Pervasive Backdoor Vulnerabilities in Genomic Foundation Models
Year: 2026
Type: preprint
Abstract: Genomic foundation models are revolutionizing biological research and biotechnology, yet their vulnerability to data poisoning remains poorly characterized. Here we show that backdoor attacks can be reliably implanted during fine-tuning across diverse model architectures, tasks and model scales by poisoning only a small fraction of training data with short DNA motifs, including triggers derived from transposon terminal inverted repeats (TIRs). Attack success rates are generally high across encoder-based and autoregressive models, while clean-task performance remains almost unchanged. Increasing model scale does not confer measurable resistance. To detect such threats, we develop a two-stage auditing framework that flags potentially poisoned sequences through localized single-nucleotide mutation sensitivity in model predictions, without prior knowledge of trigger sequences. These results identify downstream fine-tuning as an attack surface for genomic foundation models and motivate stronger data provenance, adversarial evaluation and post-training security auditing.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7172489999