Divide, Consult, Conquer: Capability Laundering Through Aligned LLMs
AuthorsMark Russinovich, Blake Bullwinkel, Giorgio Severi, Cristian Ovadiuc, Ahmed Salem
Resources
The paper shows how a weaker model can turn an aligned model’s harmless answers into a dangerous capability by splitting and recombining the task.
Key results
Gemma-4-31B recovered 8 of 14 uplift candidates.
Gemma-4-31B recovered 7 of 9 uplift candidates.
Gemma-4-31B recovered 3 of 9 uplift candidates.
Gemma-4-31B recovered 2 of 3 uplift candidates.
Gemma-4-31B rose from 62.3 to 83.1 with consultation on a 100-point rubric.
What the paper found
The paper introduces capability laundering, an attack in which an unaligned local orchestrator preserves a harmful objective, decomposes it into benign-looking fragments, consults an aligned frontier model without revealing the full intent, and recombines the answers externally. This differs from a jailbreak: no single consultation completes or visibly advances the prohibited task, yet the composed workflow can exceed both the aligned model’s direct access and the orchestrator’s unaided capability. Experiments used OpenAI’s GPT-5.5, Anthropic’s Claude Opus 4.8, and Grok-4.3 as consultants, with locally hosted Gemma-4-31B, Gemma-4-12B, Muse-Glimmer-30B, and Qwen3.6-27B orchestrators on CyBench, BountyBench, and eight rubric-scored CBRN attack-chain steps. Gemma-4-31B recovered 57% of GPT-5.5’s CyBench capability-gap candidates, or 8 of 14, and 78% with Opus, or 7 of 9; on BountyBench it recovered 33%, or 3 of 9, and 67%, or 2 of 3. In CBRN evaluations, consultation raised Gemma-4-31B’s mean rubric score from 62.3 to 83.1 on a 100-point scale, despite direct aligned frontier access largely refusing. Recovery depended strongly on orchestration quality, including decomposition, state tracking, validation, and local integration. The findings show that per-request refusal and jailbreak resistance do not guarantee system-level safety; defenses need cross-query provenance, semantic composition monitoring, and end-to-end evaluation of capabilities assembled outside a model’s context.
Original abstract
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 candidates with GPT-5.5 and 7/9 with Opus, compared with 2/21 and 4/15 for Gemma-4-12B. On BountyBench, Gemma-4-31B recovers 3/9 and 2/3 candidates, while Muse-Glimmer-30B recovers none of 22 and 13. For CBRN, we measure uplift across eight steps of a hypothetical bioweapon attack chain and find that consultation raises Gemma-4-31B's mean rubric score from 62.3 to 83.1 on a 100-point rubric scale. These results expose a gap in current defenses: refusing a harmful task does not prevent frontier capabilities from being transferred and composed across many individually permitted interactions.
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