The Rogue Scalpel: Activation Steering Compromises LLM Safety
Abstract
Activation steering is a promising technique for controlling LLM behavior by adding semantically meaningful vectors directly into a model's hidden states during inference. It is often framed as a precise, interpretable, and potentially safer alternative to fine-tuning. We demonstrate the opposite: steering systematically breaks model alignment safeguards, making it comply with harmful requests. Through extensive experiments on different model families, we show that even steering in a random direction can increase the probability of harmful compliance from 0% to 1-13%. Alarmingly, steering benign features from a sparse autoencoder (SAE), a common source of interpretable directions, demonstrates a comparable harmful potential. Finally, we show that combining 20 randomly sampled vectors that jailbreak a single prompt creates a universal attack, significantly increasing harmful compliance on unseen requests. These results challenge the paradigm of safety through interpretability, showing that precise control over model internals does not guarantee precise control over model behavior.
Cite
@article{arxiv.2509.22067,
title = {The Rogue Scalpel: Activation Steering Compromises LLM Safety},
author = {Anton Korznikov and Andrey Galichin and Alexey Dontsov and Oleg Y. Rogov and Ivan Oseledets and Elena Tutubalina},
journal= {arXiv preprint arXiv:2509.22067},
year = {2026}
}