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arXiv AI By Mark Russinovich, Blake Bullwinkel, Giorgio Severi, Cristian Ovadiuc, Ahmed Salem

Divide, Consult, Conquer: Capability Laundering Through Aligned LLMs

Read the original on arXiv AI →

The paper introduces "capability laundering," a method where a weaker, unaligned language model splits a harmful task into benign subproblems, consults a stronger aligned model on each, and locally combines the answers. Experiments with GPT‑5.5, Claude Opus 4.8, and Grok‑4.3 as consultants to various local orchestrators show significant uplift on CyBench, BountyBench, and a bioweapon attack chain, with Gemma‑4‑31B recovering many more candidates than the aligned models alone. The results reveal that refusing a harmful task does not stop frontier capabilities from being transferred and composed across multiple permitted interactions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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