Adversarial attacks significantly undermine the robustness of deep learning
models, posing a major challenge to their deployment in critical applications. Universal
adversarial perturbations pose a formidable threat due to their ability to mislead models for
various inputs. This paper introduces a novel method to attack text classifiers in a black-box
setting using universal adversarial attacks. It accomplishes this by enhancing trigger words,
which are specific sequences of words that, when added to any input, cause consistent
misclassification. The proposed method utilizes the Genetic Algorithm (GA) and differs
from the conventional white-box attack as it does not need internal architectures or
gradients within the model. Instead, it iteratively evolves a population of adversarial
triggers, effectively navigating the search space to maintain fluency and semantic coherence
in the generated sequences. We achieved a high rate of successful attacks across the Bi-
LSTM and BERT models while preserving the naturalness of the input text. Our findings
demonstrate the potential of GA-optimized universal triggers as a formidable and practical
tool for adversarial attacks in real-world situations where only input-output access to the
target model is available.