Workshop: Workshop on Machine Learning Safety

Adversarial Policies Beat Professional-Level Go AIs

Tony Wang · Adam Gleave · Nora Belrose · Tom Tseng · Michael Dennis · Yawen Duan · Viktor Pogrebniak · Joseph Miller · Sergey Levine · Stuart Russell


We attack the state-of-the-art Go-playing AI system, KataGo, by training an adversarial policy that plays against a frozen KataGo victim. Our attack achieves a >99\% win-rate against KataGo without search, and a >50% win-rate when KataGo uses enough search to be near-superhuman. To the best of our knowledge, this is the first successful end-to-end attack against a Go AI playing at the level of a top human professional. Notably, the adversary does not win by learning to play Go better than KataGo---in fact, the adversary is easily beaten by human amateurs. Instead, the adversary wins by tricking KataGo into ending the game prematurely at a point that is favorable to the adversary. Our results demonstrate that even professional-level AI systems may harbor surprising failure modes. Our results demonstrate that AI systems which are normally superhuman may still be less robust than humans. Example games are available at

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