SafeSteer: Interpretable Safety Steering with Refusal-Evasion in LLMs
Journal:
arXiv
Published Date:
Jun 1, 2025
Abstract
Fine-tuning large language models (LLMs) to adapt to evolving safety policies
is costly and impractical. Mechanistic interpretability enables inference-time
control through latent activation steering, yet its potential for precise,
customizable safety adjustments remains largely untapped. This paper
investigates an approach called SafeSteer for guiding the outputs of LLMs by:
(i) leveraging category-specific steering vectors for more precise control,
(ii) employing a simple, gradient-free unsupervised method to enhance safety
steering while preserving text quality, topic relevance, and without explicit
refusal, and (iii) accomplishing this without a hard requirement of contrastive
pairwise safe data. We also highlight that our method, being simple and
effective, aligns with recent studies suggesting that simple techniques often
outperform more complex ones in activation steering. We showcase the
effectiveness of our approach across various LLMs, datasets, and risk
categories, demonstrating its ability to provide precise control, prevent
blanket refusals, and guide models toward generating safe content while
maintaining topic relevance.
