You wouldn’t deploy a web application without a penetration test. So why are organisations deploying AI systems without adversarial testing?
The answer is usually some combination of “we didn’t know it was possible,” “our security team doesn’t cover AI,” and “the model works fine in testing.” These are the same arguments people made about web application security in 2005 — and we know how that turned out.
AI red teaming is no longer an academic exercise. It’s a practical necessity for any organisation deploying machine learning models, large language models, or AI-powered applications in production.
What AI Red Teaming Actually Means
Traditional red teaming simulates a motivated attacker to test an organisation’s defences. AI red teaming applies the same principle to machine learning systems — but the attack surface is fundamentally different.
When you red team an AI system, you’re testing for:
Adversarial inputs — carefully crafted inputs designed to fool your model. An image classifier that’s 99% accurate on your test set might completely fail on an adversarial example that looks identical to a human. An LLM that follows its system prompt perfectly in testing might leak its entire context window when presented with a well-crafted prompt injection.
Data poisoning — attacks on the training pipeline itself. If an attacker can influence the data your model trains on — even slightly — they can introduce backdoors that activate on specific trigger patterns.
Model extraction — reconstructing your proprietary model by making carefully chosen queries to its API. An attacker doesn’t need access to your infrastructure; they just need access to your prediction endpoint.
Membership inference — determining whether specific data was used in training. This has serious privacy implications, particularly under GDPR and the EU AI Act.
Why Traditional Security Testing Falls Short
Your penetration testing team is excellent at finding SQL injection, XSS, and authentication bypasses. But AI systems introduce attack vectors that sit outside the traditional web application security model.
Consider a fraud detection model. A traditional security test might check the API for authentication issues, rate limiting, and input validation. An AI red team would also test whether an attacker could craft transactions that systematically evade the model, whether they could poison the model’s retraining data, or whether the model leaks information about the training data distribution through its confidence scores.
Testing for adversarial robustness requires understanding model architectures, loss functions, and gradient-based attack methods. Testing for prompt injection requires understanding how LLMs process context, system prompts, and tool-use workflows. These are fundamentally different skills.
Real-World AI Attacks Are Happening Now
This isn’t theoretical. AI-specific attacks are happening in production systems today:
Researchers have demonstrated prompt injection attacks that cause LLM-powered applications to ignore their instructions, exfiltrate sensitive data, and execute unintended actions. Adversarial examples have been demonstrated against computer vision systems used in autonomous vehicles, medical imaging, and content moderation. Model extraction attacks have successfully replicated production ML models from major technology companies using nothing more than API access. Training data extraction from large language models has revealed memorised personal information, code, and copyrighted text.
The Regulatory Push
The EU AI Act requires providers of high-risk AI systems to perform testing, including adversarial testing, before deployment. Article 9 specifically mandates resilience against attempts by unauthorised third parties to exploit system vulnerabilities.
The NIST AI Risk Management Framework includes adversarial testing as a core component of AI risk management. The White House Executive Order on AI Safety directed NIST to establish guidelines for red-teaming of AI systems.
Organisations that establish AI red teaming capabilities now will be ahead of the compliance curve.
Getting Started
- Inventory your AI systems. Catalogue every model in production, including training data sources and access methods.
- Threat model each system. Focus on systems where compromise would have the greatest business impact.
- Start with your highest-risk system. Pick one system for an initial assessment.
- Engage specialists. AI red teaming requires ML expertise and offensive security skills. Consider working with a specialist consultancy for your first engagement.
- Build from findings. Use results to build detection capabilities. This is where blue team and purple team capabilities come in.
The Bottom Line
AI systems are powerful, valuable, and vulnerable. Red teaming your AI systems isn’t optional. It’s how you find the vulnerabilities before someone else does.
Ready to test your AI systems? Learn about our AI Red Team Services or get in touch to discuss your specific needs.
Want ongoing AI risk management? The LittleData.ai platform provides continuous risk scoring, compliance tracking, and governance dashboards.