Jailbreaking LLMs

Beschreibung

Large Language Models (LLMs) are rapidly transforming how enterprises operate, powering customer support, internal assistants, automated workflows, search, analytics, and decision-making systems. But as organizations adopt AI at scale, they are also introducing a new and expanding attack surface. Jailbreaking LLMs explores how attackers manipulate AI systems through prompt injection, jailbreaks, adversarial inputs, data poisoning, context manipulation, retrieval attacks, and unsafe tool usage to bypass safeguards, leak sensitive data, and influence AI behavior in unexpected ways.    This book provides a practical guide to understanding, testing, and defending enterprise AI systems in the real world. Through real attack scenarios, security frameworks, red-teaming methodologies, governance strategies, and defensive architecture patterns, readers will learn how to build secure, resilient, and enterprise-ready LLM deployments. Covering everything from RAG security and agentic systems to incident response, AI governance, runtime monitoring, and future attack trends, this book connects AI innovation with modern cybersecurity practices.  What you will learn  Understand how LLM jailbreaks, prompt injection, and adversarial attacks work  Identify vulnerabilities across enterprise AI systems, RAG pipelines, agents, and APIs  Design and deploy secure, enterprise-ready LLM architectures   Implement monitoring, logging, detection, and incident response workflows for AI systems  Apply red-teaming and defensive testing strategies to evaluate LLM security  Build governance, compliance, and ethical AI controls into enterprise deployments  Understand emerging AI attack trends and future cybersecurity risks 

Large Language Models (LLMs) are rapidly transforming how enterprises operate, powering customer support, internal assistants, automated workflows, search, analytics, and decision-making systems. But as organizations adopt AI at scale, they are also introducing a new and expanding attack surface. Jailbreaking LLMs explores how attackers manipulate AI systems through prompt injection, jailbreaks, adversarial inputs, data poisoning, context manipulation, retrieval attacks, and unsafe tool usage to bypass safeguards, leak sensitive data, and influence AI behavior in unexpected ways. 

This book provides a practical guide to understanding, testing, and defending enterprise AI systems in the real world. Through real attack scenarios, security frameworks, red-teaming methodologies, governance strategies, and defensive architecture patterns, readers will learn how to build secure, resilient, and enterprise-ready LLM deployments. Covering everything from RAG security and agentic systems to incident response, AI governance, runtime monitoring, and future attack trends, this book connects AI innovation with modern cybersecurity practices. 

What you will learn 

• Understand how LLM jailbreaks, prompt injection, and adversarial attacks work 

• Identify vulnerabilities across enterprise AI systems, RAG pipelines, agents, and APIs 

• Design and deploy secure, enterprise-ready LLM architectures 

•  Implement monitoring, logging, detection, and incident response workflows for AI systems 

• Apply red-teaming and defensive testing strategies to evaluate LLM security 

• Build governance, compliance, and ethical AI controls into enterprise deployments 

• Understand emerging AI attack trends and future cybersecurity risks 

Who this book is for 

This book is for cybersecurity professionals, AI/ML engineers, enterprise architects, security analysts, SOC teams, IT leaders, and technical decision-makers responsible for building, deploying, or securing AI-powered systems. It is also valuable for practitioners who want to better understand the security, governance, and operational challenges that come with adopting Large Language Models in enterprise environments. 

Autor*in

Priyanka Neelakrishnan

Themen in »Jailbreaking LLMs«

Large Language Models (LLMs) LLM Security Jailbreaking LLMs Prompt Injection Attacks Adversarial AI Enterprise AI Security AI Governance Ethical AI AI Threat Modeling AI Incident Response Data Protection for AI Generative AI Risks

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