# Adversarial Attacks on Machine Learning Systems for High-Frequency Trading

arXiv: https://arxiv.org/abs/2002.09565 · HTML: https://mlsanity.com/paper/2002.09565/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 11 of 11)

- [Baseline Defenses for Adversarial Attacks Against Aligned Language Models](https://mlsanity.com/paper/2309.00614/) (2023)
- [LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition](https://mlsanity.com/paper/2101.07922/) (2021)
- [The Threat of Offensive AI to Organizations](https://mlsanity.com/paper/2106.15764/) (2021)
- [The Uncanny Similarity of Recurrence and Depth](https://mlsanity.com/paper/2102.11011/) (2021)
- [Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey](https://mlsanity.com/paper/2303.06302/) (2023)
- [Identification of Attack-Specific Signatures in Adversarial Examples](https://mlsanity.com/paper/2110.06802/) (2021)
- [Adversarial News and Lost Profits: Manipulating Headlines in LLM-Driven Algorithmic Trading](https://mlsanity.com/paper/2601.13082/) (2026)
- [Shadows Aren't So Dangerous After All: A Fast and Robust Defense Against Shadow-Based Adversarial Attacks](https://mlsanity.com/paper/2208.09285/) (2022)
- [Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents](https://mlsanity.com/paper/2309.14615/) (2023)
- [Harmonic Machine Learning Models are Robust](https://mlsanity.com/paper/2404.18825/) (2024)
- [Catastrophic Overfitting, Entropy Gap and Participation Ratio: A Noiseless lp Norm Solution for Fast Adversarial Training](https://mlsanity.com/paper/2505.02360/) (2025)

## Abstract (arXiv)

Algorithmic trading systems are often completely automated, and deep learning is increasingly receiving attention in this domain. Nonetheless, little is known about the robustness properties of these models. We study valuation models for algorithmic trading from the perspective of adversarial machine learning. We introduce new attacks specific to this domain with size constraints that minimize attack costs. We further discuss how these attacks can be used as an analysis tool to study and evaluate the robustness properties of financial models. Finally, we investigate the feasibility of realistic adversarial attacks in which an adversarial trader fools automated trading systems into making inaccurate predictions.