  Robustness is pivotal for comprehending, designing, optimizing, and
rehabilitating networks, with simulation attacks being the prevailing
evaluation method. Simulation attacks are often time-consuming or even
impractical, however, a more crucial yet persistently overlooked drawback is
that any attack strategy merely provides a potential paradigm of
disintegration. The key concern is: in the worst-case scenario or facing the
most severe attacks, what is the limit of robustness, referred to as ``Worst
Robustness'', for a given system? Understanding a system's worst robustness is
imperative for grasping its reliability limits, accurately evaluating
protective capabilities, and determining associated design and security
maintenance costs. To address these challenges, we introduce the concept of
Most Destruction Attack (MDA) based on the idea of knowledge stacking. MDA is
employed to assess the worst robustness of networks, followed by the
application of an adapted CNN algorithm for rapid worst robustness prediction.
We establish the logical validity of MDA and highlight the exceptional
performance of the adapted CNN algorithm in predicting the worst robustness
across diverse network topologies, encompassing both model and empirical
networks.
