When AI Enables Harm
Red-teaming research across major AI platforms and the real-world safety failures it uncovered.
Safety filters fail most often at the edges: ambiguous intent, multi-turn escalation, roleplay, translation, emotional framing, and prompts that blend legitimate support with harmful operational detail.
Testing those edges requires more than a checklist. It requires adversarial evaluation that reflects how people actually interact with AI systems and how attackers adapt when direct requests are blocked.
The goal is not to produce a theatrical jailbreak score. The goal is to make risk visible, prioritize concrete fixes, and ensure safety mechanisms hold up under pressure.