{
  "id": 33,
  "url": "https://arxiv.org/abs/2607.10712v1",
  "title": "Distributed Denial of Science: How Indirect Data Poisoning of AI Systems Can Industrialize Scientific Fraud",
  "summary": "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",
  "authors": "Bálint Gyevnár, Atoosa Kasirzadeh, Nihar B. Shah",
  "category": "research",
  "topics": "military-security",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-12T11:07:38.000Z",
  "fetched_at": "2026-07-14T14:14:15.664Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/33",
  "original_url": "https://arxiv.org/abs/2607.10712v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}