The Red Teaming with Multi-Agent AI: Designing multi-agent AI systems for secure, compliant, and auditable LLM testing

The Red Teaming with Multi-Agent AI: Designing multi-agent AI systems for secure, compliant, and auditable LLM testing

Red Teaming with Multi-Agent AI: Designing multi-agent AI systems for secure, compliant, and auditable LLM testing is a hands-on guide to building a production-grade AI red teaming platform. You’ll design attacker/defender/judge/orchestrator agents, generate and mutate attack campaigns, enforce privacy-by-design, and ship replayable evidence your auditors will love—all with practical, runnable patterns and code. Written in a build-first style: every concept is paired with stdlib-only Python examples you can paste and run. Aligns to recognized governance practices (version pins, replay gates, ledgered evidence, role-based approvals). Field-tested patterns from real platform rollouts: self-play loops, meta-agent routing, CI/CD gates, and auditable bundles. About the Technology Focused on LLM systems with tools (retrieval, web fetch, calculators) where risks emerge from prompt injection, tool misuse, and data leakage. - Uses multi-agent architectures (attacker/defender/judge/orchestrator) to simulate adversaries, enforce guardrails, and score outcomes. - Bakes in observability (structured logs, correlation IDs), cost control (token budgets), and resilience (chaos testing). What’s Inside Design blueprints for control-plane/data-plane, message contracts, and evidence storage. - Technique libraries: jailbreaks, tool-chain attacks, indirect prompt injection—plus safe mutation operators. - Defender playbooks: counter-prompts, allowlists, tool caps, and leakage-safe composition. - Judge systems: rubrics, rationales, cross-judging, aggregation, and human adjudication workflows. - Self-play & feedback: turn S1/S0 failures into curated training/policy updates with replay guarantees. - Ops at scale: tracing, artifact capture, cost modeling, chaos drills, SLOs, and incident runbooks. - SDLC integration: CI/CD gates, drift detection, ticketing + SLAs, and executive/regulatory report packs. - Case studies: a chat app with tools and private-docs Q&A (RAG) with ACL-first retrieval and citation-required answers. Who This Book Is For AI platform & safety engineers who need reproducible, auditable red teaming. - Security & risk leaders responsible for policy, approvals, and compliance evidence. - ML engineers & product teams shipping LLM features that must stay safe under real-world pressure. - Startups & enterprises building internal guardrails or commercial assurance offerings. LLM features are shipping faster than your guardrails. Regulators, customers, and attackers won’t wait. The longer you delay automated testing and evidence capture, the more expensive your incidents, rollbacks, and audits become. You won’t wade through theory for weeks. The book’s copy-paste runnable slices get you from a sketch to a working platform in days, and from proof-of-concept to controlled rollouts in a few sprints—with CI gates and reporting ready when leadership asks. One preventable incident can cost more than your entire platform budget. This book helps you: Cut escape rate with targeted, evolving tests. - Reduce audit prep from weeks to hours using replayable evidence. - Control cloud spend via token budgeting, tool caps, and smart routing. - Increase shipping velocity by turning red teaming into part of CI/CD—not a blocker. Build your multi-agent red teaming platform now—pin versions, run self-play tonight, ship safer LLM features tomorrow. Grab the book, open your editor, and paste the first working blueprint. Your users—and your auditors—will thank you.