CVE-2024-6868
Mudler Localai 2.17.1
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2024-6868 is a critical-severity Link Following (CWE-59) vulnerability in Mudler Localai. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Path Interception (T1034); ranked in the top 27% of CVEs by exploit likelihood; 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 AC-3 (Access Enforcement) and AC-6 (Least Privilege) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-47862
Vulnerability Data
mudler/LocalAI version 2.17.1 allows for arbitrary file write due to improper handling of automatic archive extraction. When model configurations specify additional files as archives (e.g., .tar), these archives are automatically extracted after downloading. This behavior can be exploited to perform…
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a 'tarslip' attack, allowing files to be written to arbitrary locations on the server, bypassing checks that normally restrict files to the models directory. This vulnerability can lead to remote code execution (RCE) by overwriting backend assets used by the server.
- 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
- LocalAI is an open-source platform for local AI model inference, providing a drop-in OpenAI-compatible REST API for self-hosted LLMs and other models, fitting 'Other Platforms' as it serves as an inference server/platform.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V15.4.2
Mitigating Controls (NIST 800-53 r5) AI
Proper enforcement of access authorizations on the resolved target resource stops a link from reaching an unintended object.
Least-privilege limits the damage an attacker can cause after following an unintended link.
Validating file-name inputs can reject or canonicalize names that resolve to links before access occurs.
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 code to validate paths and avoid unsafe link following.
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 can detect link-following flaws before release.
Secure SDLC practices can mandate link-resolution checks and canonicalization before file access.
Application security requirements can explicitly require safe handling of symbolic links and path traversal.
Secure architecture principles include input validation and safe file-access design patterns.
Secure coding standards directly address canonicalization and symlink attacks during implementation.
Access-control rules can limit which files are reachable, reducing exposure to malicious links.