  In this paper, we introduce a variation of the group testing problem where
each test is specified by an ordered subset of items, and returns the first
defective item in the specified order. We refer to this as \textit{cascaded
group testing} and the goal is to identify a small set of $K$ defective items
amongst a collection of size $N$, using as few tests as possible. For the
adaptive testing regime, we show that a simple scheme is able to find all
defective items in at most $K$ tests, which is optimal. For the non-adaptive
setting, we first come up with a necessary and sufficient condition for any
collection of tests to be feasible for recovering all the defectives. Using
this, we are able to show that any feasible non-adaptive strategy requires at
least $\Omega(K^2)$ tests. In terms of achievability, it is easy to show that a
collection of $O(K^2 \log (N/K))$ randomly constructed tests is feasible. We
show via carefully constructed explicit designs that one can do significantly
better. We provide two simple schemes for $K = 1, 2$ which only require one and
two tests respectively irrespective of the number of items $N$. Note that this
is in contrast to standard binary group testing, where at least $\Omega(\log
N)$ tests are required. The case of $K \ge 3$ is more challenging and here we
come up with an iterative design which requires only $\text{poly}(\log \log N)$
tests.
