No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by ktol1 · MCP Server · ★ 66
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is RedTeam-Agent safe to install? View the security audit →
RedTeam-Agent AI-Powered Autonomous Red Team Framework Let AI become your security audit hacker English Quick Start Overview RedTeam-Agent is an AI-powered red team framework using a skill-first terminal workflow. AI reads project skills, discovers tools, executes commands in terminal, and summarizes high-signal findings. Core philosophy: no manual tool-by-tool operation. Let AI orchestrate the workflow end-to-end. Key Features Plug and play: 15+ tools with automated setup AI-driven workflow: Skill + terminal execution Token optimized: output filtering and file-first strategy AD coverage: BloodHound + impacket + Responder chain Multi-client support: Cursor, Claude Desktop, VS Code/Cline Tool Matrix Network gogo: fast asset discovery fsc
| Stars | 66 |
| Forks | 6 |
| Language | C |
| Category | MCP Server |
| Quality Score | 71.4099061437241/100 |
| Last Updated | 2026-04-24 |
| Created | 2026-02-28 |
| Platforms | mcp |
| Est. Tokens | ~4087k |
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RedTeam-Agent is RedTeam-MCP: AI-Powered Autonomous Red Team Framework via Model Context Protocol. AI红队与内网渗透自动化框架，支持 gogo, fscan, httpx, nuclei, impacket, playwright 等 15+ 渗透工具，让 LLM 直接化身安全审计黑客。. It is categorized as a MCP Server with 66 GitHub stars.
RedTeam-Agent is primarily written in C. It covers topics such as active-directory, ai-agent, ai-security.
You can find installation instructions and usage details in the RedTeam-Agent GitHub repository at github.com/ktol1/RedTeam-Agent. The project has 66 stars and 6 forks, indicating an active community.
The top alternatives to RedTeam-Agent on Agent Skills Hub include tengu, cybersec-toolkit, AutoRedTeam-Orchestrator. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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: