The Rogue Scalpel: Activation Steering Compromises LLM Safety
Authors: name: Anton Korznikov, name: Andrey Galichin, name: Alexey Dontsov, name: Oleg Y. Rogov, name: Ivan Oseledets, name: Elena Tutubalina

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 2-27%. Alarmingly, steering benign
features from a sparse autoencoder (SAE), a common source of interpretable
directions, increases these rates by a further 2-4%. 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.