Raw vector
CVSS:3.0/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:HSummary
CVE-2024-2366 is a critical-severity Command Injection (CWE-77) vulnerability in Lollms Lollms Web Ui. Its CVSS base score is 9.0 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked at the 48th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-27319
Vulnerability Data
A remote code execution vulnerability exists in the parisneo/lollms-webui application, specifically within the reinstall_binding functionality in lollms_core/lollms/server/endpoints/lollms_binding_infos.py of the latest version. The vulnerability arises due to insufficient path sanitization, allowing an attacker to exploit path traversal to navigate to arbitrary…
more
directories. By manipulating the binding_path to point to a controlled directory and uploading a malicious __init__.py file, an attacker can execute arbitrary code on the server.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- The vulnerability is in parisneo/lollms-webui, a web UI platform for running and managing large language models (LOLLMS), which qualifies as an AI/ML platform. The issue occurs in binding management endpoints, fitting 'Other Platforms' as a catch-all for AI web interfaces and deployment tools.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover command-construction flaws before deployment.
Input validation directly stops construction of commands from untrusted data containing special elements.
Secure engineering principles include proper neutralization and safe command construction practices.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly require input validation and neutralization that prevent command injection.
Runtime monitoring of software and data can detect anomalous command execution resulting from injection.
Identifying recorded vulnerabilities enables remediation of command-injection flaws before exploitation.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Secure coding standards require proper escaping and parameterization of commands, directly eliminating CWE-77.
Security testing in development catches command-injection vulnerabilities before release.
Secure development life cycle mandates input validation and command construction practices that directly prevent command injection.
Application security requirements explicitly call for controls against injection flaws including command injection.
Secure architecture principles reduce the attack surface but do not prescribe the specific neutralization techniques needed.
Environment separation limits the blast radius of an exploited command injection but does not prevent the flaw itself.