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Coding with ChatGPT and Other LLMs

You're reading from   Coding with ChatGPT and Other LLMs Navigate LLMs for effective coding, debugging, and AI-driven development

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781805125051
Length 304 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Dr. Vincent Austin Hall Dr. Vincent Austin Hall
Author Profile Icon Dr. Vincent Austin Hall
Dr. Vincent Austin Hall
Chigbo Uzokwelu Chigbo Uzokwelu
Author Profile Icon Chigbo Uzokwelu
Chigbo Uzokwelu
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1: Introduction to LLMs and Their Applications
2. Chapter 1: What is ChatGPT and What are LLMs? FREE CHAPTER 3. Chapter 2: Unleashing the Power of LLMs for Coding: A Paradigm Shift 4. Chapter 3: Code Refactoring, Debugging, and Optimization: A Practical Guide 5. Part 2: Be Wary of the Dark Side of LLM-Powered Coding
6. Chapter 4: Demystifying Generated Code for Readability 7. Chapter 5: Addressing Bias and Ethical Concerns in LLM-Generated Code 8. Chapter 6: Navigating the Legal Landscape of LLM-Generated Code 9. Chapter 7: Security Considerations and Measures 10. Part 3: Explainability, Shareability, and the Future of LLM-Powered Coding
11. Chapter 8: Limitations of Coding with LLMs 12. Chapter 9: Cultivating Collaboration in LLM-Enhanced Coding 13. Chapter 10: Expanding the LLM Toolkit for Coders: Beyond LLMs 14. Part 4: Maximizing Your Potential with LLMs: Beyond the Basics
15. Chapter 11: Helping Others and Maximizing Your Career with LLMs 16. Chapter 12: The Future of LLMs in Software Development 17. Index 18. Other Books You May Enjoy

Understanding the security risks of LLMs

Here, we look at the security considerations in AI-assisted programming.

LLMs have revolutionized many aspects of software development, from code generation to documentation. However, their integration into the development process brings new security challenges that developers must understand and address. This section explores the security risks associated with LLMs, both in their general use and specifically in code generation, providing practical advice for technical professionals working in real-world scenarios.

Data privacy and confidentiality

This subsection highlights several threats and weaknesses to be aware of when using LLMs in general. The next subsection is about code from LLMs specifically.

LLMs are trained on vast amounts of data, and when used, they process user inputs that may contain sensitive information. This raises several privacy and confidentiality concerns:

  • Training Data Leakage: LLMs might inadvertently...
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