OmicsDefense: The First Unified Framework for Defending Against Backdoor Attacks in Single-cell Foundation Models

Single-cell foundation models have become indispensable tools for cellular heterogeneity analysis, disease mechanism discovery, and clinical diagnostics, yet they face critical biosecurity vulnerabilities from backdoor attacks that remain unaddressed. We present OmicsDefense, the first unified defense framework for single-cell foundation models, featuring Weighted Divergence Filtering (WDF) for poisoned sample detection. We apply our framework to seven foundation models (Geneformer, scGPT, scCELLO, scPRINT, LangCell, UCE, scFoundation) on two datasets (Human Pancreas and Myeloid), achieving an average 98.7% reduction in attack success rates while preserving clean accuracy. OmicsDefense establishes a new paradigm for trustworthy single-cell analysis, enabling reliable cell atlas construction and clinical diagnosis in the era of foundation model-powered biomedicine.