A research paper demonstrates 'capability laundering,' where a weaker unaligned language model splits harmful tasks into benign subtasks, queries stronger aligned models like GPT-4 and Claude independently on each part, and recombines the answers to bypass safety measures. Testing shows significant uplift in harmful capabilities across multiple benchmarks, including bioweapon development scenarios, exposing gaps in current AI safety defenses.