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Large Language Models (LLMs) have rapidly evolved from experimental systems capable of generating simple text to powerful general-purpose tools that solve exam-level problems, assist human experts, summarize complex documents, and write code, leading to their widespread deployment in production environments to improve productivity. Their rapid adoption has outpaced scientific understanding of the associated security and privacy risks, creating a growing gap between real-world use and trust in LLM-based applications. Unlike traditional machine learning models, LLMs are inherently general-purpose, operate at unprecedented scale, are often proprietary, and are accessed through interactive dialog interfaces. This makes their behavior difficult to predict and introduces novel attack surfaces and emerging phenomena such as hallucinations. Existing methods and analyzes for conventional machine learning are insufficient to cope with these aspects. So, this Dagstuhl Seminar "Security and Privacy of Large Language Models" (25461) aimed to initiate a systematic scientific discussion on the security and privacy of language models by bringing together researchers from AI, security, privacy, and natural language processing to address three central questions: how safe and secure are LLMs in adversarial environments; to what extent users’ privacy may be at risk when interacting with LLMs; and what other categories of societal impact may arise from the rapid advancement of LLM technologies. The seminar sought to systematize existing knowledge, identify high-risk applications and prevalent attack vectors, discuss defense strategies and deployment challenges, and outline directions for future research in LLM security and privacy.