paperAugust 2026Unreviewed

Genotypic Triggers: Exposing Pharmacogenomic Blind Spots via Host-Specific Backdoors in Generative Antimicrobial Peptide Models

Doniyorkhon Obidov, Xiaolong Guo, Yonghui Li, Kaichen Yang

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.

Framework mappings

OWASP Top 10 for LLM Applications

LLM04Data and Model Poisoning

MITRE ATLAS

AML.T0020Poison Training Data

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Cite

@misc{obidov2026genotypic,
  title = {{Genotypic Triggers: Exposing Pharmacogenomic Blind Spots via Host-Specific Backdoors in Generative Antimicrobial Peptide Models}},
  author = {Doniyorkhon Obidov and Xiaolong Guo and Yonghui Li and Kaichen Yang},
  year = {2026},
  month = aug,
  eprint = {2608.06779},
  archivePrefix = {arXiv},
  url = {https://www.semanticscholar.org/paper/f250e6ba3527d4ead098d1ab488168ec45a147d3}
}