Security, Privacy, and Ethical Challenges in Large Language Models: A Review

With ongoing advances in artificial intelligence, Large Language Models (LLMs) have emerged as a leading approach to natural language processing (NLP). These models fundamentally demonstrate excellent capability in understanding linguistic patterns and producing human language. This review provides an extensive overview of LLMs, highlighting evaluation processes, architectural frameworks, training strategies, and real-world applications. It begins by summarising the implementation strategies of LLMs—from rule-based models and statistical approaches to neural networks and transformer-based models. The article then examines the central architecture of LLMs and explains the Transformer architecture and the self-attention mechanism. The review assesses the training processes across different stages. These involve pre-training, fine-tuning, instruction tuning, and reinforcement learning with human feedback. It illustrates how different mechanisms work together to enhance contextual understanding, text generation, question answering, summarisation, translation, and code generation. The applications of LLMs span multiple fields, including healthcare, education, cybersecurity, and software development. This study contributes to research, academia, and real-world practice. The review article primarily discusses reference datasets, performance evaluation metrics, and architectural design and training frameworks. It further examines major challenges, including hallucinations, bias, high computational cost, and energy consumption. Security, privacy, and ethical problems associated with the deployment of LLMs are rigorously assessed. A comprehensive assessment of existing LLMs is presented, highlighting their strengths and areas for improvement. Emphasising the next frontier, this study identifies key research challenges and outlines future directions. These include designing more advanced, scalable models and creating domain-specific LLMs and ethical AI systems. This comprehensive review helps researchers and practitioners obtain a clear, structured understanding of LLMs.