The Developer's Playbook for Large Language Model Security Audiobook By Steve Wilson cover art

The Developer's Playbook for Large Language Model Security

Building Secure AI Applications

Preview
LIMITED TIME OFFER

3 months free
Try for $0.00
Offer ends July 31, 2025 at 11:59PM PT.
Pick 1 audiobook a month from our unmatched collection.
Listen all you want to thousands of included audiobooks, Originals, and podcasts.
Access exclusive sales and deals.
Premium Plus auto-renews for $14.95/mo after 3 months. Cancel anytime.

The Developer's Playbook for Large Language Model Security

By: Steve Wilson
Narrated by: Jonathan Yen
Try for $0.00

$0.00/mo. after 3 months. Offer ends July 31, 2025 at 11:59PM PT. Cancel anytime.

Buy for $17.19

Buy for $17.19

Confirm purchase
Pay using card ending in
By confirming your purchase, you agree to Audible's Conditions of Use, License, and Amazon's Privacy Notice. Taxes where applicable.
Cancel

About this listen

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
  • How to identify the top risks and vulnerabilities associated with LLMs
  • Methods for deploying defenses to protect against attacks on top vulnerabilities

And more!

©2024 Stephen Wilson (P)2025 Ascent Audio
Computer Science Technology Inspiring Data Science Machine Learning Software Development
No reviews yet