<p>The emergence of Large Language Models (LLMs) has ushered in a new era in Artificial Intelligence, showcasing exceptional capabilities in understanding natural language and generating context-specific responses. Pre-trained on extensive datasets, LLMs have shown the potential to tackle complex tasks through appropriately formatted prompts. Recent initiatives, such as OpenROAD, have aimed to harness AI to minimize human involvement in hardware design. With the advent of LLMs, the hardware design and security communities are increasingly exploring these models to address challenges in their fields. Numerous studies have employed LLMs for generating hardware designs, creating test cases, and identifying bugs, highlighting their potential to revolutionize traditional workflows in these domains. Our work is an extension of the project “Netlist Whisperer: AI and NLP Fight Circuit Leakage!" which was presented at ASHES’23. In the previous Netlist Whisperer project, we sought to harness the power of LLMs to identify side-channel vulnerabilities in a given hardware design. We trained the widely used LLM Ada from OpenAI’s GPT-3 family to determine whether a specific net is leaky or non-leaky without simulating the entire design. We also utilized LLM Curie to generate side-channel attack-resistant Domain-Oriented-Masking implemented secure designs. In this extended work, we employed OpenAI’s currently available fine-tunable models Babbage, Davinci, and GPT3.5-Turbo. We curated a larger dataset by incorporating 28 circuits from ISCAS-85, ITC-99, and commonly used cipher S-box benchmarks. Upon initial training, we observed a significant drop in precision due to the substantial imbalance of leaky and non-leaky nets in the training data. As a remedy, we proposed an ensemble-based approach which yielded a significant improvement, enhancing the precision value to 44% and achieving an overall accuracy of 82%.</p>