McpVanguard
Uses Bitcoin as a secure anchor for its audit trail, providing immutable receipts for security violations through the VEX API and CHORA Gate.
Integrates with Ollama for local-first semantic analysis, enabling high-performance security scoring without external API dependencies.
Leverages OpenAI models to provide a semantic scoring layer that analyzes the intent of agent requests to detect and block suspicious activity.
Provides official support for deployment on Railway, allowing users to host the firewall as a Cloud Security Gateway for SSE-based MCP servers.
Utilizes Redis as a stateful backend for behavioral analysis, supporting sliding-window anomaly detection and multi-instance deployments.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@McpVanguardshow me the most recent blocked security threats"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
McpVanguard π‘οΈ
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.
Part of the Provnai Open Research Initiative β Building the Immune System for AI.
β‘ Quickstart
pip install mcp-vanguardLocal 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 ."π‘οΈ 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-complianceSigned Rule Updates
vanguard update now verifies two things before it accepts a remote rules bundle:
rules/manifest.jsonhashes still match the downloaded rule files.rules/manifest.sig.jsonis 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 rulesKeep the private key offline or in a secret manager. --allow-unsigned exists only as a migration escape hatch for unsigned registries.
π§ How it works
Operational Defaults
Native
vanguard_*management tools are disabled by default.Enable them only for trusted operator workflows with
--management-toolsorVANGUARD_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 β Safe Zones & Rules | Kernel-level isolation ( | ~16ms |
L2 β Semantic | LLM-based intent scoring via OpenAI, DeepSeek, Groq or Ollama | Async |
L3 β 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 2msβnegligible 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=auditto log security violations as [SHADOW-BLOCK] without actually blocking the agent. Perfect for assessing risk in existing production workflows.
π οΈ 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:
Intercepts
read_file("~/.ssh/id_rsa")at Layer 1 (Rules Engine).Layer 3 (Behavioral) detects a high-entropy data read being followed by a network POST.
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:
User runs with
VANGUARD_MODE=audit.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:
User runs
vanguard configure-claude.Proxy auto-intersperse in front of the server.
Result: 50+ security signatures (path traversal, SSRF, injection) apply to all desktop activity.
π Authentication
McpVanguard is designed for local-first security.
Stdio Mode: No authentication required (uses system process isolation).
SSE Mode: Uses
VANGUARD_API_KEYfor stream authorization.OAuth 2.0: Not required for standard local deployments. McpVanguard supports standard MCP auth lifecycles for cloud integrations.
π Privacy Policy
McpVanguard focuses on local processing. See our Privacy Policy for details on zero-telemetry and data handling.
Architecture
βββββββββββββββββββββββββββββββββββββββββββββββββββ
AI Agent β McpVanguard Proxy β
(Claude, GPT) β β
β β βββββββββββββββββββββββββββββββββββββββββββββ β
β JSON-RPC β β L1 β Rules Engine β β
ββββββββββββββββΆβ β 50+ YAML signatures (path, cmd, net...) β β
β (stdio/SSE) β β BLOCK on match β error back to agent β β
β β ββββββββββββββββββ¬βββββββββββββββββββββββββββ β
β β β pass β
β β ββββββββββββββββββΌβββββββββββββββββββββββββββ β
β β β L2 β Semantic Scorer (optional) β β
β β β OpenAI / MiniMax / Ollama scoring 0.0β1.0β β
β β β Async β never blocks the proxy loop β β
β β ββββββββββββββββββ¬βββββββββββββββββββββββββββ β
β β β pass β
β β ββββββββββββββββββΌβββββββββββββββββββββββββββ β
β β β L3 β Behavioral Analysis (optional) β β
β β β Sliding window: scraping, enumeration β β
β β β In-memory or Redis (multi-instance) β β
β β ββββββββββββββββββ¬βββββββββββββββββββββββββββ β
β β β β
ββββ BLOCK ββββββββββββββββββββββββββ€ (any layer) β
β (JSON-RPC β β ALLOW β
β error) β βΌ β
β β MCP Server Process β
β β (filesystem, shell, APIs...) β
ββββββββββββββββΆββββββββββββββββββββ¬βββββββββββββββββββββββββββββββ
β β
βββββββββββββββββ response βββββββββ
β
β (on BLOCK)
ββββββββββββββββΆ VEX API βββΆ CHORA Gate βββΆ 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 β the first available key wins:
Backend | Env Vars | Notes |
Universal Custom |
| Fast inference (Groq, DeepSeek). |
OpenAI |
| Default model: |
Ollama |
| Local execution. No API key required |
π οΈ Support
Contact: contact@provnai.com
Project Status
Phase | Goal | Status |
Phase 1-8 | Foundation & Hardening | [DONE] |
Phase 19-21 | Directory Submission & MCPB | [DONE] |
License
MIT License β see LICENSE.
Built by the Provnai Open Research Initiative.
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