LLM Primer is a structured deep dive into Large Language Models, based on a seven-book series covering everything from foundational concepts and mathematical intuition to RAG, MCP, scalable AI systems, and AI security.
This podcast is built for engineers and serious professionals who want real understanding—not surface-level explanations.
Each season corresponds to one book. Each episode builds technical clarity step by step.
Understand the model. Build better systems.
This chapter examines prompt injection and jailbreak attacks, which exploit a language model's inherent inability to distinguish between authoritative developer instructions and untrusted user data. It covers the mechanics of direct and indirect injection, categorises common jailbreaking techniques, discusses the limitations of defensive prompt engineering, and outlines a layered mitigation strategy to better defend production systems.
Amazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065: SHIMODA, SHO: Books (https://www.amazon.com/dp/B0H7PPRMQQ)
7 Jul 2026
Data Security and Privacy
This chapter examines data security and privacy throughout the LLM lifecycle. It explores the inherent risks of training data, such as copyright issues, personal information (PII) contamination, and data poisoning. Additionally, it details how models can leak sensitive information through memorization and extraction attacks, and outlines operational defenses for securing systems, including input redaction pipelines, encryption, tenant isolation, and data retention policies.
Amazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065: SHIMODA, SHO: Books (https://www.amazon.com/dp/B0H7PPRMQQ)
6 Jul 2026
Threat Modeling for LLM Systems
This chapter adapts traditional threat modeling frameworks (such as STRIDE, PASTA, and attack trees) specifically for the unique vulnerabilities of LLM systems. It guides defenders through identifying AI-specific assets and adversaries, and provides a step-by-step procedure for building a living threat model that can be maintained alongside the system's codeAmazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065: SHIMODA, SHO: Books (https://www.amazon.com/dp/B0H7PPRMQQ)
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