Sentinel AI — LLM Red Teaming Framework for AI Safety
A human-centric AI safety system that stress-tests large language models with adversarial attacks, alignment checks, and safety mechanisms before they reach users.
⚠ The Challenge
Most AI systems are tested for what they should do — almost none are systematically tested for what they should never do. Teams ship LLM features without knowing how they respond to prompt injection, jailbreaks, role-play exploits, or data-extraction attempts, and discover the failures in production.
⚙ The Approach
I designed Sentinel AI as a structured red-teaming pipeline in Python: a library of attack strategies (prompt injection, jailbreak patterns, obfuscation, multi-turn manipulation), automated execution against target models, and scoring of responses for safety violations and alignment drift. The framework treats safety evaluation like software testing — repeatable suites instead of one-off manual poking.
✓ The Outcome
A reusable framework that surfaces concrete, reproducible failure cases in LLM systems before launch. The techniques behind it now inform every LLM feature I build for clients — guardrails, output validation, and adversarial pre-launch testing come standard.