While it is possible that two trainings yield different weaknesses, the original paper is precise enough (and the authors were active enough in message boards) that it could be reproduced. Some tweaks were added as things went along. However, the fact that KataGo and ELF OpenGo, which have different tweaks but are also based on AlphaZero, are also vulnerable implies that it is very likely that AlphaGoZero and AlphaZero share the weakness.

The fundamental issue is that they all do self-play, and those plays follow the same statistical distribution as a result. Weird games happen rarely, while the adversarial network forces a weird game every time.

On the other hand, I am not sure whether MuZero would be vulnerable. I would guess so, but its dynamics system may learn something extra about the state of the board.

Either way, it seems clear that future self-play training should include adversarial models, instead of pure self-play.

(They also all share another weakness, including MuZero, which is that their MCTS algorithm doesn’t allow the neural network to gain board information from their evaluation of possible futures; only win rate. Meanwhile a human sometimes realizes, while navigating a possible sequence of moves, that one particular early move could turn the tide.)