HeyJaredLLM Primer
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…
- Indexed episodes, last 90 days
- 4
- Latest publication
- Jul 7, 2026
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- Earliest in this view
- Jul 6, 2026
Latest episodes
Prompt Injection and Jailbreaks (opens the original)
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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 System
Data Security and Privacy (opens the original)
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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 Prime
Threat Modeling for LLM Systems (opens the original)
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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 code Amazon.com: LLM Primer VII AI Security: Defending LLM Systems Against Prompt Injection, Jailbreaks, and Adversarial Threats: 9798185644065:
Why AI Security Is Different (opens the original)
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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 Secur
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