Focus on mudler vulnerabilities and metrics.
Last updated: 08 Mar 2025, 23:25 UTC
This page consolidates all known Common Vulnerabilities and Exposures (CVEs) associated with mudler. We track both calendar-based metrics (using fixed periods) and rolling metrics (using gliding windows) to give you a comprehensive view of security trends and risk evolution. Use these insights to assess risk and plan your patching strategy.
For a broader perspective on cybersecurity threats, explore the comprehensive list of CVEs by vendor and product. Stay updated on critical vulnerabilities affecting major software and hardware providers.
Total mudler CVEs: 4
Earliest CVE date: 20 Jun 2024, 00:15 UTC
Latest CVE date: 29 Oct 2024, 13:15 UTC
Latest CVE reference: CVE-2024-7010
30-day Count (Rolling): 0
365-day Count (Rolling): 4
Calendar-based Variation
Calendar-based Variation compares a fixed calendar period (e.g., this month versus the same month last year), while Rolling Growth Rate uses a continuous window (e.g., last 30 days versus the previous 30 days) to capture trends independent of calendar boundaries.
Month Variation (Calendar): 0%
Year Variation (Calendar): 0%
Month Growth Rate (30-day Rolling): 0.0%
Year Growth Rate (365-day Rolling): 0.0%
Average CVSS: 0.0
Max CVSS: 0
Critical CVEs (≥9): 0
| Range | Count |
|---|---|
| 0.0-3.9 | 4 |
| 4.0-6.9 | 0 |
| 7.0-8.9 | 0 |
| 9.0-10.0 | 0 |
These are the five CVEs with the highest CVSS scores for mudler, sorted by severity first and recency.
mudler/localai version 2.17.1 is vulnerable to a Timing Attack. This type of side-channel attack allows an attacker to compromise the cryptosystem by analyzing the time taken to execute cryptographic algorithms. Specifically, in the context of password handling, an attacker can determine valid login credentials based on the server's response time, potentially leading to unauthorized access.
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 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.
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 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.
A path traversal vulnerability exists in mudler/localai version 2.14.0, where an attacker can exploit the `model` parameter during the model deletion process to delete arbitrary files. Specifically, by crafting a request with a manipulated `model` parameter, an attacker can traverse the directory structure and target files outside of the intended directory, leading to the deletion of sensitive data. This vulnerability is due to insufficient input validation and sanitization of the `model` parameter.