LLM Primer

LLM-PRIMER
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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.

  1. 7月7日

    Prompt Injection and Jailbreaks

    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

  2. 7月7日

    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

  3. 7月6日

    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

  4. 7月6日

    Why AI Security Is Different

    This chapter explains that AI security fundamentally and structurally differs from traditional software security. Instead of finding and patching clear bugs in readable source code, defenders must secure probabilistic models whose behaviors are driven by billions of uninterpretable weights and training data. Consequently, the security focus shifts from ensuring code correctness to managing and restricting an unbounded range of unpredictable inputs and outputs. Amazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065: SHIMODA, SHO: Books

  5. 2月19日

    2-7-7. Hallucinations and Reliability: Managing Confident Errors

    This episode covers Chapter 7, examining why Large Language Models confidently generate false information. We discuss the probabilistic nature of "hallucinations," the dangerous gap between fluency and correctness, and practical strategies like calibration and hybrid verification to align model confidence with reality. Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store

  6. 2月19日

    2-7-6. Retrieval-Augmented Generation Risks: Securing the Knowledge Pipeline

    This episode covers Chapter 6, focusing on the security implications of connecting models to external data (RAG). We discuss how this introduces new trust boundaries, the dangers of malicious document injection where attackers plant traps in your knowledge base, and the necessity of validating documents before they enter the model's context. Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store

  7. 2月18日

    2-7-5. Input Validation and Output Filtering: The Defense Pipeline

    This episode covers Chapter 5, detailing how to build disciplined pipelines around an AI model. We discuss strategies for sanitizing user inputs to catch attacks early, the importance of structured prompting to reduce ambiguity, and why output moderation is essential to catch policy violations that slip through earlier defenses. Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store

  8. 2月18日

    2-7-4. Prompt Injection and Jailbreaks: Defending the Interpreter

    This episode explores Chapter 4, detailing how attackers manipulate model behavior through crafted inputs like instruction overrides. We discuss why prompt injection is an inherent property of instruction-following systems rather than a standard bug. The episode covers jailbreaking techniques like role-playing and obfuscation, and why defense requires architectural layers rather than just better prompts. Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store

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關於

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.

資料

  • 創作者
    LLM-PRIMER
  • 活躍年份
    2026年
  • 單集
    23
  • 分級
    兒童適宜
  • 版權
    © LLM-PRIMER
  • 節目網站
    LLM Primer

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