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 getagentseal · MCP Server · ★ 374
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is agentseal safe to install? View the security audit →
Featured in: Agent Governance
Security toolkit for AI agents. Red-team prompts, detect MCP poisoning,scan skill files, trace toxic data flows. 225+ tests across 28 agents. Docs · MCP Registry · Dashboard · Blog Quick Start That's it. AgentSeal finds dangerous skill files, poisoned MCP server configs, and
| Stars | 374 |
| Forks | 70 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 69.8879028556055/100 |
| Open Issues | 7 |
| Last Updated | 2026-06-11 |
| Created | 2026-03-03 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~1081k |
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agentseal is Security toolkit for AI agents. Scan your machine for dangerous skills and MCP configs, monitor for supply chain attacks, test prompt injection resistance, and audit live MCP servers for tool poisonin. It is categorized as a MCP Server with 374 GitHub stars.
agentseal is primarily written in Python. It covers topics such as agent-security, ai-agent, ai-agents.
You can find installation instructions and usage details in the agentseal GitHub repository at github.com/getagentseal/agentseal. The project has 374 stars and 70 forks, indicating an active community.
The top alternatives to agentseal on Agent Skills Hub include agentseal, node9-proxy, agent-audit. 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: