LLM Primer

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 [https://www.amazon.com/dp/B0GP5T98GJ]

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 [https://www.amazon.com/dp/B0GP5T98GJ]

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 [https://www.amazon.com/dp/B0GP5T98GJ]

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 [https://www.amazon.com/dp/B0GP5T98GJ]

2-7-3. Data Security and Privacy: The AI Lifecycle

This episode breaks down Chapter 3, tracking data risks from training to deployment. We discuss how models can memorize sensitive training data, the subtle dangers of leakage through generated outputs, and the critical importance of treating user prompts and logs as sensitive assets. Amazon.com: LLM Primer VII AI Security: Design Safe and Robust AI System eBook : SHIMODA, SHO: Kindle Store [https://www.amazon.com/dp/B0GP5T98GJ]