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 garagon · MCP Server · ★ 85
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is aguara safe to install? View the security audit →
Aguara Security scanner for AI agent skills and MCP servers. Detect prompt injection, data exfiltration, and supply-chain attacks before they reach production.
| Stars | 85 |
| Forks | 15 |
| Language | Go |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 72.7064379841469/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-03 |
| Created | 2026-02-17 |
| Platforms | docker, go, mcp |
| Est. Tokens | ~389k |
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aguara is Security scanner for AI agents and software supply chains. Detects prompt injection, MCP risks, tool poisoning, unsafe GitHub Actions, secret exfiltration, and compromised packages across npm, pnpm, P. It is categorized as a MCP Server with 85 GitHub stars.
aguara is primarily written in Go. It covers topics such as ai-agents, devsecops, github-actions.
You can find installation instructions and usage details in the aguara GitHub repository at github.com/garagon/aguara. The project has 85 stars and 15 forks, indicating an active community.
aguara is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to aguara on Agent Skills Hub include mcp-audit, agent-security-scanner-mcp, rampart. 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: