  In the era of rapid advancements in artificial intelligence (AI), neural
network models have achieved notable breakthroughs. However, concerns arise
regarding their vulnerability to adversarial attacks. This study focuses on
enhancing Concolic Testing, a specialized technique for testing Python programs
implementing neural networks. The extended tool, PyCT, now accommodates a
broader range of neural network operations, including floating-point and
activation function computations. By systematically generating prediction path
constraints, the research facilitates the identification of potential
adversarial examples. Demonstrating effectiveness across various neural network
architectures, the study highlights the vulnerability of Python-based neural
network models to adversarial attacks. This research contributes to securing
AI-powered applications by emphasizing the need for robust testing
methodologies to detect and mitigate potential adversarial threats. It
underscores the importance of rigorous testing techniques in fortifying neural
network models for reliable applications in Python.
