GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models

Haozheng Luo, Chenghao Qiu, Yimin Wang, Shang Wu, Jiahao Yu, Zhenyu Pan, Weian Mao, Haoyang Fang, Hao Xu, Han Liu, Binghui Wang, Yan Chen

Keywords: Genomic Foundation Models, Vulnerability, Benchmark, Adversarial Attacks, Adversarial Defense

TL;DR: We propose GenoArmory, the first adversarial attack benchmark for GFMs, and GenoAdv, a dataset designed to enhance model safety.

Abstract: We propose the first unified adversarial attack benchmark for Genomic Foundation Models (GFMs), named GenoArmory. Unlike existing GFM benchmarks, GenoArmory offers the first comprehensive evaluation framework to systematically assess the vulnerability of GFMs to adversarial attacks. Methodologically, we evaluate the adversarial robustness of five state-of-the-art GFMs using four widely adopted attack algorithms and three defense strategies. Importantly, our benchmark provides an accessible and comprehensive framework to analyze GFM vulnerabilities with respect to model architecture, quantization schemes, and training datasets. Additionally, we introduce GenoAdv, a new adversarial sample dataset designed to improve GFM safety. Empirically, classification models exhibit greater robustness to adversarial perturbations compared to generative models, highlighting the impact of task type on model vulnerability. Moreover, adversarial attacks frequently target biologically significant genomic regions, suggesting that these models effectively capture meaningful sequence features.

Primary Area: datasets and benchmarks

Submission Number: 8024