As the world becomes increasingly digital, cybersecurity—particularly cryptography—has become a defining concern of this century. Beyond designing robust algorithms, it is vital to evaluate the resilience of devices to adversaries who exploit various aspects of algorithm execution. Side-channel analysis targets physical leakages, such as power consumption and electromagnetic emissions, to extract secret information. State-of-the-art research identifies machine learning attacks using Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs) as the most effective. Language Models have achieved success across diverse domains, some unrelated to language. This thesis investigates their applicability to side-channel analysis and compares their performance with current state-of-the-art methods. Sane or Silly, a language model - inspired framework, is introduced and used to attack the ASCAD datasets. Results demonstrate that this approach can successfully retrieve the key in both ASCADf and ASCADv using only one trace, regardless of whether the secret masks are known during profiling. Desynchronization hindered but did not fully prevent successful attacks. These findings highlight the potential of language models as powerful tools for side-channel analysis.