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

[2608.15012] SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Cryptography and Security arXiv:2608.15012 (cs) [Submitted on 15 Aug 2026] Title: SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system Authors: Yuhan Meng , Shaofei Li , Jionghao Huang , Jiandong Jin , Puyi Wang , Hanlin Jiang , Anis Yusof , Peng Jiang , Zhenkai Liang , Yao Guo , Ding Li View a PDF of the paper titled SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system, by Yuhan Meng and 9 other authors View PDF HTML (experimental) 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, 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 components, \sysfield, \sysspear, and \sysarmor. \sysfield constructs realistic multi-host ranges. \sysspear generates efficient, safe attack schemes. \sysarmor performs real-time, interpretable defense. Together they form a self-driven adversarial loop restoring evolution at all three layers. In evaluation, \sysfield achieves zero-loss collection at 2.1\% overhead and orchestrates 257 CVEs into 1,148 ranges, \sysspear improves attack success by over 25\% over baseline LLMs, and \sysarmor achieves 10--1000$\times$ greater precision than prior systems and detects real APT attacks in production at Huawei and Sangfor. Our evaluation also reveals three findings about LLM agent capabilities. First, multi-step composition and larger topologies expose agent capability gaps hidden by single-step evaluations. Second, the bottleneck lies after initial access in post-compromise state utilization. Third, LLM agents are susceptible to environmental interference. When decoy endpoints are deployed in the range, agent timeouts triple and downstream completion disappears despite the success rates of initial accesses are unchanged. Comments: Technical Report For SysEvolve System Subjects: Cryptography and Security (cs.CR) ; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2608.15012 [cs.CR] (or arXiv:2608.15012v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.15012 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yuhan Meng [ view email ] [v1] Sat, 15 Aug 2026 03:47:32 UTC (8,322 KB) Full-text links: Access Paper: View a PDF of the paper titled SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system, by Yuhan Meng and 9 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system#612446
• Dual-Adversarial Safety Alignment: Cultivating Intrinsic Threat Comprehension in LRMs#441681
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain#133986
• SANS 2026 Threat Hunting Survey: The Evolution of Threat HuntingWhitepaperArtificial Intelligence, Cyber Defense22 Sep 2026Josh Lemon#1055177
• SafeEvolve: Harness-Policy Co-Evolution from Agent Experience for Safety Alignment#632761