CVE-2025-3578: Aidex Information Disclosure Vulnerability

CVE-2025-3578 Overview

CVE-2025-3578 is a critical prompt injection vulnerability affecting Aidex, an AI-powered application that integrates Large Language Model (LLM) chatbot functionality. The vulnerability exists in versions prior to 1.7 and allows authenticated users to manipulate the chatbot through crafted prompts, resulting in unauthorized access to sensitive credentials, user manipulation capabilities, and information disclosure.

Critical Impact

Authenticated attackers can exploit LLM prompt injection to list credentials of other users, create or modify user accounts, access production and development environment credentials, and exfiltrate sensitive system information including internal paths and software details.

Affected Products

• Aidex versions prior to 1.7

Discovery Timeline

• April 15, 2025 - CVE-2025-3578 published to NVD
• April 15, 2025 - Last updated in NVD database

Technical Details for CVE-2025-3578

Vulnerability Analysis

This vulnerability falls under CWE-1039 (Automated Recognition Mechanism with Inadequate Detection or Handling of Adversarial Input Perturbations), which specifically addresses weaknesses in AI/ML systems that fail to properly handle adversarial inputs. The flaw resides in the /api/<string-chat>/message endpoint, where the content parameter accepts user-supplied input that is processed by the underlying LLM without adequate sanitization or boundary enforcement.

The vulnerability enables an authenticated attacker to bypass intended access controls by crafting malicious prompts that manipulate the chatbot's behavior. Through this prompt injection technique, the attacker can instruct the LLM to perform unauthorized operations including credential enumeration, user management functions, and system information extraction that would otherwise be restricted.

Root Cause

The root cause of CVE-2025-3578 is insufficient input validation and lack of prompt boundary enforcement in the Aidex chatbot implementation. The application fails to properly isolate system-level instructions from user-supplied content, allowing attackers to inject commands that override or extend the intended behavior of the LLM. This absence of robust prompt injection defenses enables the chatbot to execute privileged operations based on malicious user input, effectively bypassing the application's access control mechanisms.

Attack Vector

The attack is conducted over the network and requires authentication to the Aidex application. An attacker submits specially crafted content to the /api/<string-chat>/message endpoint, manipulating the content parameter to inject malicious instructions into the LLM conversation. These injected prompts can instruct the chatbot to:

1. Enumerate and disclose credentials belonging to other users in the system
2. Create new user accounts or modify existing user configurations
3. Access and reveal credentials from both production and development environments
4. Extract sensitive system information including internal file paths and software version details

The attack exploits the trust relationship between the application and its integrated LLM component, where user input is insufficiently sanitized before being processed by the AI model.

Detection Methods for CVE-2025-3578

Indicators of Compromise

• Unusual or verbose responses from the chatbot containing system paths, credentials, or configuration details
• API logs showing repeated or suspicious requests to /api/<string-chat>/message with abnormally long or encoded content parameters
• User account creation or modification events that do not correlate with legitimate administrative activity
• Error messages or debug information appearing in chatbot responses that reveal internal application details

Detection Strategies

• Implement logging and monitoring on the /api/<string-chat>/message endpoint to track unusual request patterns
• Deploy Web Application Firewall (WAF) rules to detect common prompt injection patterns in request bodies
• Enable anomaly detection for chatbot responses that contain sensitive keywords such as credentials, passwords, API keys, or file paths
• Monitor for unauthorized user account changes through audit logging and correlation with authenticated sessions

Monitoring Recommendations

• Establish baseline metrics for normal chatbot interaction patterns and alert on deviations
• Configure SIEM rules to correlate authentication events with subsequent API activity targeting the chat endpoint
• Implement rate limiting on the message endpoint to slow down automated exploitation attempts
• Review chatbot conversation logs periodically for evidence of prompt injection attempts or unauthorized data disclosure

How to Mitigate CVE-2025-3578

Immediate Actions Required

• Upgrade Aidex to version 1.7 or later immediately to address this vulnerability
• Audit existing user accounts and credentials for signs of unauthorized access or modification
• Review API logs for the /api/<string-chat>/message endpoint to identify potential exploitation attempts
• Consider temporarily disabling the chatbot functionality until the patch can be applied if immediate upgrade is not feasible

Patch Information

Organizations running Aidex should upgrade to version 1.7 or later, which addresses this prompt injection vulnerability. Refer to the INCIBE Security Notice for official advisory details and patch guidance.

Workarounds

• Implement strict input validation and sanitization on the content parameter before processing by the LLM
• Deploy additional authentication and authorization checks to verify user permissions before executing privileged chatbot operations
• Use prompt engineering techniques to establish clear boundaries between system instructions and user input
• Restrict chatbot access to only necessary functions and implement the principle of least privilege for LLM-accessible operations

# Example: Restrict access to chat endpoint via nginx rate limiting
location /api/ {
    # Rate limit to mitigate automated exploitation
    limit_req zone=api_limit burst=10 nodelay;
    
    # Log all requests to chat endpoints for monitoring
    access_log /var/log/nginx/aidex_api_access.log detailed;
    
    proxy_pass http://aidex_backend;
}