AI systems now sit inside core business functions and influence decisions, automate workflows, summarize sensitive information, and take action through tools. 

As enterprises integrate Large Language Models (LLMs) into core business logic, they introduce non-deterministic risks that traditional software testing cannot detect. 

AI Red Teaming addresses these risks through structured adversarial evaluation, simulating attacks on models, agents, and retrieval pipelines to identify failures before deployment.

This guide aggregates essential resources for building and scaling an AI red teaming capability, progressing from foundational definitions to advanced automated architectures, to guidance on how to perform specific tests such as for Prompt Injection vulnerabilities.

Establishing the Discipline

Security teams often struggle to adapt existing playbooks to generative AI red teaming because the failures are behavioral rather than functional. 

This section defines the core concepts of AI red teaming, distinguishing it from network penetration testing and establishing the necessary organizational roles.

It then provides the baseline fundamentals required to build a compliant and effective assurance program.

AI Red Teaming 101: What is AI Red Teaming?

This post defines the scope of AI red teaming, distinguishing it from infrastructure penetration testing. 

It outlines the specific roles required including Red, Blue, Purple, and White teams, and details the operational lifecycle necessary to identify risks such as toxicity, bias, and security breaches in generative systems.

In this post, you will learn:

• The Definition: The distinction between testing infrastructure locks (penetration testing) and testing the behavior of the agent inside (red teaming).
• The Team Structure: The specific functions of Red (attackers), Blue (defenders), Purple (feedback loops), and White (governance) teams.
• Operational Scope: When to run pilot programs versus when to implement continuous automated assurance.

“You can think of it this way: penetration testing checks the locks, while red teaming tests the burglar alarms, the security staff, and the incident playbook at the same time.”

Read this post first if you need a baseline shared vocabulary for cross-functional teams.

Read: AI Red Teaming 101: What is AI Red Teaming?

AI Red Teaming Fundamentals

This section covers the core components that support disciplined practice. 

It includes threat surfaces across models, data pathways, prompts, multi-turn interactions, and connected systems. 

It describes common operational goals such as safety, security, privacy, fairness, and brand risk. 

It also outlines program structure, scoping, measurement, reporting, and governance patterns that enterprise teams rely on.

In this post, you will learn:

• The Lifecycle: A step-by-step framework covering Scoping, Data Generation, Execution, Observation, and Mitigation.
• Threat Surfaces: Strategies for testing Model Behavior, Retrieval Flows, and Tool/Action abuse.
• Key Metrics: How to calculate Attack Success Rate (ASR) and False Refusal Rate (FRR) to quantify security posture.
• Scenario Distinction: The strategic difference between Jailbreak testing (model focus) and Red Teaming (system focus).

“Bugs don’t always look like bugs; they often look like persuasive text or subtle nudges that steer a system off-policy.”

Move to this section if your organization is preparing a formal red teaming program or aligning engineering, product, and legal stakeholders.

Read: AI Red Teaming Fundamentals

Architecture and Automation

Manual adversarial testing cannot keep pace with the frequency of model updates and the complexity of agent behaviors.

To achieve continuous assurance, organizations must adopt automated architectures that can generate and manage adversarial inputs at scale.

This section examines the technical design of these systems, focusing on metaprompting as a framework for orchestrating intelligent testing workflows.

Metaprompting: The Architecture of Automated AI Red Teaming

This post examines the technical design of automated AI red teaming systems using metaprompting, which is the practice of using prompts to structure and manage other prompts. 

It explains how to move away from monolithic instructions toward a graph-based architecture where specialized nodes handle routing, generation, and evaluation, creating a robust framework for automated AI testing workflows.

In this post, you will learn:

• Metaprogramming Concepts: Moving from simple “Refiner” prompts to complex “Pathfinder” techniques.
• Graph Architecture: Why monolithic prompts fail and how to design hierarchical systems using specialized nodes (Router, Specialist, Composer).
• System Design: How to separate logic (what to do) from presentation (how to say it) to improve governance and debugging.

“Prompts are programs, and the prompts that write or manage other prompts are metaprograms… It allows us to impose structure, dictate workflows, and manage complexity in a way that a single, monolithic prompt never could.”

Use this post when you are designing or selecting an automated LLM-based red teaming system.

Read: Metaprompting: The Architecture of Automated AI Red Teaming

Case Study: Implementing Automated Red Teaming with Advanced Metaprompting

Applying these architectural concepts, this case study details the implementation of the RedSavant automated AI red teaming engine. 

It demonstrates a multi-step process that ingests business context to generate specific objectives, attack categories, and probing prompts dynamically, ensuring that testing is tailored to the specific domain risks of the target application.

In this post, you will learn:

• Context Ingestion: How to use “Reference Texts” to define sensitive data specific to a business domain (e.g., medical vs. financial).
• The Factory Model: Using a “Probe Director” and “Example Generator” to mass-produce diverse, nuanced probing prompts.
• Objective Mapping: How to translate high-level user directions into testable JSON objectives.

“A generic red-teaming approach is ineffective because risk is not one-size-fits-all… Context is everything.”

Read this post if you want to see how automated red teaming workflows operate in production-grade environments.

Read: Case Study: Implementing Automated Red Teaming with Advanced Metaprompting

Advanced Attack Vectors

Advanced agents with tool access present unique security challenges that require specific testing methodologies.

Simple safeguards often fail against sophisticated methods that exploit the fundamental nature of instruction tuning.

This section analyzes specific, high-risk vulnerabilities, detailing the mechanics of different attacks and the strategies required to secure autonomous agents against manipulation.

How to Red Team Prompt Injection Attacks on Multi-Modal LLM Agents

This guide focuses on prompt injection, the critical vulnerability where untrusted input overrides system instructions. 

It analyzes the mechanics of direct, indirect, and multi-turn attacks, specifically within the context of agents that can execute code or access external data. 

The post provides strategies for inducing systemic failures and evaluating the impact of successful injections on multi-modal systems.

In this post, you will learn:

• Injection Taxonomy: The mechanics of Direct, Indirect, and Multi-turn injections.
• Agent Vulnerabilities: How attackers exploit memory manipulation and tool-use to force unauthorized actions.
• Advanced Tactics: Using payload encoding, rhetorical framing, and forced hallucination to bypass safety filters.
• Impact Assessment: Strategies for measuring the severity of successful injections, from policy violations to data exfiltration.

“The model ‘sees’ both the developer’s instructions and the attacker’s instructions and often cannot reliably prioritize one over the other… The prompt is the only guiding context.”

Refer to this post when assessing agents that receive or generate multi-modal content or when integrating tool-calling models into operations.

Read: How to Red Team Prompt Injection Attacks on Multi-Modal LLM Agents