Automate, harden, and validate code generation using large language models LLM-generated code introduces vulnerabilities that conventional static analysis often misses. Large Language Models and Secure Code Generation addresses this problem directly, presenting methods to produce secure, production-quality code and integrate models into modern software security workflows. The book details techniques including Prompt Engineering, Prefix-Tuning, and Retrieval-Augmented Generation for improving code security. It introduces Mechanistic AI, advocating a shift from syntactic security to semantic-pragmatic security, and examines LLM-driven agents that orchestrate security audits. Coverage extends to multimodal and on-device LLM deployment trends, with code snippets, configuration examples, and task-specific recipes throughout each chapter. Readers will also find: Real-world case studies illustrating how leading teams leverage LLMs to accelerate secure feature development across production environments End-of-chapter questions and exercises designed to reinforce core concepts in secure code generation and LLM safety Methods for gathering high-quality code examples, setting training objectives, and fine-tuning models for security-critical applications Design patterns for LLM-driven agents capable of orchestrating automated security audits and adaptive threat response Coverage of emerging on-device LLM deployment architectures and their implications for software security in resource-constrained environments Designed for AI researchers, IT security professionals, and graduate students in computer science or software engineering, this book delivers the technical depth needed to build, evaluate, and deploy LLM-based systems that generate secure code. It connects architectural foundations with actionable security workflows for real-world implementation.