# Silent Killer: A Stealthy, Clean-Label, Black-Box Backdoor Attack Source: [Silent Killer: A Stealthy, Clean-Label, Black-Box Backdoor Attack]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:48:53.779Z Authors: Tzvi Lederer, Gallil Maimon, Lior Rokach Published: 2023-01-05T15:11:05.000Z ## Abstract Backdoor poisoning attacks pose a well-known risk to neural networks. However, most studies have focused on lenient threat models. We introduce Silent Killer, a novel attack that operates in clean-label, black-box settings, uses a stealthy poison and trigger and outperforms existing methods. We investigate the use of universal adversarial perturbations as triggers in clean-label attacks, following the success of such approaches under poison-label settings. We analyze the success of a naive adaptation and find that gradient alignment for crafting the poison is required to ensure high success rates. We conduct thorough experiments on MNIST, CIFAR10, and a reduced version of ImageNet and achieve state-of-the-art results. ## 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. ## Original sources and implementation [Original paper]() [PDF]()