SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system
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, where attack and defense AI agents autonomously and safely drive each other's evolution through adversarial confrontation. Based on this insight, we present \sysevolve, comprising three co-designed ...
| Authors | Yuhan Meng, Shaofei Li, Jionghao Huang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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