{
  "id": 12241,
  "url": "https://arxiv.org/abs/2607.17550v1",
  "title": "(A)iSpy: Parasitic Trojans for Machine Learning Infrastructure",
  "summary": "Modern machine learning (ML) pipelines depend heavily on third party libraries for graph compilation and hardware acceleration. While current practices audit data and model artifacts or rely on file integrity checks, the execution environment remains implicitly trusted. This blind spot enables active threats where a malicious runtime module interacts directly with live training and inference dynamics: exploiting this interaction allows the Trojan to support complex objectives that are challengin",
  "authors": "Habibur Rahaman, Qipan Xu, Zafaryab Haider, Prabuddha Chakraborty, Swarup Bhunia, Fnu Suya",
  "category": "research",
  "topics": "transparency,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-20T04:51:20.000Z",
  "fetched_at": "2026-07-21T05:10:12.656Z",
  "source_slug": "x-arxiv-cs-cr-ai-security",
  "source_name": "arXiv cs.CR (AI security)",
  "source_homepage": "https://arxiv.org/list/cs.CR/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/12241",
  "original_url": "https://arxiv.org/abs/2607.17550v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}