Even with all the compute in the world, chess engines cannot compute very deeply. So, they have to make use of pruning heuristics and discard moves from the analysis.
Seems possible that even the best heuristics will mistakenly discard good moves, and conversely go down bad paths that initially look good from a heuristic standpoint.
Have researchers tried to identify when this tends to happen, and if this can be taken advantage of, similar to adversarial examples in Deep Learning? E.g. an expert with deep understanding of a chess engine internals could concoct a series of moves to force the chess engine to make bad moves.
I play chess.com occasionally, and I've noticed something like this happening, where the chess engine will evaluate the same move as exceedingly good, and then as exceedingly bad.