# How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness Source: [How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness]() Collection index: [llms.txt]() Data updated: 2026-01-28T05:00:08.385Z Authors: Giulio Rossolini Published: 2026-01-20T22:24:47.000Z ## Abstract Adversarial attacks are widely used to identify model vulnerabilities; however, their validity as proxies for robustness to random perturbations remains debated. We ask whether an adversarial example provides a representative estimate of misprediction risk under stochastic perturbations of the same magnitude, or instead reflects an atypical worst-case event. To address this question, we introduce a probabilistic analysis that quantifies this risk with respect to directionally biased perturbation distributions, parameterized by a concentration factor $\\kappa$ that interpolates between isotropic noise and adversarial directions. Building on this, we study the limits of this connection by proposing an attack strategy designed to probe vulnerabilities in regimes that are statistically closer to uniform noise. Experiments on ImageNet and CIFAR-10 systematically benchmark multiple attacks, revealing when adversarial success meaningfully reflects robustness to perturbations and when it does not, thereby informing their use in safety-oriented robustness evaluation. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Adversarial Robustness on CIFAR-10 (test)](): Adversarial Robustness; CIFAR-10 (test); Attack Success Rate (ASR): 58.5. ## Original sources and implementation [Original paper]() [PDF]()