CVE-2023-3368 (GCVE-0-2023-3368)
Vulnerability from cvelistv5 – Published: 2023-11-28 07:05 – Updated: 2025-06-03 13:58Title
Chamilo LMS Unauthenticated Command Injection
Summary
Command injection in `/main/webservices/additional_webservices.php` in Chamilo LMS <= v1.11.20 allows unauthenticated attackers to obtain remote code execution via improper neutralisation of special characters. This is a bypass of CVE-2023-34960.
Severity
9.8 (Critical)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-06-03 13:58 UTC
CWE
- CWE-78 - Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://support.chamilo.org/projects/chamilo-18/w… | vendor-advisory |
| https://starlabs.sg/advisories/23/23-3368/ | third-party-advisory |
| https://github.com/chamilo/chamilo-lms/commit/37b… | patch |
| https://https://github.com/chamilo/chamilo-lms/co… | patch |
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 42c6316e-9a43-440e-b243-9ed14fbd0938
Exploited: Yes
Timestamps
First Seen: 2023-11-28
Asserted: 2023-11-28
Scope
Notes: Chamilo LMS Unauthenticated Command Injection | Affected: Chamilo / Chamilo | CVSS: 9.8 (CRITICAL) | EPSS: 0.69671 | Used in malware: unknown | Not yet in CISA KEV: True
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: previdian
Details
| Feed | Previdian (previdian.com) |
|---|---|
| Title | Chamilo LMS Unauthenticated Command Injection |
| Cve Id | CVE-2023-3368 |
| Vendor | Chamilo |
| Ghsa Id | GHSA-X9XC-JG9P-2J7F |
| Product | Chamilo |
| Added Date | 2023-11-28T07:05:26.000Z |
| Cvss Score | 9.8 |
| Epss Score | 0.69671 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.99349 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2023-3368 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-02 09:11 CEST
| Updated: 2026-10-02 09:11 CEST
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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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