May 2026· arXiv.org· Vol abs/2605.14868· 1 citation· 26 references
Computer Science
TL;DR
A family of attacks is introduced that eliminates the backward pass by predicting the input gradient from forward-pass hidden states via a lightweight linear regression, and exact affine conditional gradient means are derived.
Abstract
Generating adversarial examples at scale is a core primitive for robustness evaluation, adversarial training, and red-teaming, yet even"fast"attacks such as FGSM remain throughput-limited by the cost of a backward pass. We introduce a family of attacks that eliminates the backward pass by predicting the input gradient from forward-pass hidden states via a lightweight linear regression. Theoretically, we derive exact affine conditional gradient means, showing optimality in the (idealized) NTK regime. Empirically, our methods work when applied to practical finite-width models; we recover much of FGSM's attack performance while using only a small fraction of the time, corresponding to a $532\%$ increase in throughput. These results suggest gradient prediction as a simple and general route to significantly faster adversarial generation under realistic wall-clock constraints.
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