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 environmentsEnd-of-chapter questions and exercises designed to reinforce core concepts in secure code generation and LLM safetyMethods for gathering high-quality code examples, setting training objectives, and fine-tuning models for security-critical applicationsDesign patterns for LLM-driven agents capable of orchestrating automated security audits and adaptive threat responseCoverage of emerging on-device LLM deployment architectures and their implications for software security in resource-constrained environmentsDesigned 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.
