Total
4 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2024-7010 | 1 Mudler | 1 Localai | 2024-11-14 | N/A | 5.9 MEDIUM |
| 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. | |||||
| CVE-2024-6868 | 1 Mudler | 1 Localai | 2024-11-13 | N/A | 9.8 CRITICAL |
| 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. | |||||
| CVE-2024-5182 | 1 Mudler | 1 Localai | 2024-08-27 | N/A | 9.1 CRITICAL |
| 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. | |||||
| CVE-2024-6095 | 1 Mudler | 1 Localai | 2024-07-09 | N/A | 5.8 MEDIUM |
| 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. | |||||