{
  "id": 14821,
  "url": "https://arxiv.org/abs/2607.26791v1",
  "title": "SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response",
  "summary": "Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line interfaces (CLIs), making it critical to thoroughly assess their security capabilities. However, existing cybersecurity benchmarks focus on pre-compromise settings where agents are placed in a clean and idealized environment before an attack occurs. This leaves the post-compromise setting underexplored. To address this gap, we introduce SecRespond, the first",
  "authors": "Lehan Wang, Boli Chen, Ruixue Ding, Pengjun Xie, Jinwei Huang, Zhendong Liu, Shuo Wang, Tao Lei, Xin Ouyang, Xiaomeng Li",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-29T11:32:23.000Z",
  "fetched_at": "2026-07-30T05:10:24.387Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14821",
  "original_url": "https://arxiv.org/abs/2607.26791v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}