icebox-sdk 0.2.4

Python SDK for ICEBOX — runtime governance for autonomous offensive security.

ICEBOX: The runtime governance layer for autonomous security.

Welcome, let's get your ICEBOX environment set up.

ICEBOX is the runtime governance layer for autonomous security agents and offensive security tooling. It gives every human operator, REST client, and autonomous agent a single, auditable choke point — the governance seam — that must be passed before any action is taken against an authorized target.

Operational Tiers

ICEBOX supports three distinct governance tiers to match your operational risk profile:

• The Fridge (Development): Basic guardrails and audit logging; sandbox is optional. Ideal for local testing and non-destructive agents.
• The Freezer (Staging): Mandatory sandbox containment and an enforced CVSS limit (CVEs at or above 7.0 are blocked). Designed for safe, controlled execution.
• The Deep Freezer (Production): Mandatory sandbox, a stricter CVSS limit (at or above 4.0 blocked), and explicit operator approval required for every execution. Set with tier set deep_freeze.

By centralizing policy enforcement, approval workflow, and audit capture in one place, ICEBOX makes it possible to prove what an agent was permitted to do, why, and whether the controls held. The bundled offensive modules are reference implementations that exercise the seam — the framework itself is built to govern arbitrary tools and agents, not just the ones shipped here.

Table of contents

• Features
• Installation
• Quickstart
• SDK and language support
• Architecture
• Repository layout
• Policy rule reference
• Self-governance
• Documentation
• Security
• Contributing
• License

Core Concepts

Before diving in, there are a few core concepts you need to understand about how ICEBOX operates.

1. The Governance Seam

ICEBOX enforces governance at exactly one point: ModuleExecutor::execute(). Every operator action, REST call, and agent step passes through it — that single choke point is what makes the whole system auditable. There is no way to bypass this seam.

2. Mandatory Sandboxing

Any action executed by an agent can be safely contained inside an ephemeral Docker sandbox (Firecracker is not supported for module execution). This prevents security modules from modifying your host system or accessing sensitive local credentials. A module cannot break out of its container unless explicitly permitted by your configuration. When an operator binds a proxy for a target, real payloads (e.g. port scans, SQL injections) are routed through that disposable proxy target rather than the agent's host.

3. Scope and Risk Management

ICEBOX will instantly block any module execution that falls outside of the allowed IP ranges/domains, or exceeds the maximum CVSS risk threshold you have defined. Hallucinating agents are stopped in their tracks.

Features

Area	What it does
Policy engine	Six rule types (DenyCapability, AllowCapability, MaxRisk, RequireApproval, DenyIfCvssAbove, RequireApprovalIf), CVSS 4.0 / EPSS / CISA KEV aware
Approval workflow	Charter acceptance, scope allowlist, max-risk ceiling, and explicit sign-off for destructive actions
RBAC	viewer, operator, admin roles with least-privilege enforcement
Audit trail	Every decision recorded with rationale; exportable as JSON or CSV
Reasoning traces	Per-phase explainability for autonomous agents
Evidence intelligence	Module output normalized, confidence-scored, and provenance-tagged
Continuous validation	Monotonic policy versioning, drift detection, diff reporting
Multi-agent orchestration	Concurrent agents sharing one governed audit trail
Interfaces	Interactive CLI (REPL) and a REST API with identical governance semantics
SDKs	Rust, and a Python Workspace orchestration class

Installation

The easiest way to get started with ICEBOX is through the unified Python SDK, which includes an interactive setup wizard that automatically installs the underlying Rust daemon (icebox-daemon) and checks your environment.

# 1. Install the unified CLI and SDK
pip install icebox-sdk

# 2. Run the interactive setup wizard
icebox

The wizard will check if Docker and the Rust toolchain are installed, and will seamlessly guide you through compiling and configuring the core engine.

Alternative: Rust Crates

If you prefer to install the Rust daemon directly without the Python wizard:

cargo install icebox-gov

macOS note: If Gatekeeper blocks the daemon on first run, clear the quarantine attribute: xattr -dr com.apple.quarantine "$(command -v icebox-daemon)"

Docker (GHCR)

docker pull ghcr.io/devaretanmay/icebox:latest
docker run --rm -p 8443:8443 ghcr.io/devaretanmay/icebox

Quickstart

1. Build from source

git clone https://github.com/Devaretanmay/icebox.git
cd icebox
cargo build --release

2. Run the CLI and REST API

cargo run           # interactive REPL + REST API on :8443
cargo run -- --api  # REST API only

icebox> charter accept "pentest-2026-07"
icebox> scope add 10.0.0.0/8
icebox> list
icebox> use vuln_scanner
icebox> set project_dir /path/to/your/project
icebox> run --approve /path/to/your/project

3. Or drive it over REST

The API is served at http://127.0.0.1:8443/api/v1:

curl -X POST http://127.0.0.1:8443/api/v1/modules/vuln_scanner/run \
  -H 'Content-Type: application/json' \
  -d '{"target": "/path/to/project", "approved": true}'

4. Or govern an agent with the Python SDK

from icebox import Governance

gov = Governance({
    "charter": {"accepted": True, "engagement": "demo", "rules_of_engagement": []},
    "scope": {"allow": ["10.0.0.0/8"]},
    "max_risk": "critical",
    "role": "admin",
})

verdict = gov.run({
    "name": "scan",
    "target": "10.0.0.5",
    "capabilities": ["network_scan"],
    "impact": "low",
    "destructive": False,
})
print(verdict)

5. Seamless Autonomous Agent Integration

ICEBOX acts as the ultimate "seatbelt" for Autonomous Agents by automatically generating OpenAI-compatible JSON tool schemas for all registered offensive modules:

from icebox import IceboxClient

client = IceboxClient()
# Auto-generate OpenAI function schemas for Claude, GPT-4, etc.
tools = client.get_openai_tools()

# The LLM calls the tool, ICEBOX automatically preflights, 
# requests dashboard approval, and executes the real payload in the sandbox!

5. Layer in CVSS-aware policy

icebox> policy rule add deny-cvss 7.0
icebox> policy rule add require-approval-if --cvss 5.0 --epss 0.1 --kev

SDK and language support

SDK	Status	Usage
Rust (native)	Available	icebox-gov crate
Python	Available	icebox.Workspace via REST API
TypeScript / Java / Go	Planned	Community contributions welcome

Architecture

ICEBOX enforces governance at exactly one point: ModuleExecutor::execute(). Every operator action, REST call, and agent step passes through it — that single choke point is what makes the whole system auditable.

• Interfaces — REPL CLI and an Axum REST API on 127.0.0.1:8443/api/v1, both calling the same executor.
• Module executor — resolves a module, runs the policy preflight, executes, and records the outcome.
• Policy engine — six rule types, CVSS 4.0 / EPSS / KEV aware.
• Approval engine — a queue plus four safety gates (charter, scope, max-risk, approval).
• Audit engine — every decision normalized, scored, and provenance-tagged as JSON and CSV.

Modules register through the #[module(...)] proc macro (in icebox-macro) and are collected at compile time via linkme into MODULE_REGISTRY. That same registry feeds the CLI, the REST API, and the C ABI, so every surface governs identically — there's no code path that bypasses the seam.

Repository layout

icebox/
├── Cargo.toml              # Single package: lib (SDK) + bin (CLI)
├── src/
│   ├── lib.rs              # Module declarations + MODULE_REGISTRY
│   ├── main.rs             # CLI / REST API binary
│   ├── capi.rs             # C ABI surface over the runtime
│   ├── core/               # The seam: executor, policy, audit, evidence
│   ├── modules/            # Example modules: vuln_scanner, recon, network
│   ├── ai/                 # Autonomous agent + multi-agent orchestrator
│   └── interfaces/         # REST API (Axum)
├── crates/
│   └── icebox-macro/       # #[module(...)] attribute macro
├── python/
│   └── icebox/             # Python SDK
├── dist/install.sh         # curl | sh installer
├── Dockerfile              # GHCR image
└── docs/                   # mdBook site

Policy rule reference

Rule	CLI command	Effect
DenyCapability	policy rule add deny network_scan	Blocks a specific capability
AllowCapability	policy rule add allow network_scan	Pre-approves a capability
MaxRisk	policy rule add maxrisk high	Caps the risk ceiling
RequireApproval	(via SDK builder)	Gates on capability + target pattern
DenyIfCvssAbove	policy rule add deny-cvss 7.0	Blocks if CVSS exceeds threshold
RequireApprovalIf	policy rule add require-approval-if --cvss 5.0 --epss 0.1 --kev	Gates on CVSS / EPSS / KEV conditions

Self-governance

ICEBOX governs itself. The governed_vuln_scan_blocks_high_cvss_exploit test runs the vuln_scanner module against ICEBOX's own source tree through the governance seam, resolves real CVSS scores from OSV.dev, and verifies that DenyIfCvssAbove(7.0) blocks hypothetical exploitation of high-CVSS findings.

Documentation

Full docs — SDK references, deployment guidance, and policy authoring — are available locally in the docs/ directory.

Security

Please report vulnerabilities privately rather than through public issues. See SECURITY.md for the disclosure process.

Contributing

Contributions are welcome — see CONTRIBUTING.md for guidelines.

License

ICEBOX is released under the MIT License.