Author: Anand Vemula Publisher: Anand Vemula ISBN: Category : Computers Languages : en Pages : 68
Book Description
In a world increasingly powered by artificial intelligence, Large Language Models (LLMs) are emerging as powerful tools capable of generating human-quality text, translating languages, and writing different creative content. However, this power comes with hidden risks. This book dives deep into the world of LLM security, providing a comprehensive guide for developers, security professionals, and anyone interested in harnessing the potential of LLMs responsibly. Part 1: Understanding the Landscape The book starts by unpacking the inner workings of LLMs and explores how these models can be misused to generate harmful content or leak sensitive data. We delve into the concept of LLM bias, highlighting how the data used to train these models can influence their outputs. Through real-world scenarios and case studies, the book emphasizes the importance of proactive security measures to mitigate these risks. Part 2: Building Secure LLM Applications The core of the book focuses on securing LLM applications throughout their development lifecycle. We explore the Secure Development Lifecycle (SDLC) for LLMs, emphasizing secure data acquisition, robust model testing techniques, and continuous monitoring strategies. The book delves into MLOps security practices, highlighting techniques for securing model repositories, implementing anomaly detection, and ensuring the trustworthiness of LLM models. Part 3: Governance and the Future of LLM Security With the rise of LLMs, legal and ethical considerations come to the forefront. The book explores data privacy regulations and how to ensure responsible AI development practices. We discuss the importance of explainability and transparency in LLM decision-making for building trust and addressing potential biases. Looking ahead, the book explores emerging security threats and emphasizes the importance of continuous improvement and collaboration within the LLM security community. By proactively addressing these challenges, we can ensure a secure future for LLM applications.
Author: Steve Wilson Publisher: "O'Reilly Media, Inc." ISBN: 1098162161 Category : Computers Languages : en Pages : 197
Book Description
Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models. Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list—a feat accomplished by more than 400 industry experts—this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI. You'll learn: Why LLMs present unique security challenges How to navigate the many risk conditions associated with using LLM technology The threat landscape pertaining to LLMs and the critical trust boundaries that must be maintained How to identify the top risks and vulnerabilities associated with LLMs Methods for deploying defenses to protect against attacks on top vulnerabilities Ways to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization
Author: Steve Wilson Publisher: "O'Reilly Media, Inc." ISBN: 109816217X Category : Computers Languages : en Pages : 200
Book Description
Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models. Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list—a feat accomplished by more than 400 industry experts—this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI. You'll learn: Why LLMs present unique security challenges How to navigate the many risk conditions associated with using LLM technology The threat landscape pertaining to LLMs and the critical trust boundaries that must be maintained How to identify the top risks and vulnerabilities associated with LLMs Methods for deploying defenses to protect against attacks on top vulnerabilities Ways to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization
Author: Hunaidkhan Pathan Publisher: BPB Publications ISBN: 9365891043 Category : Computers Languages : en Pages : 306
Book Description
DESCRIPTION The book is all about the basics of NLP, generative AI, and their specific component LLM. In this book, we have provided conceptual knowledge about different terminologies and concepts of NLP and NLG with practical hands-on. This comprehensive book offers a deep dive into the world of NLP and LLMs. Starting with the fundamentals of Python programming and code editors, the book gradually introduces NLP concepts, including text preprocessing, word embeddings, and transformer architectures. You will explore the architecture and capabilities of popular models like GPT-3 and BERT. The book also covers practical aspects of LLM usage for RAG applications using frameworks like LangChain and Hugging Face and deploying them in real world applications. With a focus on both theoretical knowledge and hands-on experience, this book is ideal for anyone looking to master the art of NLP and LLMs. The book also contains AWS Cloud deployment, which will help readers step into the world of cloud computing. As the book contains both theoretical and practical approaches, it will help the readers to gain confidence in the deployment of LLMs for any use cases, as well as get acquainted with the required generative AI knowledge to crack the interviews. KEY FEATURES ● Covers Python basics, NLP concepts, and terminologies, including LLM and RAG concepts. ● Provides exposure to LangChain, Hugging Face ecosystem, and chatbot creation using custom data. ● Guides on integrating chatbots with real-time applications and deploying them on AWS Cloud. WHAT YOU WILL LEARN ● Basics of Python, which contains Python concepts, installation, and code editors. ● Foundation of NLP and generative AI concepts and different terminologies being used in NLP and generative AI domain. ● LLMs and their importance in the cutting edge of AI. ● Creating chatbots using custom data using open source LLMs without spending a single penny. ● Integration of chatbots with real-world applications like Telegram. WHO THIS BOOK IS FOR This book is ideal for beginners and freshers entering the AI or ML field, as well as those at an intermediate level looking to deepen their understanding of generative AI, LLMs, and cloud deployment. TABLE OF CONTENTS 1. Introduction to Python and Code Editors 2. Installation of Python, Required Packages, and Code Editors 3. Ways to Run Python Scripts 4. Introduction to NLP and its Concepts 5. Introduction to Large Language Models 6. Introduction of LangChain, Usage and Importance 7. Introduction of Hugging Face, its Usage and Importance 8. Creating Chatbots Using Custom Data with LangChain and Hugging Face Hub 9. Hyperparameter Tuning and Fine Tuning Pre-Trained Models 10. Integrating LLMs into Real-World Applications–Case Studies 11. Deploying LLMs in Cloud Environments for Scalability 12. Future Directions: Advances in LLMs and Beyond Appendix A: Useful Tips for Efficient LLM Experimentation Appendix B: Resources and References
Author: John Sotiropoulos Publisher: Packt Publishing Ltd ISBN: 1835088678 Category : Computers Languages : en Pages : 586
Book Description
Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI and LLM projects with practical examples leveraging OWASP, MITRE, and NIST Key Features Understand the connection between AI and security by learning about adversarial AI attacks Discover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMs Implement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAdversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips cybersecurity professionals with the skills to secure AI technologies, moving beyond research hype or business-as-usual strategies. The strategy-based book is a comprehensive guide to AI security, presenting a structured approach with practical examples to identify and counter adversarial attacks. This book goes beyond a random selection of threats and consolidates recent research and industry standards, incorporating taxonomies from MITRE, NIST, and OWASP. Next, a dedicated section introduces a secure-by-design AI strategy with threat modeling to demonstrate risk-based defenses and strategies, focusing on integrating MLSecOps and LLMOps into security systems. To gain deeper insights, you’ll cover examples of incorporating CI, MLOps, and security controls, including open-access LLMs and ML SBOMs. Based on the classic NIST pillars, the book provides a blueprint for maturing enterprise AI security, discussing the role of AI security in safety and ethics as part of Trustworthy AI. By the end of this book, you’ll be able to develop, deploy, and secure AI systems effectively.What you will learn Understand poisoning, evasion, and privacy attacks and how to mitigate them Discover how GANs can be used for attacks and deepfakes Explore how LLMs change security, prompt injections, and data exposure Master techniques to poison LLMs with RAG, embeddings, and fine-tuning Explore supply-chain threats and the challenges of open-access LLMs Implement MLSecOps with CIs, MLOps, and SBOMs Who this book is for This book tackles AI security from both angles - offense and defense. AI builders (developers and engineers) will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats and mitigate risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you’ll need a basic understanding of security, ML concepts, and Python.
Author: John Sotiropoulos Publisher: ISBN: 9781835087985 Category : Computers Languages : en Pages : 0
Book Description
Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI and LLM projects with practical examples leveraging OWASP, MITRE, and NIST Key Features: - Understand the connection between AI and security by learning about adversarial AI attacks - Discover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMs - Implement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systems - Purchase of the print or Kindle book includes a free PDF eBook Book Description: Adversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips cybersecurity professionals with the skills to secure AI technologies, moving beyond research hype or business-as-usual strategies. The strategy-based book is a comprehensive guide to AI security, presenting a structured approach with practical examples to identify and counter adversarial attacks. This book goes beyond a random selection of threats and consolidates recent research and industry standards, incorporating taxonomies from MITRE, NIST, and OWASP. Next, a dedicated section introduces a secure-by-design AI strategy with threat modeling to demonstrate risk-based defenses and strategies, focusing on integrating MLSecOps and LLMOps into security systems. To gain deeper insights, you'll cover examples of incorporating CI, MLOps, and security controls, including open-access LLMs and ML SBOMs. Based on the classic NIST pillars, the book provides a blueprint for maturing enterprise AI security, discussing the role of AI security in safety and ethics as part of Trustworthy AI. By the end of this book, you'll be able to develop, deploy, and secure AI systems effectively. What You Will Learn: - Understand poisoning, evasion, and privacy attacks and how to mitigate them - Discover how GANs can be used for attacks and deepfakes - Explore how LLMs change security, prompt injections, and data exposure - Master techniques to poison LLMs with RAG, embeddings, and fine-tuning - Explore supply-chain threats and the challenges of open-access LLMs - Implement MLSecOps with CIs, MLOps, and SBOMs Who this book is for: This book tackles AI security from both angles - offense and defense. AI builders (developers and engineers) will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats and mitigate risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you'll need a basic understanding of security, ML concepts, and Python. Table of Contents - Getting Started with AI - Building Our Adversarial Playground - Security and Adversarial AI - Poisoning Attacks - Model Tampering with Trojan Horses and Model Reprogramming - Supply Chain Attacks and Adversarial AI - Evasion Attacks against Deployed AI - Privacy Attacks - Stealing Models - Privacy Attacks - Stealing Data - Privacy-Preserving AI - Generative AI - A New Frontier - Weaponizing GANs for Deepfakes and Adversarial Attacks - LLM Foundations for Adversarial AI - Adversarial Attacks with Prompts - Poisoning Attacks and LLMs - Advanced Generative AI Scenarios - Secure by Design and Trustworthy AI - AI Security with MLSecOps - Maturing AI Security
Author: Miao, Fengchun Publisher: UNESCO Publishing ISBN: 9231004476 Category : Political Science Languages : en Pages : 50
Book Description
Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]
Author: El Bachir Boukherouaa Publisher: International Monetary Fund ISBN: 1589063953 Category : Business & Economics Languages : en Pages : 35
Book Description
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Author: Virginia Dignum Publisher: Springer Nature ISBN: 3030303713 Category : Computers Languages : en Pages : 127
Book Description
In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens.
Author: Anand Vemula
Author: Steve Wilson
Author: Steve Wilson
Author: Hunaidkhan Pathan
Author: John Sotiropoulos
Author: Ken Huang
Author: John Sotiropoulos
Author: Miao, Fengchun
Author: El Bachir Boukherouaa
Author: Virginia Dignum