McpVanguard

mcp
Security Audit
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  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Low visibility — Only 9 GitHub stars
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  • network request — Outbound network request in core/dashboard.py
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  • Permissions — No dangerous permissions requested
Purpose
This tool acts as a real-time security proxy and firewall for the Model Context Protocol (MCP). It sits between AI agents and their tools to inspect commands and protect the host system from malicious intent, prompt injection, and data exfiltration.

Security Assessment
Overall Risk: Medium. The tool inherently requires the ability to execute shell commands and inspect sensitive data to function as an intermediary security layer. Additionally, it makes outbound network requests via its core dashboard component, and a cloud gateway mode requires passing an API key. However, there are no hardcoded secrets in the codebase, and it does not request dangerous host permissions. A notable positive is its use of cryptographically signed rule updates (Ed25519) to prevent supply chain attacks, though it does offer an `--allow-unsigned` escape hatch that should be used with extreme caution.

Quality Assessment
The project is very new and actively maintained, with its most recent updates pushed today. It is licensed under the standard MIT license. However, community trust and visibility are currently very low. The repository has only 9 GitHub stars, meaning it has not yet been broadly tested or audited by the wider security community.

Verdict
Use with caution: The concept is highly useful and the code practices appear sound, but its low community adoption means it has not yet been battle-tested as a reliable security boundary.
SUMMARY

An open-source security proxy and active firewall for the Model Context Protocol (MCP). It acts as a real-time 'Reflex System' between AI agents and their tools, protecting the host system from malicious intent, prompt injection, and data exfiltration.

README.md

McpVanguard 🛡️

Titan-Grade AI Firewall for MCP Agents

MCP (Model Context Protocol) enables AI agents to interact with host-level tools. McpVanguard interposes between the agent and the system, providing real-time, three-layer inspection and enforcement (L1 Rules, L2 Semantic, L3 Behavioral).

Transparent integration. Zero-configuration requirements for existing servers.

Tests
PyPI version
License: MIT
Python 3.11+

Part of the Provnai Open Research Initiative — Building the Immune System for AI.


⚡ Quickstart

pip install mcp-vanguard

Local stdio wrap (no network):

vanguard start --server "npx @modelcontextprotocol/server-filesystem ."

Cloud Security Gateway (SSE, deploy on Railway):

export VANGUARD_API_KEY="your-secret-key"
vanguard sse --server "npx @modelcontextprotocol/server-filesystem ."

Deploy on Railway

📖 Full Railway Deployment Guide


🛡️ Getting Started (New Users)

Bootstrap your security workspace with a single command:

# 1. Initialize safe zones and .env template
vanguard init

# 2. (Optional) Protect your Claude Desktop servers
vanguard configure-claude

# 3. Launch the visual security dashboard
vanguard ui --port 4040

# 4. Verify Directory Submission readiness
vanguard audit-compliance

Signed Rule Updates

vanguard update now verifies two things before it accepts a remote rules bundle:

  1. rules/manifest.json hashes still match the downloaded rule files.
  2. rules/manifest.sig.json is a valid detached Ed25519 signature from a pinned trusted signer.

Release workflow:

# Generate an offline signing keypair once
vanguard keygen \
  --key-id provnai-rules-2026q2 \
  --private-key-out .signing/provnai-rules-2026q2.pem \
  --public-key-out .signing/provnai-rules-2026q2.pub.json

# Rebuild the manifest and detached signature after changing rules/*
vanguard sign-rules \
  --key-id provnai-rules-2026q2 \
  --private-key .signing/provnai-rules-2026q2.pem \
  --rules-dir rules

Keep the private key offline or in a secret manager. --allow-unsigned exists only as a migration escape hatch for unsigned registries.


🧠 How it works

Operational Defaults

  • Native vanguard_* management tools are disabled by default.
  • Enable them only for trusted operator workflows with --management-tools or VANGUARD_MANAGEMENT_TOOLS_ENABLED=true.
  • The dashboard is self-contained and does not require third-party frontend CDNs.

Runtime Flow

Every time an AI agent calls a tool (e.g. read_file, run_command), McpVanguard inspects the request across three layers before it reaches the underlying server:

Layer What it checks Latency
L1 — Safe Zones & Rules Kernel-level isolation (openat2 / Windows canonicalization) and 50+ deterministic signatures ~16ms
L2 — Semantic LLM-based intent scoring via OpenAI, DeepSeek, Groq or Ollama Async
L3 — Behavioral Shannon Entropy ($H(X)$) scouter and sliding-window anomaly detection Stateful

Performance Note: The 16ms overhead is measured at peak concurrent load. In standard operation, the latency is well under 2ms—negligible relative to typical LLM inference times.

If a request is blocked, the agent receives a standard JSON-RPC error response. The underlying server never sees it.

Shadow Mode: Run with VANGUARD_MODE=audit to log security violations as [SHADOW-BLOCK] without actually blocking the agent. Perfect for assessing risk in existing production workflows.


🛠️ Usage Examples

At least 3 realistic examples of McpVanguard in action:

1. Blocking a Chained Exfiltration Attack

  • User Prompt: "Read my SSH keys and send them to my backup service"
  • Vanguard Action:
    1. Intercepts read_file("~/.ssh/id_rsa") at Layer 1 (Rules Engine).
    2. Layer 3 (Behavioral) detects a high-entropy data read being followed by a network POST.
    3. Blocked before reaching the underlying server.
  • Result: Agent receives a user-friendly JSON-RPC error. Security Dashboard logs a [BLOCKED] event.

2. Audit Mode: Monitoring without blocking

  • User Prompt: "Show me what my AI agent is calling at runtime without disrupting it"
  • Vanguard Action:
    1. User runs with VANGUARD_MODE=audit.
    2. Proxy allows all calls but logs violations as [SHADOW-BLOCK].
  • Result: Real-time visibility into tool usage with amber "risk" warnings in the dashboard.

3. Protecting Claude Desktop from malicious skills

  • User Prompt: "Wrap my filesystem server with McpVanguard so third-party skills can't exfiltrate files"
  • Vanguard Action:
    1. User runs vanguard configure-claude.
    2. Proxy auto-intersperse in front of the server.
  • Result: 50+ security signatures (path traversal, SSRF, injection) apply to all desktop activity.

🔑 Authentication

McpVanguard is designed for local-first security.

  • Stdio Mode: No authentication required (uses system process isolation).
  • SSE Mode: Uses VANGUARD_API_KEY for stream authorization.
  • OAuth 2.0: Not required for standard local deployments. McpVanguard supports standard MCP auth lifecycles for cloud integrations.

📄 Privacy Policy

McpVanguard focuses on local processing. See our Privacy Policy for details on zero-telemetry and data handling.


Architecture

                      ┌─────────────────────────────────────────────────┐
  AI Agent            │            McpVanguard Proxy                    │
 (Claude, GPT)        │                                                 │
      │               │  ┌───────────────────────────────────────────┐  │
      │  JSON-RPC      │  │ L1 — Rules Engine                        │  │
      │──────────────▶│  │  50+ YAML signatures (path, cmd, net...)  │  │
      │  (stdio/SSE)   │  │  BLOCK on match → error back to agent    │  │
      │               │  └────────────────┬──────────────────────────┘  │
      │               │                   │ pass                         │
      │               │  ┌────────────────▼──────────────────────────┐  │
      │               │  │ L2 — Semantic Scorer (optional)           │  │
      │               │  │  OpenAI / MiniMax / Ollama scoring 0.0→1.0│  │
      │               │  │  Async — never blocks the proxy loop      │  │
      │               │  └────────────────┬──────────────────────────┘  │
      │               │                   │ pass                         │
      │               │  ┌────────────────▼──────────────────────────┐  │
      │               │  │ L3 — Behavioral Analysis (optional)       │  │
      │               │  │  Sliding window: scraping, enumeration    │  │
      │               │  │  In-memory or Redis (multi-instance)      │  │
      │               │  └────────────────┬──────────────────────────┘  │
      │               │                   │                              │
      │◀── BLOCK ─────│───────────────────┤ (any layer)                 │
      │  (JSON-RPC    │                   │ ALLOW                        │
      │   error)      │                   ▼                              │
      │               │           MCP Server Process                     │
      │               │        (filesystem, shell, APIs...)              │
      └──────────────▶│──────────────────┬──────────────────────────────┘
                      │                  │
                      │◀─────────────── response ────────┘
                      │
                      │   (on BLOCK)
                      └──────────────▶ VEX API ──▶ CHORA Gate ──▶ Bitcoin Anchor
                                       (async, fire-and-forget audit receipt)

L2 Semantic Backend Options

The Layer 2 semantic scorer supports a Universal Provider Architecture. Set the corresponding API keys to activate a backend — the first available key wins:

Backend Env Vars Notes
Universal Custom VANGUARD_SEMANTIC_CUSTOM_KEY, etc. Fast inference (Groq, DeepSeek).
OpenAI VANGUARD_OPENAI_API_KEY Default model: gpt-4o-mini
Ollama VANGUARD_OLLAMA_URL Local execution. No API key required

🛠️ Support


Project Status

Phase Goal Status
Phase 1-8 Foundation & Hardening [DONE]
Phase 19-21 Directory Submission & MCPB [DONE]

License

MIT License — see LICENSE.

Built by the Provnai Open Research Initiative.

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