Large language models (LLMs) have demonstrated unprecedented natural language capabilities, achieving strong performance across a broad spectrum of tasks including code generation, reasoning, summarization, and question answering. The rapid scaling of these systems—from hundreds of millions to hundreds of billions of parameters—has accelerated their deployment in high-stakes, real-world environments, raising fundamental concerns about their safety, reliability, and alignment with human values. AI alignment, broadly defined as the problem of ensuring that AI systems behave in accordance with the intentions and values of their designers and end users, has emerged as one of the most technically complex and consequential challenges in contemporary machine learning research.

This paper provides a technically grounded survey of the principal alignment challenges in modern LLMs. We examine core problems including objective misalignment, hallucination and factual unreliability, adversarial jailbreaks and prompt injection vulnerabilities, social bias and harmful output generation, the opacity of transformer-based reasoning, scalability failures of current alignment techniques, and the theoretically critical but empirically underexplored problem of deceptive alignment and goal misgeneralization. We critically analyze existing alignment methods—including reinforcement learning from human feedback (RLHF) [1], Constitutional AI [2], red teaming, safety fine-tuning, and human oversight—identifying their substantive limitations and unsolved failure modes. We further discuss ethical and societal implications, enumerate open research problems, and propose directions for future investigation including mechanis-tic interpretability, scalable oversight, and alignment-specific benchmarking. Our analysis concludes that current alignment techniques represent necessary but insufficient safeguards, and that the field requires coordinated, technically rigorous research investment commensurate with the accelerating