This work identifies 18 foundational challenges in ensuring the alignment and safety of large language models (LLMs). These challenges are categorized into three main areas: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. The authors pose over 200 concrete research questions to address these challenges, aiming to guide future research and development in the field. The scientific understanding of LLMs section focuses on issues such as in-context learning (ICL), the difficulty of estimating and understanding LLM capabilities, the effects of scale on capabilities, qualitative understanding of reasoning capabilities, and the risks posed by agentic LLMs. The development and deployment methods section addresses limitations in pretraining and fine-tuning processes, evaluation methods, interpretability, security, and governance. The sociotechnical challenges section explores the societal implications of LLMs, including value alignment, dual-use capabilities, trustworthiness, socioeconomic impacts, and governance. The work emphasizes the need for a comprehensive and holistic approach to ensure the safe and responsible development and deployment of LLMs.This work identifies 18 foundational challenges in ensuring the alignment and safety of large language models (LLMs). These challenges are categorized into three main areas: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. The authors pose over 200 concrete research questions to address these challenges, aiming to guide future research and development in the field. The scientific understanding of LLMs section focuses on issues such as in-context learning (ICL), the difficulty of estimating and understanding LLM capabilities, the effects of scale on capabilities, qualitative understanding of reasoning capabilities, and the risks posed by agentic LLMs. The development and deployment methods section addresses limitations in pretraining and fine-tuning processes, evaluation methods, interpretability, security, and governance. The sociotechnical challenges section explores the societal implications of LLMs, including value alignment, dual-use capabilities, trustworthiness, socioeconomic impacts, and governance. The work emphasizes the need for a comprehensive and holistic approach to ensure the safe and responsible development and deployment of LLMs.