Adversarial AI Attacks, Mitigations, and Defense Strategies. (Record no. 16016)
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| fixed length control field | 03859nam a2200265uu 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250710182906.0 |
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| 024 80 - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 9781835088678 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | PACKT |
| Transcribing agency | PACKT |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | en |
| 044 ## - COUNTRY OF PUBLISHING/PRODUCING ENTITY CODE | |
| MARC country code | GB |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | John Sotiropoulos |
| Relator term | author. |
| 245 00 - TITLE STATEMENT | |
| Title | Adversarial AI Attacks, Mitigations, and Defense Strategies. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | GB: |
| Name of publisher, distributor, etc. | Packt, |
| Date of publication, distribution, etc. | 2024-07-26. |
| 263 ## - PROJECTED PUBLICATION DATE | |
| Projected publication date | 2024-07-26 |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | GB: |
| Name of producer, publisher, distributor, manufacturer | Packt, |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 586. |
| 377 ## - ASSOCIATED LANGUAGE | |
| Language code | en |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <p><b>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</b></p><h4>Key Features</h4><ul><li>Understand the connection between AI and security by learning about adversarial AI attacks</li><li>Discover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMs</li><li>Implement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systems</li><li>Purchase of the print or Kindle book includes a free PDF eBook</li></ul><h4>Book Description</h4>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.<h4>What you will learn</h4><ul><li>Understand poisoning, evasion, and privacy attacks and how to mitigate them</li><li>Discover how GANs can be used for attacks and deepfakes</li><li>Explore how LLMs change security, prompt injections, and data exposure</li><li>Master techniques to poison LLMs with RAG, embeddings, and fine-tuning</li><li>Explore supply-chain threats and the challenges of open-access LLMs</li><li>Implement MLSecOps with CIs, MLOps, and SBOMs</li></ul><h4>Who this book is for</h4>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. . |
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| System details note | Data in extended ASCII character set. |
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| System details note | Mode of access: Internet. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | PACKT |
| 773 0# - HOST ITEM ENTRY | |
| Title | Adversarial AI Attacks, Mitigations, and Defense Strategies |
| Place, publisher, and date of publication | GB,Packt,2024-07-26 |
| Physical description | 586 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://learning.packt.com/product/475664">https://learning.packt.com/product/475664</a> |
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