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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

Security Considerations and Measures

In this chapter, we shall study the security threats and risks we can open ourselves up to by using AI-generated code, specifically code from Large Language Models (LLMs), as well as how to guard against these and operate in as safe a way as necessary. We need to learn how weaknesses are exploited, even the subtle ones. This can help you to plan, be vigilant, deal with threats, and avoid them. We’ll get into systems for constant monitoring, effective planning, and collaboration with trusted parties.

LLMs are extremely useful for many tasks, including generating code for software; they can debug, document, comment, and test functions, and even architect entire applications. However, they do present a new space for security challenges, one that is shifting all the time.

If a single line of AI-generated code could compromise an entire system or a prompt could accidentally lead to the exposure of sensitive data, then we have to work hard...

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