Targeted Manipulation: Slope-Based Attacks on Financial Time-Series Data

• URL: http://arxiv.org/abs/2511.19330v1
• Date: Mon, 24 Nov 2025 17:26:20 GMT
• Title: Targeted Manipulation: Slope-Based Attacks on Financial Time-Series Data
• Authors: Dominik Luszczynski,
• Abstract summary: This paper introduces two new slope-based methods aimed to alter the trends of the predicted stock forecast generated by an N-HiTS model.<n>Compared to the normal N-HiTS predictions, the two new methods can manipulate N-HiTS predictions by doubling the slope.<n>These new slope attacks can bypass standard security mechanisms, such as a discriminator that filters real and perturbed inputs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A common method of attacking deep learning models is through adversarial attacks, which occur when an attacker specifically modifies the input of a model to produce an incorrect result. Adversarial attacks have been deeply investigated in the image domain; however, there is less research in the time-series domain and very little for forecasting financial data. To address these concerns, this study aims to build upon previous research on adversarial attacks for time-series data by introducing two new slope-based methods aimed to alter the trends of the predicted stock forecast generated by an N-HiTS model. Compared to the normal N-HiTS predictions, the two new slope-based methods, the General Slope Attack and Least-Squares Slope Attack, can manipulate N-HiTS predictions by doubling the slope. These new slope attacks can bypass standard security mechanisms, such as a discriminator that filters real and perturbed inputs, reducing a 4-layered CNN's specificity to 28% and accuracy to 57%. Furthermore, the slope based methods were incorporated into a GAN architecture as a means of generating realistic synthetic data, while simultaneously fooling the model. Finally, this paper also proposes a sample malware designed to inject an adversarial attack in the model inference library, proving that ML-security research should not only focus on making the model safe, but also securing the entire pipeline.

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