Red Teaming with AI Agents: Designing Multi-Agent Systems for Scalable Threat Simulation, Adversarial Testing, and Secure AI Workflows

Charette, Kenneth

ISBN 13: 9798298671149

Verlag: Independently published, 2025

Beschreibung

Bestandsnummer des Verkäufers 50987025-n

Bibliografische Details

• Titel: Red Teaming with AI Agents: Designing Multi-Agent Systems for Scalable Threat Simulation, Adversarial Testing, and Secure AI Workflows
• Verlag: Independently published
• Erscheinungsjahr: 2025
• Sprache: Englisch
• ISBN-13: 9798298671149
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

In a world where artificial intelligence systems are being deployed across critical infrastructures, LLMs, APIs, and enterprise pipelines, the risks from adversarial exploitation have never been higher. Red Teaming with AI Agents equips you with the tools, frameworks, and mindset to proactively test, harden, and secure modern AI-powered systems through intelligent, coordinated agent-based simulations. This book is your step-by-step tactical guide to building scalable red team infrastructures using Python, LangChain, CrewAI, AutoGen, and reinforcement learning techniques.

Written by a seasoned AI security engineer and red team architect, this book distills field-tested strategies into actionable technical workflows. It integrates insights from enterprise security engagements, MLOps case studies, and active community tools to help you design red teaming systems that mirror real-world adversarial behavior — from insider threat emulation to LLM prompt injection campaigns.

About the Technology:
Agent-based systems are transforming the way we simulate attacks and assess robustness in AI environments. By combining reasoning models, dynamic memory, tool usage, and inter-agent communication, these autonomous agents can mimic real-world adversaries at scale. When paired with modern orchestration tools and containerized environments, red team agents can continuously evaluate models, pipelines, and endpoints in ways that are repeatable, adaptive, and safe.

What's Inside:

• Full system architecture for multi-agent red team platforms
• Reconnaissance, deception, disruption, and insider simulation agents
• Modeling and scoring AI threats like prompt injection and model extraction
• Containerized deployment pipelines with observability and CI/CD hooks
• Agent planning with behavior trees, rule engines, and LLM-integrated logic
• Case studies in MLOps, FinTech, and API misuse simulations
• Legal, ethical, and future-focused perspectives on red teaming with AI

Who This Book is For:
This book is written for security engineers, red teamers, AI researchers, and machine learning practitioners who want to move beyond static testing and embrace continuous adversarial validation. It is ideal for professionals deploying LLMs, building SaaS products, managing MLOps pipelines, or responsible for secure AI governance and incident response.

As AI-driven systems become central to business, healthcare, finance, and infrastructure, adversarial testing can no longer be an afterthought. New attack surfaces are emerging faster than traditional defenses can adapt. The sooner you operationalize AI red teaming, the better you can protect, audit, and strengthen your systems — before real threats find them first.

This is more than just a book — it’s a practical reference, a security playbook, and a long-term asset for your AI assurance strategy. With JSON templates, agent blueprints, planning checklists, and integration guides, it arms you with everything you need to build, test, and deploy real-world red team agents with confidence and clarity.

Don’t wait for a breach or a compliance deadline to start thinking about security. Start red teaming your AI systems now. Equip yourself with the tools, knowledge, and systems to challenge your models before attackers do. Get your copy of Red Teaming with AI Agents today — and begin building safer, smarter, and more resilient AI ecosystems.