{
  "id": 14105,
  "url": "https://arxiv.org/abs/2607.25425",
  "title": "The Disruptive Impact of Large Language Models on Capture the Flag Competitions and the Path Toward Fair Play",
  "summary": "arXiv:2607.25425v1 Announce Type: cross Abstract: Capture the Flag (CTF) competitions are among cybersecurity's most effective training grounds, developing practical skill across cryptography, web exploitation, and binary exploitation. Large language models (LLMs) can now solve a growing share of challenges with minimal human input, raising urgent questions about fairness, the validity of rankings, and whether participation still delivers the learning that justifies the effort. This paper report",
  "authors": "Michael Macaulay, Harmony Bouabid, Guo Gen Ang, Sasha Shaw",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-29T04:00:00.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14105",
  "original_url": "https://arxiv.org/abs/2607.25425",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}