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AI-Assisted Exploit Development

Using Large Language Models to Accelerate Vulnerability Research, Reverse Engineering, and Offensive Security

Steve T.

What if your next exploit development workflow had an AI-powered copilot? AI-Assisted Exploit Development explores how large language models are transforming vulnerability research, reverse engineering, and offensive security. From accelerating binary analysis in IDA Pro, Ghidra, and Binary Ninja to enhancing web application testing, fuzzing, debugging, and proof-of-concept exploit creation, this hands-on guide demonstrates how security professionals can leverage AI to work faster and more effectively. Through practical examples, real-world case studies, and offensive security exercises, readers will learn where AI excels, where human expertise remains essential, and how to safely integrate AI assistance into modern exploit development pipelines.

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About the Book

This book is a practical guide for security professionals who want to integrate large language models and AI-powered tools into their vulnerability research workflows. It covers how to use AI with platforms like IDA Pro, Ghidra, Binary Ninja, Burp Suite, debuggers, fuzzers, and MCP-enabled tooling to analyze code, automate repetitive tasks, identify vulnerabilities, generate proof-of-concept exploits, and streamline exploit development pipelines. Through real-world case studies, hands-on examples, and offensive security exercises, readers learn how to combine human expertise with AI assistance to improve efficiency across binary analysis, web application testing, malware research, and vulnerability discovery, while understanding the limitations, validation requirements, and operational security considerations of AI-generated results. Designed for exploit developers, penetration testers, reverse engineers, red team operators, and security researchers.

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About the Author

Steve T.

Steve T. is an experienced IT and cybersecurity professional with more than 18 years of hands-on expertise spanning application security, infrastructure security, and vulnerability management. Having started his career during the early evolution of modern web technologies, he has witnessed firsthand the transformation of enterprise systems, software development practices, and the cybersecurity landscape.

His technical background covers a broad range of disciplines, including web application security, penetration testing, security architecture reviews, secure software development, incident response, and vulnerability research. With extensive experience analyzing complex systems at both the application and operating system levels, Steve combines deep technical knowledge with a practical understanding of real-world business and security challenges.

Throughout his career, he has worked across multiple industries, helping organizations identify, assess, and remediate security risks in critical environments. His approach emphasizes pragmatic security, balancing robust protection with operational and business requirements. Through continuous engagement with emerging technologies and evolving threats, Steve remains committed to advancing secure and resilient IT environments.

Contents

Table of Contents

AI-Assisted Exploit Development

  1. Using Large Language Models to Accelerate Vulnerability Research, Reverse Engineering, and Offensive Security
  2. Dedication
  3. Table of Contents

Introduction: The New Exploit Developer’s Toolkit

  1. Chapter 1: The New Exploit Developer’s Toolkit
  2. Chapter 2: Foundations of AI-Assisted Security Research
  3. Chapter 3: The Evolution of Automation in Security Research
  4. Chapter 4: Setting Up an AI-Augmented Research Environment
  5. Chapter 5: AI in Binary Analysis and Reverse Engineering
  6. Chapter 6: Vulnerability Discovery with AI and Fuzzing
  7. Chapter 7: Proof-of-Concept Exploit Development
  8. Chapter 8: Malware Analysis and AI-Assisted Triage
  9. Chapter 9: Web Application Exploit Development with AI
  10. Chapter 10: Advanced Workflows and Automation
  11. Chapter 11: Operational Security and Risk Management
  12. Chapter 12: The Future of AI-Assisted Exploit Development
  13. Chapter 13: Becoming the AI-Augmented Expert
  14. Chapter 14: Conclusion
  15. References
  16. Appendix A: Quick Reference Card — Model Selection Guide
  17. Appendix B: Prompt Templates — Quick Reference
  18. Appendix C: Common Failure Modes — Quick Reference
  19. Appendix D: Glossary of Key Terms

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