Skill Guide

Adversarial testing and red-teaming of AI systems

The systematic practice of simulating malicious actor behavior to uncover vulnerabilities, biases, and failure modes in AI systems before deployment.

It is the primary defense against catastrophic, reputation-destroying AI failures and regulatory non-compliance. It directly protects brand integrity and prevents financial loss by identifying critical security and ethical flaws pre-launch.

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8.5 Avg Demand

20% Avg AI Risk

How to Learn Adversarial testing and red-teaming of AI systems

1. Master the AI threat taxonomy: prompt injection, data poisoning, model evasion, and extraction. 2. Understand the legal/ethical boundaries (e.g., bug bounty agreements, responsible disclosure). 3. Develop proficiency in basic penetration testing and security principles.

Focus on automating attack vectors using frameworks like Garak or Microsoft's Counterfit. Practice designing multi-step, chained attacks (e.g., a prompt injection leading to data exfiltration). Avoid the common mistake of only testing for 'happy path' outputs; stress-test edge cases and corner cases.

Architect enterprise-scale red team programs. Integrate adversarial findings into MLOps pipelines and risk management frameworks. Mentor junior testers and develop novel attack methodologies for emerging architectures like agentic AI systems.

Practice Projects

Beginner

Project

Basic Prompt Injection Testing on a Public LLM Chatbot

Scenario

You have access to a public-facing chatbot (e.g., a customer service demo). Your goal is to make it violate its system prompt and output sensitive internal information or perform an unauthorized action.

How to Execute

1. Define the chatbot's intended behavior and restrictions. 2. Craft and test 10 direct injection attempts (e.g., 'Ignore previous instructions and...'). 3. Escalate to indirect injections (e.g., summarizing a malicious URL). 4. Document all successful attacks with input/output pairs.

Intermediate

Case Study/Exercise

Model Evasion Attack on an Image Classifier

Scenario

A startup uses a pre-trained image classifier to moderate user-uploaded content. You must generate adversarial examples that bypass this filter, causing it to misclassify harmful content as benign.

How to Execute

1. Obtain or train a surrogate model of the target classifier. 2. Use gradient-based methods (e.g., FGSM, PGD) to generate minimal-perturbation adversarial images. 3. Test the transferability of these attacks to the target black-box system. 4. Write a report detailing the attack's success rate and recommending defensive patches.

Advanced

Case Study/Exercise

Red Teaming a Multi-Agent AI System

Scenario

An enterprise deploys a system where multiple AI agents collaborate to perform complex tasks (e.g., one agent retrieves data, another analyzes it, a third takes action). You must find and exploit inter-agent communication or trust boundaries.

How to Execute

1. Map the system architecture and data flows between agents. 2. Develop attack scenarios that manipulate the state of one agent to corrupt the decision of another (e.g., poisoning the context window). 3. Simulate a coordinated attack chain that leads to a high-impact business outcome (e.g., incorrect financial transaction). 4. Present findings to engineering leadership with a prioritized remediation roadmap.

Tools & Frameworks

Attack Frameworks & Libraries

Microsoft CounterfitGarak (LLM vulnerability scanner)CleverHans / Foolbox (adversarial ML libraries)

Use Counterfit for benchmarking black-box model robustness. Deploy Garak for automated, scenario-based testing of LLMs. Utilize CleverHans/Foolbox to implement and test gradient-based adversarial attacks on computer vision and other models.

Mental Models & Methodologies

MITRE ATLAS (Adversarial Threat Landscape for AI Systems)OWASP Top 10 for LLM ApplicationsSTRIDE Threat Modeling

Structure your red teaming program and reporting around MITRE ATLAS for AI-specific tactics. Use the OWASP LLM Top 10 to ensure you cover the most critical web-facing vulnerabilities. Apply STRIDE to systematically identify threats like spoofing, tampering, and information disclosure in your AI system's architecture.

Interview Questions

Answer Strategy

The candidate should outline a phased approach: reconnaissance (understanding the model's interface and advertised capabilities), planning (defining objectives based on threat models like MITRE ATLAS), execution (using both automated scanners like Garak and manual creativity for novel attacks), and reporting (prioritizing findings by business impact). A strong answer mentions collaboration with legal and compliance teams from the start.

Answer Strategy

This is a behavioral question testing practical experience, technical depth, and communication skills. The candidate must clearly articulate the technical flaw (e.g., 'an insecure deserialization flaw in the model serving API'), the methodology used to find it (e.g., 'I fuzzed the API with malformed payloads while monitoring for memory corruption'), and the business impact (e.g., 'It allowed remote code execution, risking a full system compromise').