Ensuring their security has become a critical challenge with the increasing complexity of modern software systems. Assembly-level security testing plays a crucial role in identifying vulnerabilities at the lowest layer of the code, where many sophisticated attacks can occur. This research investigates the application of artificial intelligence (AI) techniques in enhancing assembly-level security testing. We explore how AI, specifically machine learning models, can be leveraged to automate the detection of security flaws in assembly code by analyzing instruction patterns, control flow, and memory access behaviors. The paper presents a novel approach combining deep learning and static analysis tools to identify vulnerabilities such as buffer overflows, race conditions, and improper memory accesses. Experimental results show that AI-based techniques can significantly reduce the time and effort required for security analysis while improving the accuracy of vulnerability detection. Additionally, we discuss the challenges and limitations of applying AI in this context, particularly in terms of interpretability and integration with existing security tools. The findings highlight the potential of AI to revolutionize assembly-level security testing, paving the way for more efficient and robust vulnerability detection in low-level software development.