titleDecoy Direction Optimization: A Post-Hoc Defense Against LLM Abliteration
authorsAashiq Muhamed, Mona T. Diab, Virginia Smith
date2026-09-14
venuearXiv (preprint, not peer reviewed)
idarXiv:2609.16204
affiliationCarnegie Mellon University
upvotes4 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractSafety 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.

cite

@misc{muhamed2026decoy,
  title={Decoy Direction Optimization: A Post-Hoc Defense Against LLM Abliteration},
  author={Aashiq Muhamed and Mona T. Diab and Virginia Smith},
  year={2026},
  eprint={2609.16204},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2609.16204}
}