red teaming

Jailbreaking is the adversarial sport of steering a model around its own safety training. What matters is naming the technique families and treating defense as a layered, never-finished arms race, not a one-time patch.

Getting an LLM feature to production safely is an evaluation problem first and a model problem second. What lands is a layered eval-plus-guardrail design with honest, segmented metrics rather than a single 'safety classifier.' Here is how to measure and defend each failure mode.

Red teaming is how you uncover an AI system's failures before users or attackers do. What lands is a structured program covering the real attack surface (jailbreaks, harmful content, leakage, bias) and routing every finding back into defenses.

Safety is not a single number. The candidates who pass name the axes, run benchmarks as a gate, and then explain why benchmarks by themselves certify nothing. Here is the framing interviewers score highest.

A request that gets refused in one message often lands when broken across ten. The signal is understanding that conversational state is part of the attack surface, and that turn-by-turn classifiers see only fragments.

Roleplay, obfuscation, optimization-based suffixes, and multi-turn escalation are distinct attack classes that call for distinct defenses. The signal is organizing the space and pairing each class with a control instead of hoping one filter covers all.

Your guardrail reads text. The attack is not in the text. That single sentence is the whole vulnerability, and the red team you build from it looks nothing like the one you already have.