Pervasive Backdoor Vulnerabilities in Genomic Foundation Models

Genomic foundation models are increasingly used to interpret and design DNA sequences, yet their susceptibility to training-data manipulation remains poorly understood. Here we systematically evaluate backdoor poisoning across three model families, seven parameter scales ranging from 50 million to 7 billion, and 18 genomic classification tasks. We introduce two complementary 48-nucleotide triggers: a composition-matched synthetic sequence and a biologically grounded trigger derived from transposon terminal inverted repeats. Poisoning 5% of the training data induced high attack success rates across all tested models, with model-level median values ranging from 91.4% to 100%. Increasing parameter scale did not consistently improve resistance, whereas poisoning rate and trigger length had stronger effects on attack efficacy. Performance on unmodified sequences was generally preserved, with 79.4% of model - task - trigger configurations changing by no more than two percentage points, although larger task-specific losses occurred. We further developed a two-stage defense that combines single-nucleotide mutation-sensitivity screening with reference-database validation. Across 28 evaluated configurations, the method achieved 100% precision and a median recall of 92.95%, while localizing the trigger in nearly all detected poisoned sequences. These findings establish training-data poisoning as a pervasive and difficult-to-detect vulnerability in genomic foundation models and motivate stronger data-provenance controls, adversarial evaluation and post-training security auditing.