CVE-2024-2361
Lollms Web Ui ≤ 9.5
Raw vector
CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-2361 is a critical-severity Path Traversal: '\..\filename' (CWE-29) vulnerability in Lollms Lollms Web Ui. Its CVSS base score is 9.6 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Junk Data (T1001.001); ranked at the 49th 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 Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) and AC-3 (Access Enforcement) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-27314
Vulnerability Data
A vulnerability in the parisneo/lollms-webui allows for arbitrary file upload and read due to insufficient sanitization of user-supplied input. Specifically, the issue resides in the `install_model()` function within `lollms_core/lollms/binding.py`, where the application fails to properly sanitize the `file://` protocol and…
more
other inputs, leading to arbitrary read and upload capabilities. Attackers can exploit this vulnerability by manipulating the `path` and `variant_name` parameters to achieve path traversal, allowing for the reading of arbitrary files and uploading files to arbitrary locations on the server. This vulnerability affects the latest version of parisneo/lollms-webui.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- parisneo/lollms-webui is a web UI platform for running and managing Large Language Models (LLMs), fitting under 'Other Platforms' as it provides a user interface and bindings for AI model deployment and interaction, not strictly a framework, library, or specific AI subdomain like NLP Transformers or Computer Vision.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Input validation directly neutralizes the '\..\filename' sequence before pathname resolution occurs.
Access enforcement denies requests that resolve outside the intended directory even when the traversal sequence is present.
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 path sanitization that block this traversal vector.
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.
Application security requirements can mandate input validation and path canonicalization to block traversal sequences.
A.8.27 mandates security-by-design principles (least privilege, default-deny, distrust-external-input, hardening, design review against attack patterns) that reach path-traversal neutralization in many but not all cases; business-process-specific controls, outsourced development, and residual design choices leave a bounded remainder unaddressed.
Secure coding standards directly require neutralizing path traversal sequences such as '\..\filename'.
Security testing can discover path-traversal flaws but does not itself prevent them in production code.