Decoy Direction Optimization: Mechanistic Weight Editing Against LLM Abliteration

Safety guardrails in open-weight language models can be trivially bypassed using Refusal Feature Ablation (RFA), a technique that identifies and projects out a linear refusal direction from the residual stream, often achieving a high attack success rate (ASR) while preserving model capability. Defending against these attacks typically requires computationally expensive safety finetuning for every new checkpoint. We introduce Decoy Direction Optimization (DDO), a fast, post-hoc weight-editing defense that requires no base-model finetuning. Our approach is based on a simple mechanistic insight: ablation attacks rely on contrastive estimators to find the refusal direction. Rather than trying to hide the true refusal circuitry, DDO actively injects a high-magnitude, nonlinear decoy signal into the network's MLP neurons. When an attacker attempts to locate the refusal direction, the decoy corrupts their estimator, tricking them into ablating a harmless orthogonal feature while the actual safety mechanism remains intact. We prove a spectral bound formalizing this effect and evaluate DDO across six model families, where it reduces the ASR of standard RFA from >85% to <10%; matches trained defenses under adaptive multi-phase attacks (65% vs. 58% worst-case ASR); and reduces Heretic weight-level attack ASR from 88.7% to 18%, all at 30–450× lower cost.