As Vector Database Management Systems (VDBMSs) become critical infrastructure for LLM applications, this research proposes a comprehensive testing framework to ensure their reliability and security.

• System Reliability Testing: Identifying potential failure points in vector databases that could compromise AI application functionality
• Security Vulnerability Assessment: Developing methodologies to protect against threats to vector embeddings and query mechanisms
• Performance Validation: Ensuring VDBMSs can handle the scale and complexity demands of modern AI systems
• Standardized Benchmarking: Creating industry standards for evaluating VDBMS security and reliability

This research is crucial for security professionals as it addresses the gap between rapid VDBMS adoption and necessary testing methodologies, helping organizations build more secure, dependable AI infrastructure.

Towards Reliable Vector Database Management Systems: A Software Testing Roadmap for 2030