Large Language Models and Secure Code Generation

Large Language Models and Secure Code Generation

Automate, harden, and validate code generation using Large Language Models

Large Language Models (LLMs) are transforming software development by making AI-powered code generation faster and more accessible. However, LLM-generated code can introduce security vulnerabilities that conventional development practices and static analysis tools may fail to identify. Large Language Models and Secure Code Generation addresses these challenges and presents practical approaches for building secure, production-quality software with LLMs.

The book explores how LLM security, secure code generation, AI code security, and generative AI security can be incorporated into modern software development and cybersecurity workflows. It covers techniques such as Prompt Engineering, Prefix-Tuning, and Retrieval-Augmented Generation (RAG) to improve the reliability and security of AI-generated code.

The book also introduces Mechanistic AI and examines the transition from traditional syntactic security approaches toward semantic-pragmatic security. Readers will learn how LLM-powered agents can be designed to orchestrate automated security audits, identify threats, and support adaptive security responses.

Coverage includes:

• Real-world case studies showing how teams use LLMs to accelerate secure feature development in production environments
• Prompt engineering and other techniques for improving the security and quality of AI-generated code
• Prefix-Tuning and Retrieval-Augmented Generation (RAG) for security-focused LLM applications
• Methods for collecting high-quality code examples and establishing effective training objectives
• Fine-tuning Large Language Models for security-critical code generation applications
• Design patterns for LLM-driven and AI agents that automate security audits and threat-response workflows
• Mechanistic AI and semantic-pragmatic approaches to software security
• Emerging multimodal and on-device LLM architectures
• Security implications of deploying AI models on resource-constrained devices
• Code snippets, configuration examples, practical recipes, questions, and exercises

Designed for AI researchers, cybersecurity professionals, software engineers, IT security specialists, and graduate students in computer science and software engineering, this book provides the technical knowledge required to build, evaluate, and deploy LLM-based systems capable of generating more secure code.

By connecting the architectural foundations of Large Language Models with practical secure coding, AI security, and software security workflows, this book provides a valuable resource for anyone working at the intersection of generative AI, programming, cybersecurity, and secure software development.