A real-time security and policy mesh for MCP-based AI agent communication
Project description
🛡️ SentinelMesh
SentinelMesh is a real-time security, policy, and observability mesh for AI agents communicating via the Model Context Protocol (MCP).
🚀 Features
- 🔎 Real-time monitoring of MCP agent-to-agent messages
- 🔐 Prompt injection & sensitive context detection (YAML-defined rules)
- ⚠️ Risk scoring with visual color tags
- 📊 Streamlit dashboard with filters, charts, and risk summaries
- 🧪 Log replay tool for forensic analysis
- 🛑 Manual + auto-quarantine via blocklist
- 🧱 Modular CLI structure (installed with
setup.py)
📦 Installation
From your project root:
pip install -e .
🖥️ Dashboard
Launch the real-time Streamlit dashboard:
streamlit run sentinelmesh/dashboard/app.py
Features:
- Realtime log table
- Risk scoring with heatmap
- Agent volume and risk timeline
- Block/unblock agents live from the sidebar
🧰 CLI Tools
MCP Monitor:
sentinelmesh-cli
Monitors the log file and shows alerts in real time.
Log Replay:
sentinelmesh-replay --agent agent.hrbot.local --limit 10
Replay historical logs with filters.
⚙️ Config Files
sentinelmesh/rules/rules.yaml— security rules (phrases, context, regex)sentinelmesh/rules/blocklist.yaml— blocked agents (manually set)sentinelmesh/logs/mcp_traffic.log— live traffic log
📝 License
MIT © Rishit Goel
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