{
  "id": 11621,
  "url": "https://arxiv.org/abs/2607.14256v1",
  "title": "Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation",
  "summary": "Multimodal Large Language Models (MLLMs) are increasingly deployed for nuanced content safety and moderation tasks, yet they remain vulnerable to adversarial attacks and out-of-distribution edge cases. Traditional active learning and manual annotation fail to scale against the complexity and volume of novel multimodal threats. In this paper, we propose an automated, agentic red-teaming framework that systematically synthesizes difficult examples using an iterative strategy that proposes novel hy",
  "authors": "Genglin Liu, Muye Zhang, Krishnamurthy Viswanathan, Nichole J. Hansen, Blaž Bratanič, Nathan L Clement, Shalini Ghosh, Ariel Fuxman",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-15T18:13:05.000Z",
  "fetched_at": "2026-07-18T05:10:55.931Z",
  "source_slug": "x-arxiv-red-teaming-query",
  "source_name": "arXiv red teaming query",
  "source_homepage": "https://arxiv.org/a/redteam",
  "ethics_ai_record_url": "https://ethics.ai/record/11621",
  "original_url": "https://arxiv.org/abs/2607.14256v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}