CVE-2023-48741 in AI ChatBot Plugin
Summary
by MITRE • 12/19/2023
Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection') vulnerability in QuantumCloud AI ChatBot.This issue affects AI ChatBot: from n/a through 4.7.8.
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Analysis
by VulDB Data Team • 05/12/2025
The vulnerability identified as CVE-2023-48741 represents a critical SQL injection weakness within the QuantumCloud AI ChatBot application, specifically impacting versions ranging from n/a through 4.7.8. This flaw resides in the improper neutralization of special elements within SQL commands, creating a pathway for malicious actors to manipulate database queries through user input. The vulnerability falls under the well-established CWE-89 category, which classifies SQL injection as a fundamental weakness in application security where untrusted data is directly incorporated into SQL command structures without adequate sanitization or parameterization. The affected QuantumCloud AI ChatBot system processes user interactions and queries without sufficient input validation, allowing attackers to inject malicious SQL code that can be executed by the underlying database engine.
The technical exploitation of this vulnerability occurs when user-supplied data containing special SQL characters or commands is not properly escaped or parameterized before being incorporated into database queries. Attackers can leverage this weakness to perform unauthorized database operations including data extraction, modification, or deletion, potentially leading to complete database compromise. The vulnerability's impact extends beyond simple data theft as it can enable attackers to escalate privileges, access sensitive user information, and potentially gain deeper system access. The lack of proper input sanitization means that any user interaction with the chatbot could serve as an attack vector, particularly when the application processes natural language queries that might inadvertently contain SQL injection payloads. This weakness is particularly dangerous in AI chatbot environments where user inputs are often processed dynamically without strict validation protocols.
The operational consequences of CVE-2023-48741 are severe and multifaceted, potentially exposing organizations to significant data breaches and compliance violations. The vulnerability allows for unauthorized access to backend databases that may contain sensitive user information, conversation histories, and potentially proprietary data processed through the AI chatbot interface. Organizations relying on QuantumCloud AI ChatBot for customer service or data processing may face regulatory penalties under data protection frameworks such as gdpr, hipaa, or other applicable compliance standards. The attack surface is broadened by the nature of chatbot applications which typically process high volumes of user inputs, making automated exploitation more feasible. Additionally, successful exploitation can lead to service disruption, data corruption, and potential lateral movement within network environments where the affected system resides, as database compromise often provides attackers with additional attack vectors.
Mitigation strategies for CVE-2023-48741 must focus on implementing robust input validation and parameterized query execution throughout the QuantumCloud AI ChatBot application. Organizations should immediately upgrade to version 4.7.9 or later, which contains the necessary patches addressing this SQL injection vulnerability. The implementation of proper input sanitization techniques, including the use of prepared statements and parameterized queries, should be enforced across all database interaction points. Additionally, comprehensive code reviews should be conducted to identify and remediate any other potential SQL injection vulnerabilities within the application codebase. Network segmentation and database access controls should be strengthened to limit potential damage from successful exploitation attempts. Organizations should also implement web application firewalls and intrusion detection systems to monitor for suspicious database activity patterns. The remediation process must include thorough testing of patched code to ensure that the vulnerability has been properly addressed without introducing new functionality issues. Regular security assessments and penetration testing should be conducted to validate the effectiveness of implemented controls and to identify any emerging threats that may exploit similar weaknesses in the system architecture.