Trading Inference-Time Compute for Adversarial Robustness
Authors: name: Wojciech Zaremba, name: Evgenia Nitishinskaya, name: Boaz Barak, name: Stephanie Lin, name: Sam Toyer, name: Yaodong Yu, name: Rachel Dias, name: Eric Wallace, name: Kai Xiao, name: Johannes Heidecke, name: Amelia Glaese

We conduct experiments on the impact of increasing inference-time compute in
reasoning models (specifically OpenAI o1-preview and o1-mini) on their
robustness to adversarial attacks. We find that across a variety of attacks,
increased inference-time compute leads to improved robustness. In many cases
(with important exceptions), the fraction of model samples where the attack
succeeds tends to zero as the amount of test-time compute grows. We perform no
adversarial training for the tasks we study, and we increase inference-time
compute by simply allowing the models to spend more compute on reasoning,
independently of the form of attack. Our results suggest that inference-time
compute has the potential to improve adversarial robustness for Large Language
Models. We also explore new attacks directed at reasoning models, as well as
settings where inference-time compute does not improve reliability, and
speculate on the reasons for these as well as ways to address them.