Microsoft paper: unaligned small models 'launder' capabilities via frontier model consultation
dair_ai · x · 2026-09-16
A new Microsoft safety paper describes "capability laundering": a weak, unaligned model splits a harmful task into innocuous-looking subquestions, asks an aligned frontier model each one in separate sessions, and reassembles answers locally—each request passes safety checks on its own.
- Consulted models included GPT-5.5, Claude Opus 4.8 and Grok-4.3
- On CyBench, Gemma-4-31B recovered 8 of 14 failed tasks after consulting GPT-5.5
- On a CBRN attack chain, consultation raised mean rubric score from 62.3 to 83.1
The finding shows frontier-model alignment can be bypassed via decomposed multi-agent requests, a new multi-agent safety risk.
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