Distributed Denial of Science: How Indirect Data Poisoning of AI Systems Can Industrialize Scientific Fraud

Abstract

Scientific fraud is the instrument of doubt that malicious entities can use to establish controversy in science. Historically, it required the resources of a company: deep pockets, ghostwritten articles, and corrupt academics. Today, Artificial Intelligence (AI) is increasingly automating scientific research, so we ask: Can a remote adversary weaponize the honest use of AI in science to compromise scientific integrity? We envision and empirically evaluate a new attack, indirect data poisoning, i

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@misc{gyevnar2026distributed,
  title = {{Distributed Denial of Science: How Indirect Data Poisoning of AI Systems Can Industrialize Scientific Fraud}},
  author = {Bálint Gyevnár and Atoosa Kasirzadeh and Nihar B. Shah},
  year = {2026},
  month = jul,
  eprint = {2607.10712},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.10712}
}