The Developers Playbook For Large Language Model Security

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The Developer's Playbook for Large Language Model Security

The Developer's Playbook for Large Language Model Security
Author :
Publisher : "O'Reilly Media, Inc."
Total Pages : 200
Release :
ISBN-10 : 9781098162177
ISBN-13 : 109816217X
Rating : 4/5 (17X Downloads)

Book Synopsis The Developer's Playbook for Large Language Model Security by : Steve Wilson

Download or read book The Developer's Playbook for Large Language Model Security written by Steve Wilson and published by "O'Reilly Media, Inc.". This book was released on 2024-09-03 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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


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