Skill Guide

AI red teaming - systematic evaluation of model robustness using frameworks like Microsoft PyRIT, Garak, and Anthropic's red-team tooling

AI red teaming is the adversarial testing of AI systems, using structured frameworks and attack simulations, to proactively identify and mitigate security, safety, and reliability vulnerabilities before deployment.

This skill is critical for mitigating catastrophic reputational, legal, and financial risks by uncovering exploitable failures in model behavior that standard testing misses. It directly impacts business outcomes by safeguarding brand trust, ensuring regulatory compliance, and preventing costly incidents.

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How to Learn AI red teaming - systematic evaluation of model robustness using frameworks like Microsoft PyRIT, Garak, and Anthropic's red-team tooling

Foundational concepts, terms, or basic habits to build first: 1. **Core Threat Taxonomy**: Learn the OWASP Top 10 for LLMs (e.g., prompt injection, sensitive info disclosure). 2. **Basic Tool Proficiency**: Install and run simple scans with Garak (a Python-based LLM vulnerability scanner). 3. **Attack Anatomy**: Understand the components of an attack: objective, method, payload, and measurement of success.

How to move from theory to practice: Focus on **orchestrated multi-turn attacks** using PyRIT (Python Risk Identification Toolkit). Practice building red team scenarios that combine jailbreaking, persuasion, and context manipulation. **Common mistake**: Treating red teaming as a one-off penetration test instead of an integrated, continuous evaluation pipeline.

How to master the skill at an executive, lead, or architect level: Focus on **designing and scaling red team programs**. This includes creating organizational threat models, building custom attack generators and scorers, integrating red team metrics into MLOps pipelines, and developing mitigation playbooks. Lead cross-functional exercises with legal, policy, and PR teams.

Practice Projects

Beginner

Project

First Vulnerability Scan with Garak

Scenario

You are given access to a simple, hosted chatbot model (e.g., a Hugging Face Inference API endpoint). Your task is to perform an initial automated vulnerability scan.

How to Execute

1. Install Garak (`pip install garak`). 2. Configure the `garak` YAML to point to your target model's API. 3. Run the `garak` CLI with the `--probes promptinject` flag to test for basic prompt injection. 4. Analyze the generated report for high-severity findings.

Intermediate

Project

Orchestrated Multi-Turn Jailbreak with PyRIT

Scenario

A model has a known safety policy against generating malicious code. Your goal is to use PyRIT's orchestrator to bypass this safety filter over multiple conversational turns.

How to Execute

1. Set up a PyRIT `RedTeamingBot` as the attacker and the target model. 2. Configure a `MultiTurnOrchestrator` with a high-level objective (e.g., 'generate a ransomware script'). 3. Define a custom `Scorer` (e.g., a regex or LLM-based judge) to detect if the objective was met. 4. Execute the orchestration loop and analyze the conversation for the point of failure.

Advanced

Project

Enterprise Red Team Program Design & Execution

Scenario

As the lead AI security engineer, you are tasked with standing up a repeatable, quarterly red team assessment program for your company's flagship customer-facing LLM agent.

How to Execute

1. **Threat Modeling**: Define the attack surface and priority threat scenarios with stakeholders. 2. **Infrastructure**: Build a scalable testing harness using PyRIT to run hundreds of attack scenarios in parallel. 3. **Custom Toolkit Development**: Create company-specific attack generators and toxicity/factuality scorers. 4. **Reporting & Integration**: Develop a standardized risk report and integrate key metrics (e.g., Attack Success Rate) into the CI/CD pipeline.

Tools & Frameworks

Adversarial Testing Frameworks

Microsoft PyRIT (Python Risk Identification Toolkit)Garak (LLM vulnerability scanner)Anthropic's Red-Teaming Tooling (PyRIT-compatible components)

PyRIT is for complex, multi-turn, interactive adversarial dialogues. Garak is for broad, automated vulnerability scanning against a taxonomy of known flaws. Anthropic's tools provide specialized components for testing alignment and helpfulness.

Complementary & Foundational Tools

LangSmith / LangFuse (Tracing & Eval)Giskard (ML Monitoring & Scanning)Hugging Face `transformers` & `safetensors`

Tracing tools are essential for logging and debugging red team interactions. Giskard provides scanning and monitoring capabilities. Hugging Face libraries are for loading models and tokenizers for local, offline testing.

Interview Questions

Answer Strategy

The candidate must demonstrate a structured approach. **Strategy**: Use a threat model (e.g., STRIDE) to frame the answer. Focus on access control bypass (indirect prompt injection to exfiltrate data) and confidentiality breaches. **Sample Answer**: 'I'd prioritize indirect prompt injection leading to data exfiltration and unauthorized internal tool use. I'd start with Garak for a broad scan of known injection patterns, then use PyRIT to simulate a malicious user attempting to manipulate the agent to dump document snippets over multiple turns. Success would be measured by a custom scorer detecting if sensitive, non-public data appeared in the output.'

Answer Strategy

Tests deep technical implementation skill. The answer should cover both rule-based and LLM-based judging. **Sample Answer**: 'I'd design a two-layer scoring system. First, a regex-based scorer for explicit banned keywords. Second, and more importantly, an LLM-as-a-judge scorer where I prompt a separate, highly-capable model with the conversation history and a rubric asking it to evaluate the model's response for safety violations, considering coded language and context. The final score would be a weighted combination, with the LLM judge having the primary weight.'