Temporally Sparse Attack for Fooling Large Language Models in Time Series Forecasting

Fuqiang Liu, Sicong Jiang

TL;DR: This study introduces a novel temporally sparse adversarial attacks, showing that manipulate a small part of input time series can significantly degrade LLM-based forecasting performance.

Abstract: Large Language Models (LLMs) have shown great potential in time series forecasting by capturing complex temporal patterns. Recent research reveals that LLM-based forecasters are highly sensitive to small input perturbations. However, existing attack methods often require modifying the entire time series, which is impractical in real-world scenarios. To address this, we propose a Temporally Sparse Attack (TSA) for LLM-based time series forecasting. By modeling the attack process as a Cardinality-Constrained Optimization Problem (CCOP), we develop a Subspace Pursuit (SP)--based method that restricts perturbations to a limited number of time steps, enabling efficient attacks. Experiments on advanced LLM-based time series models, including LLMTime (GPT-3.5, GPT-4, LLaMa, and Mistral), TimeGPT, and TimeLLM, show that modifying just 10\% of the input can significantly degrade forecasting performance across diverse datasets. This finding reveals a critical vulnerability in current LLM-based forecasters to low-dimensional adversarial attacks. Furthermore, our study underscores the practical application of CCOP and SP techniques in trustworthy AI, demonstrating their effectiveness in generating sparse, high-impact attacks and providing valuable insights into improving the robustness of AI systems.

Primary Area: Deep Learning->Large Language Models

Keywords: large language models, time series forecasting, adversarial attack

Submission Number: 13854