CVE-2024-6971
Path Traversal in Lollms-Webui 9.8
Raw vector
CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2024-6971 is a medium-severity Path Traversal (CWE-22) vulnerability in Lollms Lollms-Webui. Its CVSS base score is 4.4 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 24th 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 Data-Related Vulnerabilities risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) 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-2024-2983
Vulnerability Data
A path traversal vulnerability exists in the parisneo/lollms-webui repository, specifically in the `lollms_file_system.py` file. The functions `add_rag_database`, `toggle_mount_rag_database`, and `vectorize_folder` do not implement security measures such as `sanitize_path_from_endpoint` or `sanitize_path`. This allows an attacker to perform vectorize operations on `.sqlite`…
more
files in any directory on the victim's computer, potentially installing multiple packages and causing a crash.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Data-Related Vulnerabilities
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- The vulnerability affects parisneo/lollms-webui, a web UI platform for running large language models (LLMs) with RAG features, classifying it as an AI platform rather than a specific framework or library.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V5.3.2
Mitigating Controls (NIST 800-53 r5) AI
Enforces the intended directory access authorizations that path traversal would otherwise bypass.
Input validation directly neutralizes special path elements before pathname construction occurs.
Least privilege reduces the impact of any unauthorized file access obtained via traversal.
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.
Patching/maintenance can remediate known path-traversal flaws in deployed software (partial prevention of exploitability) but does nothing to stop the coding defect from being introduced in the first place.
PR.AA-05 defines and reviews access policies but does not address code-level pathname neutralization, so neither direction prevents CWE-22.
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.
Security testing in development catches path traversal via static/dynamic analysis.
Secure SDLC mandates input validation and path sanitization that directly prevent path traversal.
Application security requirements include rules for safe file handling and canonicalization.
Secure architecture principles require least-privilege file access and directory isolation.
Secure coding standards explicitly forbid unsafe path construction and mandate safe APIs.
Information access restriction limits which files an application may read or write.