Divide, Consult, Conquer: Capability Laundering Through Aligned LLMs
Attack shows unaligned orchestrators can launder capabilities from aligned frontier LLMs via benign subtask consultation, raising Gemma-4-31B CBRN rubric score from 62.3 to 83.1.
The paper introduces capability laundering, where a weaker unaligned model decomposes a harmful task into benign-looking subproblems, queries a stronger aligned model on each, and recombines answers locally, bypassing per-interaction safety evaluations. Evaluation used GPT-5.5, Claude Opus 4.8, and Grok-4.3 as consultants to four local orchestrators on CyBench, BountyBench, and CBRN tasks. On CyBench, Gemma-4-31B recovered 8/14 candidate tasks with GPT-5.5 and 7/9 with Opus, while Muse-Glimmer-30B recovered none. Across an eight-step hypothetical bioweapon attack chain, consultation raised Gemma-4-31B's mean rubric score from 62.3 to 83.1, exposing a gap in defenses that only refuse complete harmful tasks.
- Capability laundering splits harmful tasks into individually benign subproblems
- No single consultant response constitutes the harmful task
- GPT-5.5, Claude Opus 4.8, and Grok-4.3 tested as consultants
- Gemma-4-31B CBRN rubric score rises from 62.3 to 83.1
- Per-interaction refusals fail to stop capability transfer and composition
From the original article
1 min read at arxiv.orgLanguage 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.
By Mark Russinovich · arxiv.org
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