title: Trading Inference-Time Compute for Adversarial Robustness
summary: 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.
paper_authors._id: 67a02c75221b701e4c04da7f
paper_authors.name: Wojciech Zaremba
paper_authors._id: 67a02c75221b701e4c04da80
paper_authors.name: Evgenia Nitishinskaya
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-03T12:56:51.252Z
paper_authors._id: 6792b8359967b7f195447e43
paper_authors.avatarUrl: /avatars/0fcad468c7062d003902e78975daf6ea.svg
paper_authors.fullname: Evgenia Nitishinskaya
paper_authors.type: user
paper_authors.user: gadzin1203
paper_authors._id: 67a02c75221b701e4c04da81
paper_authors.name: Boaz Barak
paper_authors._id: 67a02c75221b701e4c04da82
paper_authors.name: Stephanie Lin
paper_authors._id: 67a02c75221b701e4c04da83
paper_authors.name: Sam Toyer
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-03T12:57:23.000Z
paper_authors._id: 62952a811e87ffbe5c06e3d4
paper_authors.avatarUrl: /avatars/8140d3a6cbb0f85c284a1fd388915cb2.svg
paper_authors.fullname: Sam Toyer
paper_authors.type: user
paper_authors.user: qxcv
paper_authors._id: 67a02c75221b701e4c04da84
paper_authors.name: Yaodong Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-03T12:57:28.373Z
paper_authors._id: 6100e69a393be1b5c4c83867
paper_authors.avatarUrl: /avatars/1b87098cffb9c50345789808daea4f68.svg
paper_authors.fullname: Yaodong Yu
paper_authors.type: user
paper_authors.user: yaodongyu
paper_authors._id: 67a02c75221b701e4c04da85
paper_authors.name: Rachel Dias
paper_authors._id: 67a02c75221b701e4c04da86
paper_authors.name: Eric Wallace
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-03T12:57:48.651Z
paper_authors._id: 63112d2431257261d20d5754
paper_authors.avatarUrl: /avatars/502a68c0fb2f0c6989fe2869d0a7e3f4.svg
paper_authors.fullname: Eric Wallace
paper_authors.type: user
paper_authors.user: EricWallace
paper_authors._id: 67a02c75221b701e4c04da87
paper_authors.name: Kai Xiao
paper_authors._id: 67a02c75221b701e4c04da88
paper_authors.name: Johannes Heidecke
paper_authors._id: 67a02c75221b701e4c04da89
paper_authors.name: Amelia Glaese
paper_title: Trading Inference-Time Compute for Adversarial Robustness
paper_summary: 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.
paper_ai_summary: Increasing inference-time compute enhances the robustness of reasoning models, such as OpenAI o1-preview and o1-mini, against adversarial attacks without adversarial training.
paper_ai_keywords: reasoning models OpenAI o1-preview o1-mini adversarial attacks inference-time compute adversarial robustness Large Language Models