Book

Description

Large Language Models (LLMs) have rapidly become integral to modern software systems, but their widespread adoption has introduced new security challenges, including prompt injection, indirect instruction attacks, and sensitive data leakage. This book presents the Secure Prompt Engineering Framework (SPEF), a practical defense architecture designed to improve the resilience of LLM-based applications through layered prompt engineering and input validation strategies. Combining experimental evaluation with real-world attack scenarios, the framework analyzes six categories of prompt-based attacks and discusses both its effectiveness and methodological limitations. Rather than offering purely theoretical guidance, this work provides a structured approach for researchers, developers, and cybersecurity professionals interested in building more secure AI systems.