Skill Guide

Red-teaming methodologies for generative AI systems

Red-teaming for generative AI is a structured adversarial testing methodology designed to proactively discover and document model failures, safety violations, and harmful outputs before deployment.

This skill is highly valued as it directly mitigates legal liability, reputational damage, and regulatory non-compliance by identifying model weaknesses that standard testing misses. It enables organizations to deploy AI systems with quantifiable safety margins, which is critical for maintaining user trust and avoiding costly product recalls or public incidents.

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15% Avg AI Risk

How to Learn Red-teaming methodologies for generative AI systems

Foundational concepts to build first: 1) **Taxonomy of Harm**: Learn the core categories-bias, toxicity, misinformation, privacy leakage, and jailbreaking. 2) **Prompt Crafting Basics**: Master techniques like role-playing, context manipulation, and indirect questioning to elicit unintended responses. 3) **Documentation Standards**: Understand how to log findings with prompt, output, harm category, and severity rating.

Moving from theory to practice involves: 1) **Scenario-Based Testing**: Systematically test for specific, high-risk use cases like financial advice or medical guidance. 2) **Automation Integration**: Use scripting to scale attack vectors and analyze outputs. 3) **Common Mistake**: Avoid tunnel vision on prompt injection; equally test for passive failures like hallucination in grounded tasks or bias amplification in outputs.

Mastery at the architect level requires: 1) **Threat Modeling for AI Systems**: Map the entire attack surface of a production pipeline (e.g., RAG, fine-tuning endpoints). 2) **Strategic Alignment**: Translate red-team findings into actionable risk registers and engineering requirements for the ML and product teams. 3) **Mentoring and Process Design**: Develop and institutionalize red-teaming playbooks, scoring rubrics, and escalation protocols.

Practice Projects

Beginner

Project

Basic Jailbreak and Safety Boundary Audit

Scenario

You are given access to a public-facing chatbot API. Your goal is to determine if it will generate content violating its stated safety policies (e.g., creating harmful code, adult content).

How to Execute

1. **Define Scope**: List 3-5 specific prohibited content types from the model's guidelines. 2. **Develop Attack Vectors**: For each type, craft 10 distinct prompts using direct requests, fictional scenarios, and role-play (e.g., 'You are a screenwriter...'). 3. **Execute and Log**: Run prompts against the API, logging every prompt-response pair. 4. **Analyze & Report**: Classify each failure, calculate a basic success rate for the model, and write a summary with the most effective attack vector.

Intermediate

Project

Domain-Specific Hallucination Stress Test

Scenario

Your team has fine-tuned a model to answer questions about internal corporate financial documents. You must test its reliability and tendency to fabricate information (hallucinate).

How to Execute

1. **Build a Ground Truth Set**: Create a set of 50 questions with answers verifiably present in the source documents and 20 questions where the answer is absent. 2. **Design Probes**: Include questions requiring synthesis across multiple documents, ambiguous phrasing, and questions that sound plausible but are unanswerable. 3. **Run Batch Inference**: Use a script to run all questions through the model. 4. **Evaluate & Metric**: Use a combination of automated checks (for source presence) and human review to score hallucination rates. Calculate and report metrics like Faithfulness Score.

Advanced

Case Study/Exercise

Orchestrated Multi-Turn Attack on a Retrieval-Augmented Generation (RAG) System

Scenario

A financial advisory RAG system cites SEC filings. An adversary attempts to make it recommend a specific stock by poisoning its responses over multiple turns, exploiting the system's context window and retrieval logic.

How to Execute

1. **Threat Model**: Map the attack surface: the vector DB, the retriever, the LLM context. 2. **Design the Campaign**: Script a multi-turn conversation that first establishes credibility (asking safe questions), then gradually introduces biased context or leading questions to 'steer' the LLM's synthesis. 3. **Execute and Monitor**: Run the scripted attack, monitoring which documents are retrieved and how the final answer is composed. 4. **Develop Countermeasures**: Propose mitigations such as input sanitization, context length limits for adversarial sequences, or retriever confidence thresholds.

Tools & Frameworks

Software & Platforms

PyRIT (Microsoft's Python Risk Identification Toolkit)Garak (LLM vulnerability scanner)LangSmith (for tracing and debugging RAG chains)Promptfoo (for automated evaluations and red-teaming)

Use PyRIT and Garak for structured, automated attack generation and vulnerability scanning against models. Use LangSmith to trace and analyze the internal decision logic of complex chains during red-team exercises. Use Promptfoo to define and run repeatable test suites against multiple model endpoints.

Mental Models & Methodologies

OWASP Top 10 for LLM ApplicationsNIST AI Risk Management Framework (AI RMF)STRIDE Threat Modeling (adapted for AI)MITRE ATLAS (Adversarial Threat Landscape for AI Systems)

Use OWASP Top 10 to ensure comprehensive coverage of common application-layer vulnerabilities. Use NIST AI RMF and MITRE ATLAS to align red-teaming with organizational risk governance and to catalog adversary tactics, techniques, and procedures. Adapt STRIDE to model threats like spoofing model identity or tampering with training data.

Reporting & Severity Frameworks

CVSS (Common Vulnerability Scoring System) - adapted for AIInternal Bug Bounty Tiers (Critical, High, Medium, Low)HARM Taxonomy (Hallucination, Abuse, Bias, Data Leakage, Malicious Use)

Use adapted CVSS or internal tiers to standardize severity assessment of findings, enabling prioritized engineering fixes. The HARM taxonomy provides a consistent language for categorizing and discussing failure modes across teams.

Interview Questions

Answer Strategy

The interviewer is testing structured problem-solving and threat modeling. Use a phased approach: 1) **Scoping**: Define prohibited outputs (e.g., real persons, copyrighted art styles, violent scenes) based on policy and law. 2) **Methodology**: Describe a mix of automated (using adversarial prompt libraries) and manual testing (creative artists and cultural experts probing edge cases). 3) **Execution**: Explain how you'd document failures with a consistent severity rubric. 4) **Reporting**: Emphasize translating findings into specific engineering tasks (e.g., 'strengthen NSFW filter for specific artist name triggers') and a risk assessment for legal/compliance.

Answer Strategy

This tests understanding of nuanced failures beyond simple block/allow. Categorize this as a **Circumvention via Indirect Prompting** and a failure of **Contextual Integrity**. The core competency is recognizing that safety filters can be brittle. Sample answer: 'I'd report this as a High-severity jailbreak. The failure is not in the refusal mechanism but in the model's inability to maintain its ethical stance within a different narrative frame. The fix likely requires alignment training to recognize harmful themes across all output formats, not just direct Q&A. I'd recommend a dedicated test suite for fictional and role-play scenarios.'