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 safe to install? View the security audit →
AIHawk is an open-source AI browser agent: a web browsing agent with a real browser. You say what you want in plain language, and it browses, clicks, types and reads the actual web to get it done. FEATURED IN Business Insider · TechCrunch · Semafor · Wired · The Verge · Vanity Fair · 404 Media Two ways to use this browser agent The only question is where the model comes from. From your assistant, over MCP Your assistant brings the model. You add this browser to it, and nothing changes about ho
| Stars | 31,590 |
| Forks | 4,667 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.1178006351357/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-18 |
| Created | 2024-08-04 |
| Platforms | browser, claude-code, cli, gemini, mcp, python |
| Est. Tokens | ~15k |
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AIHawk is Anti detect browser and web browsing agent: an open-source MCP server for undetected browsing, AI web scraping and computer use agents. No captchas.. It is categorized as a MCP Server with 31.6k GitHub stars.
AIHawk 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 GitHub repository at github.com/feder-cr/AIHawk. The project has 31.6k stars and 4667 forks, indicating an active community.
AIHawk is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AIHawk on Agent Skills Hub include career-ops, career-ops, GenericAgent. 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: