Divide, Consult, Conquer: Capability Laundering Through Aligned LLMs
Language model safety is typically evaluated one interaction at a time. We show that a weaker, unaligned model can split a harmful task into benign-looking subproblems, consult a stronger aligned model independently on each, and combine the answers locally. We call this attack capability laundering. Unlike a jailbreak, no single response is a harmful task. We measure consultation-aided uplift using tasks that a raw frontier model solves, the aligned frontier refuses, and the unassisted orchestrator fails. We evaluate GPT-5.5, Claude Opus 4.8, and Grok-4.3 as consultants to four local orchestrators on CyBench, BountyBench, and harmful CBRN requests. On CyBench, Gemma-4-31B recovers 8/14 ca...
| Authors | Mark Russinovich, Blake Bullwinkel, Giorgio Severi et al. |
|---|---|
| Primary category | cs.CR |
| Published | 14 September 2026 |
| Last updated at source | 12 October 2026 |
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