Red Teaming and Vulnerability Assessment for Distributed AI Systems
I served as a technical lead in a red-teaming initiative to stress-test an LLM’s understanding of secure system design. My role involved crafting complex adversarial prompts designed to expose flaws in how the model generates infrastructure-as-code (Terraform/Kubernetes). I evaluated model responses for security anti-patterns, such as hardcoded credentials, insecure API configurations, and lateral movement risks in distributed environments. By providing high-fidelity corrections and "gold-standard" secure code samples, I helped fine-tune the model to prioritize the principle of least privilege and zero-trust architecture in its output.