. Prior to 1.11.38 and 2.0.0-RC.3, Chamilo LMS contains an OS Command Injection vulnerability in the file move function. The move() function in fileManage.lib.php passes user-controlled path values directly into exec() shell commands without using escapeshellarg(). When a user moves a document via document.php, the move_to POST parameter — which only passes through Security::remove_XSS() (an HTML-only filter) — is concatenated directly into shell commands such as exec("mv $source $target"). By default, Chamilo allows all authenticated users to create courses (allow_users_to_create_courses = true). Any user who is a teacher in a course (including self-created courses) can move documents, making this vulnerability exploitable by any authenticated user. The attacker must first place a directory with shell metacharacters in its name on the filesystem (achievable via Course Backup Import), then move a document into that directory to trigger arbitrary command execution as the web server user (www-data). This vulnerability is fixed in 1.11.38 and 2.0.0-RC.3.
Severity
9.1 (Critical)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-14 14:07 UTC
CWE
- CWE-78 - Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/chamilo/chamilo-lms/security/a… | x_refsource_CONFIRM |
| https://github.com/chamilo/chamilo-lms/commit/359… | x_refsource_MISC |
| https://github.com/chamilo/chamilo-lms/commit/626… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| chamilo | chamilo-lms |
Affected:
< 1.11.38
Affected: >= 2.0.0-alpha.1, < 2.0.0-RC.3 |
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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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