CVE-2025-0747 in EmbedAI
Summary
by MITRE • 01/30/2025
A Stored Cross-Site Scripting vulnerability has been found in EmbedAI. This vulnerability allows an authenticated attacker to inject a malicious JavaScript code into a message that will be executed when a user opens the chat.
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Analysis
by VulDB Data Team • 10/08/2025
The stored cross-site scripting vulnerability identified as CVE-2025-0747 represents a critical security flaw within the EmbedAI platform that undermines user data integrity and system security. This vulnerability specifically affects the chat functionality where users can exchange messages, creating an environment where malicious actors can persistently inject harmful scripts into the system. The flaw stems from inadequate input validation and output encoding mechanisms within the message processing pipeline, allowing attackers to bypass security controls that should normally prevent script execution in user-generated content. The vulnerability's classification aligns with CWE-79 which defines cross-site scripting as a condition where an application includes untrusted data in a web page without proper validation or escaping, making it susceptible to client-side code injection attacks.
The technical exploitation of this vulnerability requires an authenticated attacker who can leverage their valid credentials to submit malicious payloads through the chat interface. Once the malicious JavaScript code is stored within the application's database or message system, it becomes persistent and will execute whenever any user accesses the affected chat message. This stored nature differentiates the vulnerability from reflected XSS attacks where scripts must be injected into URLs or HTTP parameters. The attack vector operates through the normal user workflow of reading chat messages, making it particularly insidious as users are not actively attempting to execute malicious code but rather consuming legitimate content that has been compromised. The vulnerability essentially transforms the platform's communication features into a vector for persistent code execution, potentially enabling session hijacking, data theft, or further system compromise.
The operational impact of CVE-2025-0747 extends beyond immediate script execution to encompass broader security implications for the EmbedAI platform and its user base. Attackers could potentially harvest user session cookies, redirect victims to malicious domains, or perform actions on behalf of authenticated users through the compromised chat functionality. This vulnerability creates a persistent threat that remains active until the malicious code is removed from the system, potentially affecting thousands of users who interact with the chat feature. The attack surface is particularly concerning given that chat systems often contain sensitive information and are frequently accessed by users with varying privilege levels. From an attacker perspective, this vulnerability provides a stable foothold for further reconnaissance and lateral movement within the platform's ecosystem, as demonstrated by ATT&CK technique T1566 which covers social engineering through phishing and malicious content delivery.
Mitigation strategies for this vulnerability must address both immediate remediation and long-term prevention measures to secure the EmbedAI platform against similar threats. The primary fix involves implementing robust input sanitization and output encoding mechanisms that properly escape or filter user-supplied content before storing or rendering it in web pages. This includes applying context-aware encoding based on the output context where data is rendered, such as HTML, JavaScript, or URL contexts. Organizations should implement Content Security Policy (CSP) headers to limit script execution and prevent unauthorized code from running within the application context. Additionally, comprehensive logging and monitoring should be established to detect anomalous message content patterns that might indicate malicious injection attempts. Regular security testing including automated scanning and manual penetration testing should be conducted to identify similar vulnerabilities in other application components. The solution must also consider implementing rate limiting and content analysis mechanisms to prevent automated injection attacks while maintaining legitimate user functionality. This vulnerability highlights the importance of defense-in-depth strategies and proper security controls throughout the application lifecycle, aligning with industry best practices for preventing cross-site scripting attacks and maintaining user trust in web applications.