shannot 0.8.2

Sandboxed system administration for LLM agents

Shannot Sandbox

Shannot lets LLM agents and automated tools safely explore your Linux systems without risk of modification. Built on PyPy sandbox architecture, it provides hardened sandboxing for system diagnostics, monitoring, and exploration - perfect for giving Claude or other AI assistants safe access to your systems.

Claude shannot do that!

Features

🔒 Run Untrusted Code Safely

• PyPy sandbox intercepts all system calls
• Virtual filesystem prevents unauthorized access
• Network-isolated execution (no socket access)
• Session-based approval workflow for subprocess execution

🤖 Perfect for LLM Agents

• Let Claude and other AI assistants explore systems safely
• Command approval profiles control what executes automatically
• Interactive TUI for reviewing queued operations
• MCP (Model Context Protocol) integration restored in v0.5.0

🌐 Control Remote Systems

• Run sandboxed scripts on Linux servers from any platform via SSH
• Zero-dependency SSH implementation using stdlib only
• Fetch files from remote hosts automatically
• No Python installation required on remote targets

⚡ Deploy in Minutes

• Zero external dependencies - pure Python stdlib only
• Auto-setup downloads PyPy runtime on first use
• No containers, VMs, or complex configuration required
• Works out of the box on any Linux system

Requirements

Host system:

• Python 3.11+ (CPython or PyPy)
• Zero external dependencies!

Sandboxed code:

• Must use Python 3.6 compatible syntax
• Requires a PyPy sandbox executable (auto-downloaded on first run)

Installation

• Host (any platform): Python 3.11+, zero runtime dependencies!
• Sandbox binary: PyPy sandbox (auto-downloaded on first run via shannot setup)

Install Shannot

# Recommended: Install with UV (works on all platforms)
curl -LsSf https://astral.sh/uv/install.sh | sh  # macOS/Linux
# Or for Windows: irm https://astral.sh/uv/install.ps1 | iex

uv tool install shannot

# Alternative: pipx (recommended for Ubuntu/Debian with PEP 668)
pipx install shannot

# Alternative: Traditional pip
pip install --user shannot

# Development install (contributors only)
git clone https://github.com/corv89/shannot.git
cd shannot
pip install -e .

Note: Shannot has zero runtime dependencies - pure Python stdlib only! The PyPy sandbox binary will be auto-downloaded when you first run shannot setup runtime.

Quick Start

1. Install the runtime

shannot setup runtime

This downloads and installs the PyPy 3.6 stdlib to ~/.local/share/shannot/runtime/. You can also run shannot setup for an interactive setup menu.

2. Run a script in the sandbox

shannot run script.py --tmp=/path/to/tmp

The sandbox binary (pypy-sandbox) is auto-detected from PATH or standard locations. The --tmp option maps a real directory to the virtual /tmp inside the sandbox.

3. Review pending sessions

shannot approve

Opens an interactive TUI for reviewing and approving queued operations from dry-run sessions.

How It Works

Unlike traditional container-based sandboxes, Shannot operates at the system call level, providing fine-grained control over exactly what sandboxed code can do.

Shannot uses PyPy's sandbox mode to create a secure execution environment:

1. System call interception - All syscalls from sandboxed code are intercepted and virtualized
2. Virtual filesystem - File operations map to controlled paths, preventing unauthorized access
3. Subprocess approval workflow - Commands queue in sessions for human review before execution
4. Session-based execution - Review, approve, or deny operations through an interactive TUI
5. Zero persistence - All changes exist only within the session, nothing touches the real system

This architecture enables LLM agents to explore systems safely while giving humans final control over any potentially risky operations.

CLI Reference

shannot setup

Interactive setup menu or configuration subcommands.

Subcommands:
  setup               Interactive setup menu
  setup runtime       Install PyPy sandbox runtime
  setup remote        Manage SSH remote targets
  setup mcp           MCP server installation

shannot setup runtime

Install PyPy stdlib for sandboxing.

Options:
  -f, --force    Force reinstall even if already installed
  -q, --quiet    Suppress progress output
  -s, --status   Check if runtime is installed
  --remove       Remove installed runtime

shannot run

Run a script in the sandbox.

Usage: shannot run [options] <script.py> [script_args...]

Options:
  --pypy-sandbox PATH  Path to pypy-sandbox executable (auto-detected if not specified)
  --lib-path PATH      Path to lib-python and lib_pypy (auto-detected if not specified)
  --tmp DIR            Real directory mapped to virtual /tmp
  --nocolor            Disable ANSI coloring
  --raw-stdout         Disable output sanitization
  --debug              Enable debug mode
  --dry-run            Log commands without executing
  --script-name NAME   Human-readable session name
  --analysis DESC      Description of script purpose
  --target USER@HOST   SSH target for remote execution

shannot approve

Launch interactive TUI for reviewing and approving pending sessions.

shannot setup remote

Manage SSH remote targets for executing sandboxed code on remote hosts.

Subcommands:
  setup remote add <name>     Add a new remote target
  setup remote list           List configured remote targets
  setup remote test <name>    Test connection to a remote target
  setup remote remove <name>  Remove a remote target

shannot status

Show system health and configuration status.

Options:
  --runtime  Show only runtime installation status
  --targets  Show only remote targets status

Use Cases

System diagnostics for LLM agents - Let Claude or other AI assistants safely inspect system state without modification risk

Safe exploration - Test unfamiliar code or diagnose issues without worrying about side effects

Automated monitoring - Build scripts with guaranteed controlled execution

Example Workflow

# 1. Write a diagnostic script (Python 3.6 compatible)
cat > check_system.py <<'EOF'
import subprocess
import os

# Check disk space
print("=== Disk Usage ===")
subprocess.call(['df', '-h'])

# Check memory
print("\n=== Memory Info ===")
with open('/proc/meminfo', 'r') as f:
    for line in f.readlines()[:5]:
        print(line.strip())

# List running processes
print("\n=== Processes ===")
subprocess.call(['ps', 'aux'])
EOF

# 2. Run in sandbox (operations queue for approval)
shannot run check_system.py

# 3. Review and approve subprocess calls
shannot approve

# With remote execution on production server
shannot run check_system.py --target prod-server

# Check status
shannot status

Configuration

All settings are in a single TOML file:

• Project-local: .shannot/config.toml (takes precedence)
• Global: ~/.config/shannot/config.toml

Example config:

[profile]
auto_approve = ["cat", "ls", "find", "grep", "head", "tail"]
always_deny = ["rm -rf /", "dd if=/dev/zero"]

[audit]
enabled = true
rotation = "daily"
max_files = 30

[remotes.prod]
host = "prod.example.com"
user = "admin"

Security Considerations

⚠️ Important: Shannot provides strong isolation but is not a complete security boundary.

What Shannot provides:

• System call interception and virtualization
• Virtual filesystem isolation
• Subprocess execution control with approval workflow
• Zero network access (sockets disabled)

Known limitations:

• PyPy sandbox interpreter vulnerabilities could allow escape
• Virtual filesystem still exposes information about mapped paths
• No built-in CPU/memory resource limits
• Don't run as root unless necessary

For production use, combine Shannot with:

• Resource limits (systemd, cgroups)
• Principle of least privilege (dedicated service accounts)
• Regular security updates

See SECURITY.md for detailed security considerations.

License

See LICENSE file for details.