Possibly but no one has demonstrated them, and there are probably many fewer in Zero than in previous AlphaGos. Previously AlphaGos suffered from problems which

Possibly but no one has demonstrated them, and there are probably many fewer in Zero than in previous AlphaGos. Previously AlphaGos suffered from problems which Silver calls 'delusions' (https://www.reddit.com/r/MachineLearning/comments/76xjb5/ama_we_are_david_silver_and_julian_schrittwieser/dohp9vc/ https://www.reddit.com/r/MachineLearning/comments/76xjb5/ama...) ie persistent long-term misevaluations of specific board positions, which were however (entirely? how would we know if not?) fixed by Zero's self-play - arguably these are not 'adversarial examples' in the sense you're thinking of but then I'm not sure what would constitute an adversarial example for a Go agent, so maybe the AlphaGo 'delusions' were the equivalent. (On a side note, the more I think about Anthony et al 2017/AlphaGo Zero's tree/expert iteration, the more beautifully simple it appears to me.)