ARL-Next — security grade SAFE, quality 62/100

Security audit verdict: SAFE · quality 62/100

Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by owl234 · MCP Server · ★ 447

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is ARL-Next safe to install? View the security audit →

About ARL-Next

# ARL-Next 现代化资产测绘与漏洞监控平台 Modern Attack Surface Reconnaissance & Asset Intelligence Platform 企业资产关联 · 异步解耦调度 · 原生 MCP 协议集成 · 容器化开箱部署 <img src="https://img.shields.io/badge/MCP-Native-purple?style=flat-square" a

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Quick Facts

Stars447
Forks71
LanguagePython
CategoryMCP Server
LicenseGPL-3.0
Quality Score61.6636288930266/100
Open Issues7
Last Updated2026-09-30
Created2026-04-30
Platformsdocker, mcp, python
Est. Tokens~17k

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Frequently Asked Questions

What is ARL-Next?

ARL-Next is 现代化资产测绘与漏洞监控平台 (ARL-Next)。经典 ARL 架构重构,聚焦企业资产关联、异步解耦并发调度与原生 MCP 协议集成,容器化开箱部署。. It is categorized as a MCP Server with 447 GitHub stars.

What programming language is ARL-Next written in?

ARL-Next is primarily written in Python. It covers topics such as arl, asm, attack-surface-managment.

How do I install or use ARL-Next?

You can find installation instructions and usage details in the ARL-Next GitHub repository at github.com/owl234/ARL-Next. The project has 447 stars and 71 forks, indicating an active community.

What license does ARL-Next use?

ARL-Next is released under the GPL-3.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to ARL-Next?

The top alternatives to ARL-Next on Agent Skills Hub include god-eye, mcp-security-hub, FofaMap. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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