Automated Red Teaming via Model Curriculum

Evaluating the safety of large language models via automated red-teaming is frequently. bottlenecked by black-box testing methodologies. When adversarial agents interact with rigid, binary safety filters, they suffer from sparse reward signals. This often traps the optimizer in local optima, resulting in superficial reward hacking and model collapse instead of genuine vulnerability discovery. To overcome these limitations, we present an automated red-teaming framework that utilizes an adapter-driven curriculum to create a tractable optimization landscape. Rather than attacking a fully hardened model immediately, our adversarial agent trains against a progressive curriculum of target models modified via Heretic adapters. By systematically scaling the target’s alignment strictness—transitioning from an abliterated, unaligned baseline up to full alignment—we prevent early-stage gradient problems and guide the agent toward correct prompts. Our results demonstrate that this adapter-based curriculum effectively bypasses optimization stall on smaller models. The framework successfully isolates zero-shot jailbreaks, revealing latent vulnerabilities hidden beneath standard safety alignments