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M. Russinovich

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#artificial intelligence Preprint Sep 2026

Divide, Consult, Conquer: Capability Laundering Through Aligned LLMs

This work shows 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 to laundering attack capability laundering.

M. Russinovich, Blake Bullwinkel, Giorgio Severi et al. · 0 citations
#natural language process... Preprint Sep 2026

SpecGuard: Inference-Time Backdoor Detection For Free

SpecGuard is introduced, an inference-time backdoor detector that repurposes speculative decoding at zero added model-computation cost and doubles as a free, always-on signal for detecting backdoored LLM behavior.

Wen Rui, Ahmed Salem, Andrew Paverd et al. · 0 citations
Preprint Aug 2026

Fool's Gold: Defensive Deception Against Safety-Removal Attacks on Open-Weight Models

The defense, decoy hardening ("Fool's Gold"), concedes the refusal strip and poisons its payoff: once refusal is stripped, most answers to hazardous operational requests are confident, fluent decoys whose critical elements are falsified.

M. Russinovich · 0 citations

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