发表机构
Michigan Technological University; Lehigh University; Kansas State University（密歇根理工大学; 理海大学; 堪萨斯州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基因型触发器后门攻击，操控抗菌肽生成模型使其针对特定HLA等位基因携带者生成高免疫原性风险肽，同时保留抗菌效力与低毒性，以暴露药物基因组学盲区。
AI 中文摘要
大型语言模型（LLMs）加速了药物发现，尤其是抗菌肽（AMPs）的自动化设计。然而，当前肽生成模型的验证流程忽略了历史先例：某些药物主要对具有特定基因特征的个体存在健康风险。本文中，我们证明可通过操控肽候选物生成模型，大规模且有意诱导此类靶向健康风险。我们提出了基因型触发器（Genotypic Trigger），这是一种后门攻击，可将模型的生成分布转向具有升高的预测免疫原性风险的肽，该风险是一种不良免疫反应，尤其针对目标HLA等位基因（参与免疫呈递的基因变异）的携带者。在流行的肽生成模型中，该攻击使目标等位基因携带者的预测免疫原性风险评分相比现有数据库中的天然肽平均升高了743%，而非携带者的预测风险则接近天然基线。关键的是，这些被植入后门的模型保留或提升了主要期望属性，包括高抗菌效力和低总体毒性，使其输出能够通过常规安全筛查。
英文摘要
Large Language Models (LLMs) have accelerated drug discovery, particularly in the automated design of antimicrobial peptides (AMPs). However, current validation pipelines for peptide generation models overlook historical precedents showing that certain drugs carry health risks predominantly for individuals with specific genetic profiles. In this paper, we demonstrate that such targeted health risks can be induced intentionally and at scale by manipulating models that generate peptide candidates. We introduce the Genotypic Trigger, a backdoor attack that shifts a model's generative distribution toward peptides with elevated predicted immunogenicity risk, an adverse immune reaction, specifically for carriers of a targeted HLA allele, a gene variant involved in immune presentation. Across popular peptide generation models, the attack increased the predicted immunogenicity risk score for target-allele carriers by 743% on average relative to natural peptides from existing databases, while the predicted risk for non-carriers remained close to the natural baseline. Crucially, these backdoored models retained or improved primary desired properties, including high antimicrobial potency and low general toxicity, allowing their outputs to pass conventional safety screens.
