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Adversarial testing methods that probe AI systems for weaknesses: jailbreaks, attack vectors, stress-testing techniques, and how to build a red teaming practice that catches failures before users do.
Most red-teaming targets the model. The bigger gap is testing the system — its purpose, capabilities, boundaries. A walkthrough of how we generate product-aware adversarial prompts, with a healthcare-assistant example showing how a roleplay strategy bypasses guardrails.
How we built our red-teaming dataset: collecting adversarial prompts from public sources, cleaning and standardising them, and using K-means clustering on sentence embeddings to surface six categories of attack. Includes a curated open-source subset on Hugging Face.
