Privacy and Security for Large Language Models by Baihan Lin

Privacy and Security for Large Language Models by Baihan Lin

As LLMs get deployed in production, they bring with them serious privacy risks — memorized training data, prompt injection attacks, model inversion, and more. Most teams discover these vulnerabilities after launch. This book helps you find them before your users do.

Baihan Lin delivers hands-on, privacy-preserving techniques for personalizing AI safely — covering differential privacy, federated learning, adversarial robustness, and secure inference. It's the essential O'Reilly guide for any team serious about deploying LLMs responsibly. Secure your model before someone else exploits it.

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