Decoy Direction Optimization: A Post-Hoc Defense Against LLM Abliteration

2026-09-14

AI Summary

本文提出Decoy Direction Optimization方法，通过注入非线性诱饵信号来防御针对语言模型的Refusal Feature Ablation攻击，无需基础模型微调，显著降低了攻击成功率。

Abstract

Safety guardrails in open-weight language models can be readily 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, achieving <10% ASR under standard RFA. On Llama-3-8B-Instruct, DDO remains comparable to 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 to 450 times lower optimization cost per configuration than the trained baselines.

Problem

• Safety Guardrails
• Refusal Feature Ablation (RFA)
• Attack Success Rate (ASR)

Innovation

• Decoy Direction Optimization
• Post-Hoc Defense
• Nonlinear Decoy Signal
• Safety Mechanism Protection
• Reduced Attack Success Rate