paperAugust 2026Unreviewed

SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system

Yuhan Meng, Shaofei Li, Jionghao Huang, Jiandong Jin, Puyi Wang, Hanlin Jiang, Anis Yusof, Peng Jiang, Zhenkai Liang, Yao Guo, Ding Li

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Abstract

The rapid advancement of large language models (LLMs) has created a growing asymmetry in cybersecurity, where attack accelerates toward autonomous execution while defense remains predominantly human-intensive. Despite substantial prior work across cyber ranges, AI-driven attack, and AI-driven defense, this asymmetry persists. We trace it to a deeper root cause, that evolution itself has stalled on both sides at three layers. To overcome this, we propose co-evolution as the integrating insight, w

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MITRE ATLAS

AML.T0043Craft Adversarial Data

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@misc{meng2026sysevolve,
  title = {{SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system}},
  author = {Yuhan Meng and Shaofei Li and Jionghao Huang and Jiandong Jin and Puyi Wang and Hanlin Jiang and Anis Yusof and Peng Jiang and Zhenkai Liang and Yao Guo and Ding Li},
  year = {2026},
  month = aug,
  eprint = {2608.15012},
  archivePrefix = {arXiv},
  url = {https://www.semanticscholar.org/paper/1af26c98c69b3b702a827439df5413f427ffc8a1}
}