  A new and successful attack strategy in neural cryptography is presented. The
neural cryptosystem, based on synchronization of neural networks by mutual
learning, has been recently shown to be secure under different attack
strategies. The advanced attacker presented here, named the ``Majority-Flipping
Attacker'', is the first whose success does not decay with the parameters of
the model. This new attacker's outstanding success is due to its using a group
of attackers which cooperate throughout the synchronization process, unlike any
other attack strategy known. An analytical description of this attack is also
presented, and fits the results of simulations.
