Breaking

46-Chapter AI/LLM Red Team Handbook Released With Automated Test Tools

July 9, 2026 at 00:14 EDT

Security
Open Source
AI Agents

An open-source, 46-chapter security curriculum for assessing AI and LLM systems—titled "AI / LLM Red Team Field Manual & Consultant's Handbook"—has been published on GitHub. Delivered as a "Gold Master" release, it bundles the handbook itself with operational checklists and a Python-based automated testing toolkit.

Gold Master Release · v1.46.263 (audited)

A 46-Chapter Open-Source Field Manual for Red-Teaming AI & LLM Systems

An operational handbook, field checklists, and a Python testing toolkit — bundled for consultants running adversarial security assessments against LLMs, AI agents, and RAG pipelines. Free under CC BY-SA 4.0.

Chapters across 8 parts (I–VIII)

Supported APIs: OpenAI · Anthropic · Ollama

Formats: GitHub · GitBook · PDF

3.8+

Python version for the automation toolkit

What ships in the package

Three components covering the full assessment workflow

HANDBOOK

46-chapter curriculum

Ethics, threat modeling, attacks, defense & compliance

FIELD MANUAL

Checklists & payloads

Field references built to be immediately practical

PYTHON TOOLKIT

Automated testing

Prompt-injection tester, fuzzing & safety validation

Eight parts, front to back

The 46 chapters walk a consultant through the full assessment arc

PART I

Foundations

PART II

Preparation

PART III

Technical Fundamentals

PART IV

Pipeline Security

PART V

Attacks & Techniques

PART VI

Defense & Mitigation

PART VII

Advanced Operations

PART VIII

Advanced Topics

The threats it addresses — and how it differs

Prompt injection Jailbreaking Model theft Data leakage Adversarial ML

Differentiators vs. existing open resources:

Sheer scale of 46 chapters · pairing with the Field Manual · integrated Python automation · compliance coverage for the EU AI Act and ISO 42001.

Why it's welcomed

A structured curriculum paired with automation tooling is seen as useful for consultants and operations staff, with checklists and payloads expected to be immediately practical.

Caveats to note

The release is recent, so real usage reports are scarce. Users must respect each target LLM's terms of service, account for API dependence, and expect environment-specific customization.

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