以推理時間計算為代價，提升對抗韌性

Trading Inference-Time Compute for Adversarial Robustness

January 31, 2025

作者: Wojciech Zaremba, Evgenia Nitishinskaya, Boaz Barak, Stephanie Lin, Sam Toyer, Yaodong Yu, Rachel Dias, Eric Wallace, Kai Xiao, Johannes Heidecke, Amelia Glaese

cs.AI

摘要

我們對增加推論時間計算對推理模型（具體來說是OpenAI o1-preview和o1-mini）對抗對抗攻擊的韌性的影響進行實驗。我們發現，在各種攻擊中，增加推論時間計算會提高韌性。在許多情況下（有重要的例外情況），隨著測試時間計算量的增加，攻擊成功的模型樣本比例趨近於零。我們對我們研究的任務未進行任何對抗訓練，僅通過允許模型在推理過程中花費更多計算量來增加推論時間計算，獨立於攻擊形式。我們的結果表明，推論時間計算有潛力提高大型語言模型的對抗韌性。我們還探索了針對推理模型的新攻擊，以及推論時間計算無法提高可靠性的情況，並推測了這些情況的原因以及解決方法。

English

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.