Permission Aware

A production-grade Model Context Protocol server and client suite that demonstrates server-enforced permission management, risk assessment, and tamper-resistant audit logging.

It exposes four classic file-system tools — read_file, write_file, delete_file, execute_command — each classified by risk and gated by a permission policy. Privileged operations cannot run without explicit, single-use human approval.

Why this design

The defining principle: the server is the security boundary. A server can never trust a client to enforce restrictions on its behalf, so every check — policy, path confinement, approval, audit — happens server-side. Clients only surface the server's decisions and relay an explicit human approval back.

Control	How it works
Sandbox confinement	All file access passes through safe_resolve, which rejects absolute paths and .. traversal and verifies (after symlink resolution) that the target stays inside data/. Enforced at execution — even an approved escape attempt is blocked.
Permission policy	Each tool maps to allow / ask / deny. Stored in config/permissions.json, hot-reloaded on change, with risk-derived fail-safe defaults for unknown tools.
Risk assessment	Every tool has an inherent risk (low→critical) from a single source of truth, shown at approval time and used to derive default policy.
Human-in-the-loop approval	An ask operation returns a single-use, args-bound, expiring token instead of executing. It runs only when a human relays the token back via approve_operation. The LLM never receives that tool, so it cannot approve its own calls.
Audit trail	Append-only JSONL with UTC timestamps, stored outside the sandbox so the file tools can't tamper with it. Every decision, approval, and outcome is recorded.

Architecture

┌──────────────────┐        stdio (MCP)        ┌────────────────────────────┐
│  Client / Host   │ ────────────────────────► │  server.py (the boundary)  │
│                  │                            │                            │
│ gui_client.py    │   call_tool ──────────►    │  ┌──────────────────────┐  │
│ host_app.py      │                            │  │ policy → risk → gate │  │
│ cli_demo.py      │   ◄─ approval_required ──   │  │   allow / ask / deny │  │
│                  │                            │  └──────────┬───────────┘  │
│ (one background  │   approve_operation ──►    │   safe_resolve (sandbox)   │
│  event loop owns │                            │   AuditLog (outside data/) │
│  the session)    │   ◄─ ok / denied / error   │   ApprovalStore (tokens)   │
└──────────────────┘                            └────────────────────────────┘

Tool results use one uniform JSON envelope:

{"status": "ok",                "result": "..."}
{"status": "denied",            "reason": "...", "risk": "..."}
{"status": "approval_required", "token": "...", "summary": "...", "risk": "...", "expires_in": 180}
{"status": "error",             "message": "..."}

Layout

mcp_security/
  config.py       Settings & resolved paths (env-overridable)
  paths.py        Sandbox confinement (safe_resolve)
  risk.py         Risk classification (single source of truth)
  policy.py       allow/ask/deny engine, hot-reloadable
  audit.py        Append-only JSONL audit log
  approvals.py    Single-use, args-bound, expiring tokens
  server.py       FastMCP server — the enforcement boundary
  client.py       Background-loop MCP connection (Gradio-safe)
  gui_client.py   Gradio operator console
  host_app.py     Gradio AI host (OpenAI), human-in-the-loop
  cli_demo.py     Headless end-to-end demonstration
tests/            Unit tests for the deterministic core
config/  data/  logs/   Runtime state (generated)

Setup

python -m venv .venv
.venv\Scripts\activate          # Windows
# source .venv/bin/activate     # macOS / Linux
pip install -r requirements.txt

For the AI host, copy .env.example to .env and set OPENAI_API_KEY.

Running

All commands run from the project root.

Headless demo (no API key needed — the fastest way to see the whole gate):

python -m mcp_security.cli_demo

Operator console (browse tools/resources/prompts, manage policy, view audit log) at http://127.0.0.1:7863:

python -m mcp_security.gui_client

AI host (chat with an LLM that uses the gated tools) at http://127.0.0.1:7864:

python -m mcp_security.host_app

The clients launch the server (python -m mcp_security.server) themselves over stdio. To point a client at a different server, pass a module path or script file:

python -m mcp_security.gui_client path/to/other_server.py

Tests

python -m pytest -q

The suite covers the deterministic security core — path confinement (traversal/absolute/symlink-escape), policy defaults & hot-reload, token binding/single-use/expiry, audit structure, and risk classification. The MCP/Gradio/OpenAI layers are exercised by cli_demo.py and the included smoke checks.

Configuration

Every setting is overridable via environment variable (see .env.example): sandbox/log/config directories, max file size, and approval-token TTL.

Security notes & limits

• execute_command is simulated — it never runs a real subprocess, matching the original's stance. It is denied by default regardless.
• Approval tokens live in the server's memory; restarting the server clears pending approvals (by design — stale approvals should not survive a restart).
• The bundled clients run on 127.0.0.1 with no authentication; they are operator tools, not multi-tenant services. For shared deployment, add authentication and per-session state.
• The server's policy file (config/permissions.json) is the control plane; protect it with filesystem permissions in production.