  Machine learning algorithms are vulnerable to data poisoning attacks. Prior
taxonomies that focus on specific scenarios, e.g., indiscriminate or targeted,
have enabled defenses for the corresponding subset of known attacks. Yet, this
introduces an inevitable arms race between adversaries and defenders. In this
work, we study the feasibility of an attack-agnostic defense relying on
artifacts that are common to all poisoning attacks. Specifically, we focus on a
common element between all attacks: they modify gradients computed to train the
model. We identify two main artifacts of gradients computed in the presence of
poison: (1) their $\ell_2$ norms have significantly higher magnitudes than
those of clean gradients, and (2) their orientation differs from clean
gradients. Based on these observations, we propose the prerequisite for a
generic poisoning defense: it must bound gradient magnitudes and minimize
differences in orientation. We call this gradient shaping. As an exemplar tool
to evaluate the feasibility of gradient shaping, we use differentially private
stochastic gradient descent (DP-SGD), which clips and perturbs individual
gradients during training to obtain privacy guarantees. We find that DP-SGD,
even in configurations that do not result in meaningful privacy guarantees,
increases the model's robustness to indiscriminate attacks. It also mitigates
worst-case targeted attacks and increases the adversary's cost in multi-poison
scenarios. The only attack we find DP-SGD to be ineffective against is a
strong, yet unrealistic, indiscriminate attack. Our results suggest that, while
we currently lack a generic poisoning defense, gradient shaping is a promising
direction for future research.
