Adversarial AI Security and Model Analysis with Python and C++: Defensive Techniques for LLM Securit

Marke: Takehiro Kanegi

Variante: Taschenbuch

Eigenschaften:

Reactive PublishingAs artificial intelligence systems become foundational to modern infrastructure, they also become high-value targets for increasingly sophisticated adversarial attacks. Adversarial AI Security and Model Analysis with Python and C++ delivers a rigorous, practitioner-focused exploration of how large language models (LLMs) and machine learning systems can be analyzed, stress-tested, and secured against real-world threats.This book bridges the gap between theoretical ML security and applied engineering. It introduces structured methodologies for understanding model behavior under adversarial conditions, examining how vulnerabilities emerge in training pipelines, inference layers, and deployment environments. Through hands-on implementation using Python and C++, readers gain insight into how attackers probe models, how weaknesses manifest, and how defensive architectures can be designed to mitigate exposure.Key areas covered include adversarial testing methodologies, model introspection techniques, threat modeling for LLM systems, and practical MLSecOps patterns for secure deployment at scale. The book also explores red teaming workflows from an engineering perspective, focusing on how to systematically evaluate robustness without relying on black-box assumptions.Rather than focusing on hype or abstract theory, this work emphasizes disciplined engineering practice: reproducible experiments, structured analysis, and security-first design principles applicable to production AI systems.Ideal for ML engineers, security practitioners, and advanced developers working with generative AI systems, this book provides a technical foundation for understanding and defending the next generation of intelligent systems.

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