Generate a comprehensive red team exercise plan for your AI system. Select your system type, choose attack categories, and get time-boxed exercises with risk ratings, step-by-step instructions, and finding documentation templates.
Different AI systems have different attack surfaces. The exercise plan is tailored to your specific system type.
AI red teaming has evolved from an informal practice to a regulatory requirement. The White House Executive Order on AI (October 2023) mandated red teaming for frontier AI models before deployment. The EU AI Act, which enters full enforcement in August 2026, requires high-risk AI systems to undergo ongoing risk assessment that includes adversarial testing. NIST's AI Risk Management Framework (AI RMF 1.0) explicitly recommends red teaming as a core practice in its "Test" function. Organizations deploying AI systems now face a choice between proactive red teaming and reactive incident response when vulnerabilities are exploited in production.
Unlike traditional cybersecurity red teaming which focuses on network penetration, privilege escalation, and data exfiltration, AI red teaming targets attack surfaces unique to machine learning: the training data pipeline, the model weights, the inference API, the output filtering, and the feedback loops that connect user interactions back to model improvement. A skilled AI red team can extract training data from a language model, cause an image classifier to misidentify stop signs as speed limit signs, manipulate a recommender system to promote specific content, or trick a fraud detection model into approving fraudulent transactions. Each of these attacks requires domain-specific expertise that traditional penetration testers may not possess.
For LLM-based systems, prompt injection is the highest-priority attack category. Direct prompt injection provides malicious instructions in the user input that override the system prompt. Indirect prompt injection embeds attack payloads in data the LLM processes (web pages, documents, emails) that execute when the LLM reads that data. System prompt extraction tricks the LLM into revealing its configuration instructions, which often contain business logic, access patterns, and security policies. Jailbreaking uses social engineering techniques, role-playing prompts, or encoding tricks to bypass safety filters. The red team exercise plan tests all four subcategories with escalating sophistication, from basic "ignore previous instructions" to multi-turn context manipulation and tool-use exploitation.
Data poisoning attacks corrupt the model at its source by manipulating training data. Backdoor injection plants trigger patterns that cause specific misclassifications when activated. Label flipping changes the labels of training examples to confuse the decision boundary. Clean-label poisoning uses correctly labeled but carefully selected examples that shift the model's learned features in an adversarial direction. For LLMs, poisoning attacks can insert persistent biases, create trigger phrases that produce predetermined outputs, or embed instructions that survive fine-tuning. Red team exercises for data poisoning involve attempting to modify data pipelines, inject data through public data sources that feed training, and verify that data validation controls detect manipulation.
Model extraction attacks steal the behavior of a proprietary model by querying it systematically and training a surrogate model on the responses. The stolen model can then be used without paying API fees, analyzed for vulnerabilities offline, or deployed by competitors. Red team exercises test the effectiveness of rate limiting, output perturbation, query pattern detection, and model watermarking. For API-served models, the exercise includes estimating how many queries are needed to achieve a given fidelity of extraction, testing whether rate limits can be bypassed through distributed querying, and verifying that watermarks survive the extraction process.
Bias testing is a distinct form of red teaming that tests whether the AI system produces discriminatory outputs across protected groups (race, gender, age, disability, religion). This includes testing input variations that change only the protected attribute (e.g., changing a name from "James" to "Jamal" in a resume screener), testing whether the model's error rates differ across demographic groups, and checking whether the model amplifies or perpetuates stereotypes in its outputs. For LLMs, bias red teaming includes testing whether the model generates different quality or tone of responses based on implied demographics, refuses certain requests for some groups but not others, or associates protected groups with negative attributes. Bias findings map directly to anti-discrimination regulations and can create significant legal liability.
Start with a focused scope: pick the top two or three attack categories most relevant to your system and time-box each exercise to two to four hours. Document findings immediately using the structured template (vulnerability, reproduction steps, severity, affected component, recommended mitigation). After the first exercise, review findings with the development team and prioritize mitigations. Schedule red team exercises quarterly, or on every major model update. Track the number of findings per category over time to measure your security posture improvement. The exercise builder above generates a tailored plan with specific exercises, time estimates, and documentation templates for your system type and selected attack categories.
Red teaming is only valuable if findings lead to mitigations. For each finding, assign a severity based on exploitability (how easy is the attack), impact (what damage results), and scope (how many users or data records are affected). Critical findings (easy to exploit, high impact, wide scope) should block deployment or trigger immediate remediation. High findings should be fixed before the next scheduled release. Medium findings should be addressed within the quarter. Low findings should be tracked and addressed as part of ongoing security improvement. The exercise plan export includes a severity framework and mitigation tracking template that maps directly to the finding documentation, creating an end-to-end workflow from discovery to resolution.
AI red teaming systematically tests AI systems for vulnerabilities, biases, and failure modes. It covers unique attack surfaces: prompt injection, data poisoning, model extraction, and adversarial examples. Required by the White House Executive Order on AI and the EU AI Act for high-risk systems.
Prompt Injection, Data Poisoning, Model Extraction, Adversarial Inputs, Privacy Attacks, Bias and Fairness, Infrastructure Security, and Compliance Testing. Each covers different attack surfaces and requires different expertise.
Single-category: 2-4 hours. Top-4 categories: 1-2 days. Full 8-category engagement: 3-5 days per system. Time-box each exercise to prevent scope creep and document findings continuously.
Combine ML security expertise, domain knowledge, and traditional security skills. Include an ML engineer, security researcher, domain expert, compliance specialist, and penetration tester. For LLMs, include diverse perspectives for bias testing.
Prioritize by attack surface exposure (external-facing first), data sensitivity (PII-processing first), and regulatory timeline (EU AI Act deadline). Within each system, start with prompt injection and adversarial inputs as the most commonly exploited vectors.