  CountSketch is a popular dimensionality reduction technique that maps vectors
to a lower dimension using randomized linear measurements. The sketch supports
recovering $\ell_2$-heavy hitters of a vector (entries with $v[i]^2 \geq
\frac{1}{k}\|\boldsymbol{v}\|^2_2$). We study the robustness of the sketch in
adaptive settings where input vectors may depend on the output from prior
inputs. Adaptive settings arise in processes with feedback or with adversarial
attacks. We show that the classic estimator is not robust, and can be attacked
with a number of queries of the order of the sketch size. We propose a robust
estimator (for a slightly modified sketch) that allows for quadratic number of
queries in the sketch size, which is an improvement factor of $\sqrt{k}$ (for
$k$ heavy hitters) over prior work.
