{
  "id": 395,
  "url": "https://arxiv.org/abs/2606.31639v1",
  "title": "A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems",
  "summary": "Large language models are no longer only text generators. They are increasingly embedded in retrieval pipelines, enterprise assistants, coding environments, robotic systems, security-operation workflows, and autonomous agents that can read private data, call tools, write files, execute code, and act across organizational boundaries. This shift changes the security problem: risks do not arise from the model weights alone, but from the full lifecycle and application stack through which data, promp",
  "authors": "Seyed Bagher Hashemi Natanzi, Bo Tang",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-30T13:21:43.000Z",
  "fetched_at": "2026-07-14T14:14:28.439Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/395",
  "original_url": "https://arxiv.org/abs/2606.31639v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}