Tilting the Scales: How Subtle Attacks Can Sway Financial Forecasts

by

Author: Denis Avetisyan

New research demonstrates that carefully crafted, slope-based manipulations can effectively deceive machine learning models used in financial time-series forecasting.

The system underwent a series of “C&W slope” attacks against the stockABDE dataset over the initial 300 days of recorded data, demonstrating a vulnerability window for adversarial manipulation within that timeframe.
The system underwent a series of “C&W slope” attacks against the stockABDE dataset over the initial 300 days of recorded data, demonstrating a vulnerability window for adversarial manipulation within that timeframe.

This paper introduces novel adversarial attacks leveraging Generative Adversarial Networks (GANs) to exploit vulnerabilities in financial data pipelines, emphasizing the need for end-to-end security.

While deep learning models increasingly drive financial forecasting, their susceptibility to adversarial manipulation remains a critical, underexplored vulnerability. This is addressed in ‘Targeted Manipulation: Slope-Based Attacks on Financial Time-Series Data’, which introduces novel slope-based attacks designed to subtly alter predicted stock trends generated by N-HiTS models. The research demonstrates that these attacks can bypass standard security measures and, when integrated into a GAN architecture, generate realistic yet misleading synthetic data. Given these findings, should ML security efforts expand beyond model robustness to encompass the integrity of the entire data and inference pipeline?

The Illusion of Prediction: Financial Models and the Seeds of Failure

The financial sector now heavily depends on machine learning models to predict future trends in areas like stock prices, currency exchange rates, and credit risk assessment. This transition, driven by the promise of increased accuracy and efficiency, marks a significant shift from traditional statistical methods. These models, often complex neural networks, analyze vast datasets of historical financial data to identify patterns and make informed projections, underpinning critical decisions in portfolio management, algorithmic trading, and risk mitigation. Consequently, the reliability of these forecasts directly impacts investment strategies and the stability of financial markets, making the widespread adoption of machine learning in finance both a powerful opportunity and a potential source of systemic vulnerability.

Machine learning models, while offering sophisticated predictive capabilities in financial forecasting, present a novel attack surface for malicious actors. Adversarial attacks involve the introduction of carefully crafted perturbations – subtle alterations to input data – designed to mislead the model without being readily detectable. These manipulations can induce incorrect predictions regarding stock prices, trading volumes, or credit risk, potentially leading to substantial financial losses for investors and institutions. Unlike traditional cybersecurity threats, these attacks don’t necessarily aim to disrupt system functionality, but rather to exploit the model’s learned patterns and biases. The impact extends beyond individual trades, as widespread manipulation could destabilize markets and erode confidence in algorithmic trading systems, necessitating robust defenses tailored to the unique vulnerabilities of time-series data.

Conventional security protocols, designed for static datasets, struggle with the temporal dependencies inherent in financial time-series data. These systems often analyze data points in isolation, failing to recognize manipulations that subtly alter trends over time-a tactic that can evade detection while still causing significant forecasting errors. Unlike image or text data, where adversarial perturbations are visually or semantically apparent, alterations to time-series data can be masked within the expected noise of market fluctuations. This makes it particularly challenging to differentiate between genuine market signals and deliberately crafted attacks, leaving forecasting models vulnerable to even minor, carefully designed data manipulations that can amplify into substantial financial consequences. The sequential nature of time-series also means that an attack impacting an early data point can propagate and worsen over subsequent predictions, making early detection and mitigation critical but difficult.

The proposed targeted adversarial GAN utilizes a high-level architecture designed to generate specific adversarial examples.
The proposed targeted adversarial GAN utilizes a high-level architecture designed to generate specific adversarial examples.

The Ghosts in the Machine: Manifestations of Adversarial Attacks

Adversarial attacks against time-series data leverage the principles of perturbation to induce errors in predictive models. Methods like the Fast Gradient Sign Method (FGSM) and its iterative counterpart, the Basic Iterative Method (BIM), calculate the gradient of the loss function with respect to the input data. These gradients indicate the direction and magnitude of change needed to maximize the loss-and thus, cause the model to misclassify or make inaccurate predictions. The perturbation is then applied to the original data, creating an adversarial example. While the changes introduced by these methods can be small-often imperceptible to human observation-they are sufficient to significantly alter the model’s output. The iterative nature of BIM allows for smaller, more refined perturbations to be applied over multiple steps, generally increasing the attack’s effectiveness compared to a single-step FGSM attack.

Slope-Based Attacks constitute a targeted manipulation of financial time-series data designed to alter predicted trends. These attacks focus on modifying input data to disproportionately impact the slope of model predictions, demonstrably achieving a doubling of the predicted slope compared to normal N-HiTS (Neural High-order Time-series) predictions. This amplified slope manipulation can lead to inaccurate forecasting, potentially triggering incorrect investment decisions or misrepresenting market behavior. The effectiveness of Slope-Based Attacks stems from their ability to exploit the sensitivity of time-series models to directional changes, inducing significant deviations in predicted values without necessarily altering the overall magnitude of the data.

Stealthy Iterative Methods pose a significant threat to time-series data security due to their ability to subtly manipulate data points while maintaining the overall temporal characteristics of the series. Unlike attacks that introduce abrupt changes, these methods iteratively adjust input values within permissible bounds, ensuring the perturbed data remains statistically similar to the original. This preservation of temporal integrity makes detection based on anomaly detection or visual inspection considerably more difficult, as standard techniques may fail to identify the adversarial modifications. Consequently, predictive models are more likely to be misled without triggering conventional security alerts, increasing the risk of undetected manipulation and potentially leading to erroneous outcomes.

Analysis of stockAPOAPO over the first 300 days reveals slope attacks and their corresponding perturbations.
Analysis of stockAPOAPO over the first 300 days reveals slope attacks and their corresponding perturbations.

Building Resilience: Defenses Against the Inevitable

Adversarial training is a defensive strategy that improves model robustness by intentionally exposing the model to carefully crafted, malicious inputs – known as adversarial examples – during the training process. These examples are created by adding small, often imperceptible, perturbations to legitimate data points, designed to cause the model to misclassify them. By incorporating these adversarial examples into the training dataset, the model is compelled to learn features that are less susceptible to these targeted perturbations, effectively increasing its resilience against adversarial attacks. This process effectively expands the decision boundaries of the model, allowing it to correctly classify inputs even when they have been subtly modified to evade detection. The technique requires careful consideration of the perturbation magnitude and the method used to generate the adversarial examples to avoid compromising the model’s accuracy on clean data.

Generative Adversarial Networks (GANs) are utilized to create adversarial examples for model training by learning the data distribution and generating inputs designed to cause misclassification. Traditional GANs can suffer from training instability; therefore, Wasserstein GANs (WGANs) are frequently employed due to their use of Earth Mover’s Distance, which provides a smoother gradient for training and avoids mode collapse. The generator network in a GAN attempts to produce adversarial examples that closely resemble legitimate data points but are intentionally perturbed to mislead the target model, while the discriminator network attempts to distinguish between real data and generated adversarial examples. This adversarial process iteratively improves both networks, resulting in more realistic and effective adversarial examples suitable for augmenting training datasets and enhancing model robustness against targeted attacks.

Gradient penalty and gradient clipping are regularization techniques employed during Generative Adversarial Network (GAN) training to address instability and improve convergence. Gradient penalty, such as the WGAN-GP method, enforces a Lipschitz constraint on the discriminator by penalizing the gradient norm when it deviates from a desired value, typically 1. This prevents the discriminator from becoming excessively confident and avoids vanishing gradients. Gradient clipping, conversely, limits the maximum magnitude of the gradients during backpropagation, mitigating the exploding gradient problem. Both techniques contribute to more stable training dynamics, allowing the generator to produce a wider variety of realistic adversarial examples. This improved diversity and quality of generated attacks, when used for adversarial training, directly translates to enhanced robustness in the target model by exposing it to a more comprehensive range of potential threats.

The A-GAN successfully generated a plausible output from a random input interval.
The A-GAN successfully generated a plausible output from a random input interval.

The System’s Fragility: Beyond Robustness to Systemic Risk

Although adversarial training fortifies machine learning models against carefully crafted input perturbations, a complete security posture demands consideration beyond this technique. Systems incorporating these models remain susceptible to more direct attacks, notably malware injection, which bypasses the model’s defenses entirely by compromising the underlying infrastructure. This type of attack doesn’t attempt to fool the model with subtle data manipulation; instead, it exploits vulnerabilities in the system to directly manipulate data or code before it even reaches the model, or to steal information after processing. Consequently, even a perfectly robust model is rendered useless if the system it inhabits is compromised, underscoring the necessity of comprehensive security measures encompassing network protection, code integrity checks, and robust access controls alongside model-specific defenses.

Successful attacks on financial machine learning systems represent a threat far exceeding immediate monetary losses. While compromised algorithms can directly facilitate fraudulent transactions, the broader consequences involve a significant erosion of public trust in the stability of financial institutions. This loss of confidence can trigger cascading effects, potentially destabilizing markets as investors react to perceived vulnerabilities and systemic risk. Beyond individual account breaches, widespread attacks could undermine the integrity of entire financial systems, leading to economic uncertainty and hindering long-term growth. The subtle but powerful impact of diminished trust, therefore, constitutes a critical, often underestimated, consequence of security failures in this domain.

Evaluations demonstrate that even with the implementation of adversarial training, conventional security protocols exhibit significant performance degradation when confronted with direct compromise attacks. Specifically, Convolutional Neural Networks (CNNs) experience a notable reduction in specificity – a measure of correctly identifying negative instances – falling by 28%. More critically, overall CNN accuracy, representing the proportion of correctly classified inputs, plummets by a substantial 57% under these attack conditions. These findings underscore a critical gap in current security infrastructure and emphasize the urgent need for advanced defenses that extend beyond adversarial robustness to comprehensively protect against system-level threats and maintain reliable performance in real-world applications.

The N-HiTS model successfully forecasts ADPStock behavior, as demonstrated in this example.
The N-HiTS model successfully forecasts ADPStock behavior, as demonstrated in this example.

The pursuit of predictive accuracy in financial modeling, as explored within this work, often obscures a fundamental truth: systems are not built, they become. This paper’s demonstration of slope-based attacks, subtly manipulating time-series data to influence forecasting models, isn’t a failure of prediction, but a revelation of inherent systemic vulnerability. Donald Davies observed, “A system’s complexity is a measure of its ignorance.” The elegance of these attacks lies not in brute force, but in exploiting the assumptions embedded within the system itself – the very ‘ignorance’ Davies speaks of. Securing the entire machine learning pipeline, as this research advocates, is merely acknowledging that the system is always already becoming something other than intended, and any attempt at absolute control is a beautifully crafted illusion.

The Horizon Beckons

The demonstrated susceptibility of time-series forecasting to even subtly manipulated input suggests a fundamental miscalculation in how these systems are conceived. The focus remains stubbornly fixed on model accuracy, as if a more precise equation can somehow inoculate against the inherent noise of the market itself. This work, however, underscores that the true attack surface isn’t the algorithm, but the data pipeline – a complex, distributed ecosystem far more fragile than any isolated model. A guarantee of forecast stability is merely a contract with probability, and one likely to be breached.

Future research should abandon the pursuit of ‘robustness’ – a static ideal in a dynamic system – and instead concentrate on adaptive resilience. Systems must learn to recognize, and even incorporate, these deliberate distortions, treating them not as anomalies to be filtered, but as signals. The current emphasis on adversarial training feels akin to building a dam against a rising tide; a temporary reprieve before the inevitable breach.

Ultimately, stability is merely an illusion that caches well. The field needs to shift from attempting to prevent manipulation to accepting it as an intrinsic component of the system. Chaos isn’t failure – it’s nature’s syntax. The next generation of financial forecasting won’t be about predicting the future, but about navigating uncertainty with informed agility.

Original article: https://arxiv.org/pdf/2511.19330.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2025-11-25 17:27