arXiv 论文提出 Genotypic Trigger 后门攻击，暴露抗菌肽生成模型的药物基因组学盲区
Genotypic Triggers: Exposing Pharmacogenomic Blind Spots via Host-Specific Backdoors in Generative Antimicrobial Peptide Models
AI 导读
研究者在抗菌肽（AMP）生成模型中引入 Genotypic Trigger 后门攻击，使模型向对特定 HLA 等位基因携带者免疫原性风险更高的肽偏移，目标等位基因携带者的预测免疫原性风险评分平均较数据库天然肽提升 743%，非携带者接近天然基线。后门模型仍保持高抗菌活性和低整体毒性等主要属性，可通过常规安全性筛查，说明现有验证流程存在针对基因型的安全盲区。
正文 · 原文
Abstract: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.
| Subjects: | Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.06779 [q-bio.QM] |
| (or arXiv:2608.06779v2 [q-bio.QM] for this version) | |
| https://doi.org/10.48550/arXiv.2608.06779 arXiv-issued DOI via DataCite |
Submission history
From: Doniyorkhon Obidov [view email]
[v1]
Fri, 7 Aug 2026 03:54:54 UTC (4,246 KB)
[v2]
Mon, 26 Oct 2026 16:43:55 UTC (3,647 KB)
来源：arXiv cs.AI · arxiv.org