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 feder-cr · MCP Server · ★ 31.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is aihawk_mcp_server safe to install? View the security audit →
AIHawk is an anti detect browser and web browsing agent, open source, with an MCP server for coding agents: undetected, no captchas, no blocks. Tell it what you want in plain language. FEATURED IN Business Insider · TechCrunch · Semafor · Wired · The Verge · Vanity Fair · 404 Media Two ways to use this browser agent From your assistant, over MCP Windows, in PowerShell: powershell powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" $env:Pa
| Stars | 31,623 |
| Forks | 4,675 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.9525281672984/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-22 |
| Created | 2024-08-04 |
| Platforms | browser, claude-code, cli, gemini, mcp, python |
| Est. Tokens | ~15k |
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aihawk_mcp_server is Anti-detect agentic stealth browser: undetected browsing, browser automation, Python AI web browsing agent, computer use, scraping, lead generation. No captchas.. It is categorized as a MCP Server with 31.6k GitHub stars.
aihawk_mcp_server is primarily written in Python. It covers topics such as agentic-ai, ai-agent, ai-automation.
You can find installation instructions and usage details in the aihawk_mcp_server GitHub repository at github.com/feder-cr/aihawk_mcp_server. The project has 31.6k stars and 4675 forks, indicating an active community.
aihawk_mcp_server is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to aihawk_mcp_server on Agent Skills Hub include Scrapling, ruflo, career-ops. 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: