AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Safety-aligned large language models (LLMs) remain vulnerable to adversarial prompts that bypass their safety training. Existing automated red-teaming methods either require privileged access unavailable in production, rely on manually engineered per-model templates, or optimize a single objective without population-le...
How a stack behaves, and the quantities a defender cares about diverge: coverage saturates within a tier, cost rises by class, false refusals accumulate as a union, and residual attack success falls multiplicatively only under independence.
A red teaming framework that systematically uncovers vulnerabilities in LLM outputs and provides both actionable insights into current LLM vulnerabilities and a scalable methodology for ongoing safety evaluation as models continue to evolve is presented.
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