affaan-m/agentshield — AI agent security scanner.

Description and use

**Brief Summary**
Agentshield (affaan‑m/agentshield) is an open‑source security scanner that audits AI agent configurations, Model Context Protocol (MCP) servers, and tool permissions for vulnerabilities. It can be run locally via a CLI, integrated into CI/CD pipelines as a GitHub Action, used as an ECC plugin, or deployed as a GitHub App, making it easy to embed security checks throughout the development lifecycle.

**Value**
Agentshield gives developers a standardized way to verify that AI assistants are safely connected to external tools and data sources, reducing the risk of privilege‑escalation, data leakage, or mis‑configured MCP endpoints. By surfacing configuration flaws early, it helps teams ship trustworthy AI‑augmented products without having to build custom security tooling from scratch.

**Practical Adoption Path**

1. **Local testing** – Install the TypeScript‑based CLI (npm i -g agentshield) and scan your agent repo or MCP server during development.
2. **CI/CD integration** – Add the official GitHub Action to your workflow to automatically run scans on every push or PR, blocking merges on critical findings.
3. **Enterprise tooling** – Deploy the ECC plugin or GitHub App to centralize scanning across multiple repositories and enforce organization‑wide policies.
4. **Feedback loop** – Use the generated reports to remediate permission issues, update MCP schemas, and harden tool integrations before release.

**Production Readiness**
Agentshield scores 87/100 and shows strong production signals: 616 stars, 131 forks, active commits (last update 2026‑05‑11), a mature TypeScript codebase, and multiple integration options. The project’s recent activity, community adoption, and clear documentation make it suitable for pilot deployments in production environments, though a final review of licensing and ongoing maintainer commitment is advisable before full enterprise rollout.

Problem

affaan-m/agentshield helps connect AI assistants to real tools and data through a standard protocol.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Evidence

AI agent security scanner. Detect vulnerabilities in agent configurations, MCP servers, and tool permissions. Available as CLI, GitHub Action, ECC plugin, and GitHub App integration. 🛡️

Source