May 14, 2024
Updated July 21, 2025
12 minute read
A Comprehensive Guide to Prompt Injection
Prompt injection is a critical vulnerability affecting the artificial intelligence systems that are becoming increasingly integrated into our daily lives. At a high level, it is a cyberattack that involves tricking a Large Language Model (LLM) by feeding it cleverly crafted inputs, known as prompts, that cause it to behave in unintended and often malicious ways. This is less like hacking a computer in the traditional sense and more like a form of social engineering for AI, where an attacker manipulates the model by exploiting the very way it processes natural language instructions.
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Find a path to becoming a Prompt Injection. Learn more at:
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Reading list
We've selected 31 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Prompt Injection.
Is specifically focused on LLM security, including prompt injection as a key vulnerability. It offers a comprehensive guide to understanding, exploiting, and defending against attacks on LLMs, providing practical examples and techniques. This is essential reading for those looking to deepen their understanding of prompt injection from a technical perspective.
This practical guide focuses exclusively on LLM security challenges and vulnerabilities, including a dedicated chapter on prompt injection. It draws on the collective wisdom from the creation of the OWASP Top 10 for LLMs, offering real-world guidance and strategies for developers and security teams. is highly relevant for professionals working directly with LLMs.
This guide focuses on securing LLM applications, covering vulnerabilities and threats relevant to developers. It offers practical tools and techniques, including real-world case studies, to help safeguard LLM projects. is highly relevant for those involved in the development and deployment of LLM-based systems.
Provides a comprehensive overview of AI and ML fundamentals, with a strong emphasis on security, ethics, and privacy. It covers common threats, vulnerabilities, and attack vectors, including those relevant to LLMs, making it highly valuable for gaining a broad understanding of the landscape surrounding prompt injection. It useful reference tool for professionals and students alike.
This comprehensive guide is dedicated to the security of Large Language Models (LLMs), covering strategies, best practices, and future trends. It delves into the threat landscape and vulnerabilities specific to LLMs, making it highly relevant for understanding prompt injection in depth.
Timely resource that explores the intersection of generative AI, cybersecurity, and ethics. It provides insights into the security risks and ethical considerations of generative AI technologies, which are directly relevant to understanding the broader implications of prompt injection. It is suitable for a wide audience, including leaders and professionals.
Explores the challenge of aligning AI systems with human values. While not directly about prompt injection techniques, it provides essential background on the fundamental problem of controlling AI behavior, which is at the heart of prompt injection vulnerabilities. It's a must-read for understanding the broader implications of AI safety.
Written by a leading AI researcher, this book examines the risks of advanced AI and how to ensure it remains beneficial to humanity. It provides a foundational understanding of the control problem in AI, which is highly relevant to the challenges posed by prompt injection and other adversarial attacks aimed at manipulating AI behavior.
This guide for cybersecurity professionals covers adversarial AI attacks and defense strategies. Understanding adversarial attacks is crucial for comprehending prompt injection, as it form of adversarial manipulation. The book provides insights into threat modeling and securing AI systems.
This practical book focuses on transformers, the dominant architecture for state-of-the-art NLP, including LLMs. It provides hands-on guidance for working with transformers, which is highly relevant for understanding the technology targeted by prompt injection attacks.
Explores the potential impact of superintelligent AI, including the existential risks. While a more philosophical and long-term view, it highlights the importance of controlling advanced AI, a concept directly related to the security challenges, such as prompt injection, that arise with increasingly capable models. It's considered a classic in AI safety discussions.
Provides a foundational understanding of adversarial machine learning, a core concept underlying prompt injection and other AI attacks. It covers the theory and tools for building robust ML in adversarial environments and discusses various attack types and defense mechanisms. While not solely focused on LLMs, it provides crucial prerequisite knowledge.
Explores the evolving relationship between cybersecurity and AI, including how generative AI is reshaping security landscapes. It provides a good overview of both the threats and opportunities presented by AI in cybersecurity, offering context for the specific threat of prompt injection.
Serves as a comprehensive guide to cybersecurity and AI capabilities, focusing on identifying potential harms and protecting against danger. It is beneficial for understanding the broader context of AI security and the need for robust guardrails against attacks like prompt injection.
Explores the application of AI in cybersecurity, covering various techniques for threat detection and prevention. While it has a broader scope than just LLMs, the principles and methods discussed for securing AI systems are applicable to understanding defenses against attacks like prompt injection. It's a practical guide for implementing AI in security.
This edited volume addresses the challenges of constructing safe and secure advanced machine intelligence. It includes chapters on various aspects of AI safety and control, providing a broader perspective on the security concerns surrounding AI, which includes vulnerabilities like prompt injection.
Focuses on using machine learning for cybersecurity, covering techniques to detect and counter cyber threats. It provides fundamentals and real-world case studies on applying ML to security, offering valuable background for understanding how AI can be used defensively against attacks like prompt injection.
Delves into the transformation of security through the integration of AI. It covers topics such as artificial neural networks, intrusion detection, and large language models, providing a broad perspective on how AI is used to enhance security. This is relevant for understanding the defensive side of the AI security landscape.
This seminal textbook on deep learning, the technology that powers LLMs. A deep understanding of deep learning architectures and training is beneficial for truly grasping the technical underpacing of prompt injection vulnerabilities and potential defenses. It difficult but essential reference for advanced learners.
While not solely focused on LLMs, this book provides a strong foundation in applying machine learning to security issues. Understanding the broader field of machine learning security is beneficial for comprehending the context of prompt injection. It offers a practical guide and a toolkit of ML algorithms applicable to security problems, making it a valuable reference.
Provides an accessible guide to the potential risks AI may pose and how to develop and deploy AI safely. It covers malicious use and accidental failures, offering a societal perspective on AI safety that complements the technical focus on prompt injection.
This handbook offers interdisciplinary perspectives on responsible AI, including sections on AI for security applications and governance. While not solely focused on prompt injection, it provides a broader understanding of the ethical, social, and legal considerations surrounding AI security, which are important for a holistic view.
Examines the ethical implications of AI and generative AI, including their role in misinformation, bias, and privacy issues. Understanding these ethical dimensions is crucial for comprehending the potential harms that can be facilitated through prompt injection and the importance of ethical safeguards.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/g7e8et/prompt
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