  Robust network flows are a concept for dealing with uncertainty and
unforeseen failures in the network infrastructure. They and their dual
counterpart, network flow interdiction, have received steady attention within
the operations research community over the past years. One of the most basic
models is the Maximum Robust Flow Problem: Given a network and an integer k,
the task is to find a path flow of maximum robust value, i.e., the guaranteed
value of surviving flow after removal of any k arcs in the network. The
complexity of this problem appeared to have been settled almost a decade ago:
Aneja et al. (2001) showed that the problem can be solved efficiently when k =
1, while an article by Du and Chandrasekaran (2007) established that the
problem is NP-hard for any constant value of k larger than 1.
  We point to a flaw in the proof of the latter result, leaving the complexity
for constant k open once again. For the case that k is not bounded by a
constant, we present a new hardness proof, establishing that the problem is
strongly NP-hard, even when only two different capacity values occur and the
number of paths is polynomial in the size of the input. We further show that
computing optimal integral solutions is already NP-hard for k = 2 (whereas for
k=1, an efficient algorithm is known) and give a positive result for the case
that capacities are in {1, 2}.
