Jailbreaking LLMs

As Large Language Models (LLMs) become deeply integrated into enterprise applications, customer support systems, internal workflows, and decision-making platforms, they also introduce a rapidly expanding attack surface. Jailbreaking LLMs explores how modern AI systems can be manipulated through prompt injections, adversarial attacks, context manipulation, data poisoning, and jailbreak techniques and why organizations must treat these threats as critical security risks rather than theoretical concerns. With two-thirds of enterprises now deploying generative AI systems in production, the stakes have never been higher.

Through real-world examples, practical frameworks, and enterprise-focused security strategies, this book equips readers to design, secure, monitor, and defend LLM-powered systems at scale. Readers will learn to identify vulnerabilities, implement secure AI architectures, conduct red-teaming exercises, establish governance controls, and build resilient AI environments that align innovation with security, compliance, and responsible AI practices.

Understand the risks and mechanics of LLM jailbreaking prompt injection, adversarial inputs, data poisoning, and context manipulation

Identify and mitigate prompt injection and adversarial attacks

Design secure and enterprise-ready LLM architectures

Build monitoring, detection, and AI security response workflows

Apply red-teaming and defensive testing strategies for LLM systems

Embed ethical AI governance and regulatory considerations into deployment models