This thesis investigated the use of Large Language Models (LLMs) for vulnerability analysis of decompiled smart contracts. A controlled experiment was conducted in which an automated system was developed to decompile smart contracts using two decompilers: Dedaub and Heimdall-rs, and subsequently analyze them using three LLMs: OpenAI’s GPT-4 and GPT-3.5, as well as Meta’s CodeLlama. The study focuses on assessing the effectiveness of the LLMs at identifying a range of vulnerabilities. The evaluation method included the collection and comparative analysis of performance and evaluative metrics such as the precision, recall and F1-scores. Our results show the LLM-decompiler pairing of Dedaub and GPT-4 to exhibit impressive detection capabilities across a range of vulnerabilities while failing to detect some vulnerabilities at which CodeLlama excelled. We demonstrated the potential of LLMs to improve smart contract security and sets the stage for future research to further expand on this domain.