Skill Guide

Red Teaming & Threat Modeling for AI

Red Teaming & Threat Modeling for AI is the structured practice of proactively identifying, assessing, and mitigating adversarial risks, failure modes, and unintended behaviors within AI systems by simulating attacker mindsets and mapping attack surfaces.

It is critical for ensuring AI system robustness, safety, and compliance, directly protecting organizations from reputational damage, financial loss, and regulatory penalties. This skill transforms AI development from a feature-first to a security-first paradigm, building user trust and enabling responsible deployment.

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10% Avg AI Risk

How to Learn Red Teaming & Threat Modeling for AI

Focus on 1) Core terminology: learn the difference between adversarial examples, data poisoning, model inversion, and prompt injection. 2) Study the OWASP Top 10 for LLMs as a foundational threat catalog. 3) Begin by manually testing public-facing chatbots with basic jailbreak prompts to understand failure modes firsthand.

Move to structured threat modeling using frameworks like STRIDE or PASTA, applying them to a sample ML pipeline. Practice writing formal red team reports that map found vulnerabilities to specific components (e.g., data ingestion, model inference API). Avoid the common mistake of focusing only on model-centric threats while ignoring infrastructure and data supply chain risks.

Master designing and orchestrating multi-phase, multi-vector red team engagements across the full AI lifecycle. Develop custom tooling and attack libraries. Align threat models with business risk appetite and regulatory frameworks (e.g., EU AI Act, NIST AI RMF). Mentor junior engineers and present risk narratives to executive leadership to drive resource allocation.

Practice Projects

Beginner

Project

Red Team a Public AI API Endpoint

Scenario

You are given a publicly accessible API for a sentiment analysis model. Your task is to probe it for vulnerabilities using standard adversarial techniques.

How to Execute

1. Use the 'TextAttack' library to generate adversarial text examples that fool the model. 2. Probe for input validation failures by sending malformed, excessively long, or empty payloads. 3. Document findings in a table with columns: Attack Vector, Payload, Observed Behavior, and Severity.

Intermediate

Project

Conduct a STRIDE-Based Threat Model for an ML Pipeline

Scenario

A team is deploying a real-time credit scoring model. The pipeline includes data ingestion from a cloud storage bucket, a training job on a managed platform, and a model served via a REST API.

How to Execute

1. Diagram the pipeline and data flows. 2. For each component, systematically apply STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege). 3. For each identified threat, define a concrete mitigation (e.g., implementing input data schema validation, adding authentication to the model endpoint). 4. Produce a prioritized risk register.

Advanced

Project

Design a Red Team Engagement for a Multi-Modal AI Agent

Scenario

An autonomous AI agent has access to internal company APIs (calendar, email, CRM) and can take actions based on user queries. It is a high-value target for business logic abuse and social engineering.

How to Execute

1. Map the agent's action space and data sources to create an attack surface diagram. 2. Design multi-step attack chains, such as using indirect prompt injection via a crafted email to exfiltrate CRM data. 3. Develop custom tooling to automate the generation of malicious payloads across modalities (text, image, audio). 4. Simulate insider threats by corrupting the agent's training data. 5. Deliver a full report with kill chain analysis and architectural redesign recommendations.

Tools & Frameworks

Red Teaming Toolkits & Frameworks

Microsoft CounterfitNVIDIA GarakTextAttackART (Adversarial Robustness Toolbox)

Used for executing automated and systematic adversarial attacks. Counterfit is a CLI for model vulnerability scanning. Garak is a probe-based LLM vulnerability scanner. Use these to operationalize and scale red teaming beyond manual testing.

Threat Modeling Methodologies

STRIDEPASTA (Process for Attack Simulation and Threat Analysis)LINDDUNOWASP AI Security Top 10

Structured frameworks for identifying and categorizing threats. STRIDE is a classic for general software, now adapted for ML components. PASTA is risk-centric, aligning threats to business objectives. LINDDUN is for privacy threat modeling. The OWASP Top 10 is the essential checklist for LLM-specific risks.

Monitoring & Defense

Weights & Biases ArtifactsGreat Expectations (for data validation)Adversarial Training Libraries

Used for implementing mitigations post-red teaming. Use W&B to track data lineage and model versions for reproducibility and audit. Great Expectations can enforce data contracts to prevent poisoning. Adversarial training libraries help harden models by incorporating found vulnerabilities into the training loop.

Interview Questions

Answer Strategy

Use a structured framework like STRIDE or PASTA, but immediately apply it to the context. Sample Answer: 'I'd start with a component-level analysis using STRIDE. For the user input vector, I'd focus on Prompt Injection and Denial of Service. For the model itself, I'd assess Information Disclosure via training data extraction and Elevation of Privilege if it can generate API calls. Crucially, I'd also model the downstream integration-could the model's output, if manipulated, trigger harmful actions in our ticketing system? I'd produce a risk matrix prioritizing threats by impact and likelihood.'

Answer Strategy

Tests adaptability, communication, and process improvement. The candidate should focus on their methodological response, not just the finding. Sample Answer: 'During a red team of a document summarization model, I discovered it could be tricked into reproducing large verbatim chunks of copyrighted text from its training data when given specific instructions-a data extraction attack. I documented the exact exploit chain, assessed the legal and reputational risk as high, and immediately briefed the security lead. We then implemented output filtering and a rigorous data provenance audit, and I updated our threat model to include 'Training Data Copyright Infringement' as a standard threat for all future generative AI projects.'