AI Red Teaming and Adversarial Testing

Introduction

AI Red Teaming and Adversarial Testing sits inside the AI Risk Management for Security domain of the ISACA Advanced in AI Security Management (AAISM) body of knowledge. It is one of the most practical topics in the certification because it bridges the gap between abstract AI risk statements and concrete evidence about how an AI system actually behaves when someone tries to break it. This guide explains why the topic matters, what red teaming and adversarial testing are, how a mature program works end to end, and how to reason through exam questions on the subject.

Why AI Red Teaming and Adversarial Testing Is Important

Traditional software is deterministic: given the same input, it produces the same output, and its logic can be read and reviewed. AI systems, especially machine learning and generative models, are probabilistic, data-driven, and often opaque. Their failure modes are not visible in source code because the behaviour is learned from data rather than written by a developer. This creates several security management problems:

1. Unknown attack surface. A model can be manipulated through its inputs (prompts, images, sensor data), through its training data, through its supply chain (pre-trained weights, plug-ins, retrieval sources), and through its outputs (data leakage, harmful content). Static reviews cannot reveal these weaknesses.
2. Regulatory and framework expectations. The NIST AI Risk Management Framework (Measure function), the EU AI Act (testing obligations for high-risk and general-purpose AI models with systemic risk), ISO/IEC 42001, and the US Executive Order on AI all explicitly reference red teaming or adversarial evaluation as a control. An AI security manager is expected to demonstrate that models have been tested against realistic adversaries before and after deployment.
3. Risk-based assurance. Red teaming converts theoretical risks on a register into validated findings with likelihood and impact evidence, allowing management to prioritise remediation and make defensible residual-risk decisions.
4. Trust and accountability. Boards, customers, and regulators increasingly ask not whether an AI system is secure, but how the organisation knows it is secure. Adversarial testing provides the answer.

For the AAISM exam, remember the guiding principle: you cannot manage the risk of a system whose adversarial behaviour you have never observed.

What AI Red Teaming and Adversarial Testing Are

AI Red Teaming is a structured, goal-oriented exercise in which a team adopts the mindset, tools, and techniques of a realistic adversary to discover vulnerabilities, harmful behaviours, and unintended capabilities in an AI system. It is broader than security alone: AI red teams also probe for bias, toxicity, misinformation, privacy leakage, and safety failures. The scope covers the whole system, including the model, the data pipelines, the orchestration layer, APIs, plug-ins, and the human processes around it.

Adversarial Testing is the more technical subset focused on deliberately crafted inputs or conditions designed to make a model misbehave. It includes adversarial examples (small perturbations that cause misclassification), prompt-based attacks, and stress testing of model robustness. Adversarial testing can be highly automated and repeated continuously, whereas red teaming is typically campaign-based and relies heavily on human creativity.

How this differs from traditional penetration testing. Penetration testing targets known classes of software and infrastructure flaws such as injection, misconfiguration, and privilege escalation. AI red teaming adds model-specific threats that have no equivalent in classical applications: a system may be fully patched and still produce a jailbroken, data-leaking, or discriminatory output. Exam questions frequently test whether you understand that a clean penetration test does not provide assurance over model behaviour.

The Major Adversarial Threat Categories

The MITRE ATLAS framework and the OWASP Top 10 for Large Language Model Applications are the two reference taxonomies most often cited. You should be able to recognise and classify the following:

1. Prompt Injection - Malicious instructions embedded in user input (direct) or in content the model retrieves or processes, such as web pages, documents, or emails (indirect). The goal is to override system instructions, exfiltrate data, or trigger unauthorised actions through connected tools.
2. Jailbreaking - Techniques that bypass safety guardrails to produce prohibited content, including role-play framing, encoding tricks, multi-turn manipulation, and many-shot attacks.
3. Data Poisoning - Corrupting training, fine-tuning, or retrieval data so the model learns a backdoor, bias, or degraded behaviour. This is a supply-chain and integrity attack.
4. Evasion and Adversarial Examples - Inputs crafted to be misclassified, such as perturbed images that defeat a vision model or obfuscated text that evades a content filter.
5. Model Extraction and Theft - Repeated queries used to reconstruct a functionally equivalent copy of a proprietary model, stealing intellectual property and bypassing licensing.
6. Model Inversion and Membership Inference - Attacks that recover sensitive training data or determine whether a specific individual's record was used in training, creating privacy and regulatory exposure.
7. Sensitive Information Disclosure - The model revealing secrets, system prompts, personal data, or confidential documents through its outputs.
8. Excessive Agency and Insecure Tool Use - Agentic systems with plug-ins or function calling that can be manipulated into taking harmful real-world actions.
9. Denial of Service and Resource Abuse - Inputs designed to exhaust tokens, compute, or budget, including sponge examples.
10. Supply Chain Compromise - Tampered pre-trained weights, malicious model files, compromised libraries, or untrusted third-party model APIs.

How an AI Red Teaming Program Works

A mature program follows a lifecycle that an AI security manager is expected to govern. Memorise the phases and what each produces.

Phase 1: Define Objectives and Scope. Identify which AI systems are in scope based on risk tier, criticality, data sensitivity, and regulatory classification. Define what harms matter: security breaches, safety violations, privacy leakage, bias, reputational damage. Establish rules of engagement, legal authorisation, and data handling constraints. High-risk systems and externally facing generative AI are prioritised.

Phase 2: Threat Modelling. Use frameworks such as MITRE ATLAS, STRIDE adapted for AI, or OWASP LLM Top 10 to enumerate plausible adversaries, their capabilities, and their goals. Map assets (training data, weights, prompts, APIs, tools) to threats. Threat modelling ensures the red team tests what matters rather than what is merely interesting.

Phase 3: Team Composition. Effective AI red teams are multidisciplinary: security engineers, ML specialists, domain experts, ethicists or responsible-AI specialists, and sometimes external participants to bring diversity of perspective. Independence from the development team is a key governance principle to avoid bias and conflicts of interest.

Phase 4: Execution. Testing combines manual exploration with automated tooling. Manual testing uncovers novel, creative attacks; automated adversarial testing (fuzzing prompts, generating adversarial examples, running benchmark attack suites, and using AI models to attack other models) provides breadth and repeatability. Testing should cover the entire system, not only the model in isolation, because many failures arise in the integration layer such as retrieval pipelines, tool connectors, and output handling.

Phase 5: Analysis and Risk Rating. Findings are documented with reproduction steps, evidence, affected components, likelihood, impact, and alignment to the risk register. Severity is rated using the organisation's standard risk methodology so AI findings can be compared with other enterprise risks.

Phase 6: Remediation and Mitigation. Typical controls include input and output filtering, improved system prompts and guardrails, safety fine-tuning or reinforcement learning from human feedback, adversarial training to improve robustness, rate limiting and query monitoring to counter extraction, least-privilege design for agent tools, data provenance controls for poisoning, and architectural changes such as human-in-the-loop approval for high-impact actions.

Phase 7: Retesting and Continuous Assurance. Models change through retraining, fine-tuning, vendor updates, and drift. Red teaming is therefore not a one-time gate but a recurring activity integrated into the AI lifecycle, triggered by model changes, new threat intelligence, incidents, or regulatory updates. Results feed metrics, management reporting, and the continuous improvement cycle required by ISO/IEC 42001 and the NIST AI RMF Manage function.

Governance Considerations for the AI Security Manager

AAISM is a management-level certification, so questions emphasise oversight rather than hands-on exploitation. Key governance points:

- Policy and standards: Formal requirements specifying when red teaming is mandatory (for example, before deployment of high-risk or customer-facing AI, after material model changes, and on a periodic schedule).
- Third-party and foundation models: Organisations rarely train their own large models. Managers must obtain and evaluate vendor red teaming evidence, model cards, system cards, and contractual rights to test, and must still test the integrated application they deploy.
- Metrics: Attack success rate, time to remediate, coverage of threat categories, percentage of in-scope systems tested, and recurrence of previously fixed issues.
- Reporting: Results communicated to risk committees and the board in business terms, with residual risk accepted by the appropriate owner.
- Ethics and safety of testing: Red team activities must be authorised, must protect real user data, must avoid generating or storing harmful content inappropriately, and must be conducted under legal review.
- Integration with incident response: Red team findings should inform detection rules, monitoring, and playbooks for AI-specific incidents.

Exam Tips: Answering Questions on AI Red Teaming and Adversarial Testing

1. Think like a manager, not an attacker. When a question asks for the BEST or MOST important action, the answer usually involves governance: defining scope based on risk, ensuring independence, aligning findings to the risk register, or obtaining management acceptance of residual risk. Highly technical exploitation details are rarely the correct answer at this level.

2. Distinguish red teaming from penetration testing. If a scenario states that a penetration test found no issues but the question concerns model behaviour such as hallucinated advice, biased outputs, or jailbreaks, the correct response is that traditional testing does not cover model-specific risks and AI red teaming is required.

3. Know the attack taxonomy cold. Expect scenario questions describing an attack and asking you to name it. Instructions hidden in a retrieved document equals indirect prompt injection. Recovering training records equals model inversion. Determining whether a person was in the training set equals membership inference. Reconstructing a model via queries equals model extraction. Corrupted training data equals poisoning. Misclassified perturbed input equals evasion or adversarial example.

4. Match the control to the threat. Rate limiting and query monitoring counter extraction. Data provenance and validation counter poisoning. Adversarial training improves robustness against evasion. Output filtering and least-privilege tool design counter injection and excessive agency. Differential privacy counters inversion and membership inference. Wrong-control distractors are common.

5. Remember the lifecycle and timing. Red teaming is most valuable before deployment and must be repeated after significant changes. If a question asks when to perform it, prefer answers indicating risk-based, recurring, and change-triggered testing over one-time or annual-only options.

6. Prioritise by risk. When resources are limited, the correct answer directs red teaming toward high-risk, externally exposed, or regulated AI systems first, consistent with a risk-based approach.

7. Independence and diversity matter. Answers that place sole testing responsibility with the development team, or that rely on a single discipline, are usually wrong. Look for options emphasising independent, multidisciplinary teams.

8. Third-party models still require your testing. If a scenario involves a vendor foundation model, the correct answer typically requires obtaining vendor assurance evidence AND testing the organisation's own integrated application, because the deployment context introduces new risks.

9. Findings must drive action. Questions about what to do after red teaming point toward documenting findings in the risk register, assigning owners, remediating based on severity, retesting, and reporting to governance bodies. An exercise with no follow-through provides no assurance.

10. Watch for framework references. Recognise MITRE ATLAS as the adversarial tactics and techniques knowledge base for AI, OWASP Top 10 for LLM Applications as the vulnerability list for generative AI apps, NIST AI RMF Measure and Manage functions as the risk framework context, ISO/IEC 42001 for AI management systems, and the EU AI Act for legal testing obligations.

11. Eliminate absolutes. Options stating that red teaming guarantees security, eliminates all risk, or can be fully automated are distractors. Red teaming reduces uncertainty and informs risk decisions; it does not provide certainty.

Worked Example

Question: An organisation is preparing to launch a customer-facing generative AI assistant that retrieves information from internal knowledge bases and can create support tickets on behalf of users. The development team reports that all infrastructure penetration tests passed. What should the AI security manager do FIRST?

Reasoning: The scenario involves retrieval (indirect prompt injection risk), tool execution (excessive agency risk), and external exposure. Infrastructure penetration testing does not address these model-specific threats. The manager-level first step is to ensure the system is in scope for independent AI red teaming based on its risk profile before launch, with findings fed into the risk register and remediation plan. Options such as approving launch based on the penetration test, relying solely on the vendor model card, or asking developers to self-test would be incorrect.

Summary

AI Red Teaming and Adversarial Testing is how an organisation obtains evidence about the real-world adversarial behaviour of its AI systems. It extends beyond penetration testing to cover prompt injection, jailbreaking, poisoning, evasion, extraction, inversion, data leakage, and agentic misuse. A mature program is risk-scoped, threat-model driven, independent, multidisciplinary, combines manual and automated methods, rates and remediates findings through the enterprise risk process, and repeats continuously as models change. For the AAISM exam, anchor every answer in risk-based governance, correct threat-to-control matching, lifecycle integration, and the principle that assurance over AI requires testing the AI itself, not just the infrastructure around it.