Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:L/I:N/A:NSummary
CVE-2024-6095 is a medium-severity SSRF (CWE-918) vulnerability in Mudler Localai. Its CVSS base score is 5.8 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 17% 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 APIs and Models; in the Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
A vulnerability in the /models/apply endpoint of mudler/localai versions 2.15.0 permits Server-Side Request Forgery (SSRF) and partial Local File Inclusion (LFI). The endpoint accepts both http(s):// and file:// schemes, with the latter enabling limited file reads through error messages whose length constrains output. The flaw is tracked as CWE-918 and carries a CVSS 3.1 score of 5.8.
An attacker with network access to the LocalAI instance can supply crafted URLs to reach internal HTTP or HTTPS services and to read portions of local files. Successful exploitation yields unauthorized disclosure of internal resources without requiring authentication or user interaction.
The issue is resolved in LocalAI version 2.17. Public references point to commits that restrict scheme handling on the affected endpoint and to the corresponding Huntr disclosure that documents the original report and fix.
The EPSS score has reached a peak of 0.8921 with a current value of 0.8638, indicating sustained exploitation interest following disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-47247
Vulnerability Data
A vulnerability in the /models/apply endpoint of mudler/localai versions 2.15.0 allows for Server-Side Request Forgery (SSRF) and partial Local File Inclusion (LFI). The endpoint supports both http(s):// and file:// schemes, where the latter can lead to LFI. However, the output…
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is limited due to the length of the error message. This vulnerability can be exploited by an attacker with network access to the LocalAI instance, potentially allowing unauthorized access to internal HTTP(s) servers and partial reading of local files. The issue is fixed in version 2.17.
- CWE(s)
AI Security AnalysisAI
- AI Category
- APIs and Models
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- LocalAI is an open-source platform providing OpenAI-compatible REST APIs for local inference of AI models (LLMs, etc.), and the vulnerability is in the /models/apply endpoint used for model loading/application.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.6V1.5.3V5.3.2V10.4.7
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can restrict which destinations the server is allowed to contact on behalf of users.
Input validation directly stops untrusted URLs from being accepted and fetched without destination checks.
Boundary protection limits the network reach of server-initiated requests even if SSRF 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 development practices directly include input validation and destination allow-listing that prevent SSRF.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
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.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.