CVE-2024-9900 in localai
Summary
by MITRE • 03/20/2025
mudler/localai version v2.21.1 contains a Cross-Site Scripting (XSS) vulnerability in its search functionality. The vulnerability arises due to improper sanitization of user input, allowing the injection and execution of arbitrary JavaScript code. This can lead to the execution of malicious scripts in the context of the victim's browser, potentially compromising user sessions, stealing session cookies, redirecting users to malicious websites, or manipulating the DOM.
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Analysis
by VulDB Data Team • 04/04/2025
The CVE-2024-9900 vulnerability affects mudler/localai version v2.21.1 and represents a critical cross-site scripting flaw within the application's search functionality. This vulnerability stems from inadequate input validation and sanitization mechanisms that fail to properly process user-supplied data before rendering it in the web interface. The flaw exists in the way the application handles search queries and other user inputs that are subsequently displayed without proper encoding or filtering, creating an avenue for malicious actors to inject harmful JavaScript code into the application's response.
The technical implementation of this vulnerability allows attackers to craft malicious payloads that exploit the lack of input sanitization in the search component. When users submit search terms containing malicious script code, the application fails to sanitize these inputs before displaying them in the user interface. This oversight creates a persistent XSS vector where the injected JavaScript executes within the context of the victim's browser session. The vulnerability operates under CWE-79 which specifically addresses Cross-Site Scripting flaws in web applications. The attack surface is particularly concerning as it leverages the application's legitimate search functionality, making the exploit more likely to succeed as users naturally engage with search features.
The operational impact of this vulnerability extends beyond simple script execution, creating multiple potential attack vectors for threat actors. Successful exploitation can lead to session hijacking through cookie theft, redirecting users to malicious domains, or modifying the application's DOM to display fraudulent content. Attackers could potentially establish persistent access to user accounts, harvest sensitive information, or use the compromised browser session to perform actions on behalf of authenticated users. The vulnerability's presence in a machine learning or AI platform like localai increases the risk as these applications often handle sensitive data and may be used in enterprise environments where session compromise could lead to broader system infiltration. This aligns with ATT&CK technique T1531 which covers "Use of Web Services" for maintaining access and persistence through compromised web applications.
Mitigation strategies for CVE-2024-9900 should focus on implementing robust input validation and output encoding mechanisms throughout the application's search functionality. The most effective immediate solution involves sanitizing all user inputs before rendering them in the web interface, utilizing proper HTML encoding techniques, and implementing Content Security Policy headers to limit script execution. Organizations should also consider implementing a Web Application Firewall to detect and block malicious payloads targeting the search functionality. The recommended approach includes updating to the patched version of localai v2.21.2 or later, which addresses the input sanitization issues. Additionally, security teams should conduct thorough code reviews of all input handling components and implement automated testing for XSS vulnerabilities in the development lifecycle to prevent similar issues from emerging in future releases.