Privacy and Security for Large Language Models: Hands-On Privacy-Preserving Techniques for Personalized AI by Baihan Lin

Privacy and Security for Large Language Models: Hands-On Privacy-Preserving Techniques for Personalized AI  by Baihan Lin

The core thesis of Privacy and Security for Large Language Models is that foundation models introduce entirely new attack surfaces that traditional cybersecurity architectures are fundamentally ill-equipped to handle. Large Language Models are highly fluid, probabilistic systems. They do not just execute explicit code paths; they interpret natural language prompts, memorize training data characteristics, and rely on external retrieval tools (like RAG databases) that can be easily manipulated. Lin establishes that securing production AI requires a comprehensive defense-in-depth framework that guards the model across its entire lifecycle: from raw training datasets to runtime token inferences.

Rather than offering generic security tips, Lin walks readers through the exact mechanics of advanced AI exploitation and defense. The book outlines how attackers manipulate context windows through automated indirect prompt injections, extract confidential enterprise secrets via data leakage exploits, and force corporate chatbots to bypass safety protocols using complex jailbreaking vectors. To counter these threats, the text serves as an operational masterclass in cutting-edge mitigation frameworks. It details how to mathematically guarantee user anonymity using Differential Privacy (DP), run computations on untrusted clouds using Homomorphic Encryption, and build real-time guardrail networks that actively sanitize user inputs and model outputs.