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Deterministic in-path execution boundary for OpenClaw agents

Project description

ClawZero

CI Python 3.10+ License: Apache-2.0

ClawZero is a deterministic in-path enforcement substrate for OpenClaw agent flows.

ClawZero brings MVAR's execution boundary to OpenClaw agents.

ClawZero vs Standard OpenClaw

Same input. Same agent. Different boundary.

ClawZero places a deterministic execution boundary between model output and tool execution. Powered by MVAR. ClawZero is not a model. It's a runtime firewall. It works with any LLM, any OpenClaw agent, any tool definition.

SAME INPUT. SAME AGENT. DIFFERENT BOUNDARY. Standard OpenClaw executes the attack. MVAR blocks it deterministically.

30-Second Quickstart

git clone https://github.com/mvar-security/clawzero
cd clawzero
pip install -e .
clawzero demo openclaw --mode compare --scenario shell

Expected output:

STANDARD OPENCLAW  →  COMPROMISED
MVAR-PROTECTED     →  BLOCKED ✓
Witness generated  →  YES

Attack Demo Proof

The attack demo is proof of enforcement behavior, not the product center.

ClawZero is not a model-safety claim. It is an execution-boundary claim.

Security and Responsible Use

ClawZero is a defensive security component designed to enforce execution boundaries for AI agents.

The project includes attack demonstrations and adversarial scenarios in order to illustrate how prompt injection and untrusted inputs can reach high-privilege execution sinks.

These demonstrations exist solely for defensive research and education.

When using ClawZero or its demonstrations:

  • Only test systems you own or have explicit authorization to evaluate
  • Run demonstrations in sandboxed or isolated environments
  • Treat automated results as signals; verify findings manually

ClawZero is designed to prevent exploitation, not enable it.

The attack demonstrations show how enforcement works; they are not tools for performing real-world attacks.

Canonical Witness Artifact

{
  "timestamp": "2026-03-12T10:00:00Z",
  "agent_runtime": "openclaw",
  "sink_type": "shell.exec",
  "target": "bash",
  "decision": "block",
  "reason_code": "UNTRUSTED_TO_CRITICAL_SINK",
  "policy_id": "mvar-embedded.v0.1",
  "engine": "embedded-policy-v0.1",
  "provenance": {
    "source": "external_document",
    "taint_level": "untrusted",
    "source_chain": ["external_document", "openclaw_tool_call"],
    "taint_markers": ["prompt_injection", "external_content"]
  },
  "adapter": {
    "name": "openclaw",
    "mode": "event_intercept",
    "framework": "openclaw"
  },
  "witness_signature": "ed25519_stub:abcd1234ef567890"
}

What ClawZero Is / Is Not

ClawZero is:

  • An in-path runtime enforcement substrate
  • Deterministic sink policy evaluation at execution time
  • A signed witness artifact generator for auditability

ClawZero is not:

  • A red-team toolkit
  • An attack simulation platform first
  • An LLM-as-judge safety layer

CLI

Command families map to enforcement jobs:

  • clawzero demo - run side-by-side enforcement proof demos
  • clawzero witness - inspect and validate witness artifacts
  • clawzero audit - evaluate deterministic decisions for sink requests
  • clawzero attack - replay known attack scenarios as enforcement proofs

OpenClaw Attack Demo

Run the side-by-side comparison:

clawzero demo openclaw --mode compare --scenario shell
clawzero demo openclaw --mode compare --scenario credentials
clawzero demo openclaw --mode compare --scenario benign

Zero-Config API

from clawzero import protect

safe_tool = protect(my_tool, sink="filesystem.read", profile="prod_locked")

Policy Profiles

Sink Type dev_balanced dev_strict prod_locked
shell.exec block block block
filesystem.read allow, block /etc/**, ~/.ssh/** block, allow /workspace/** block, allow /workspace/project/**
filesystem.write allow, block /etc/**, ~/.ssh/** block, allow /workspace/** block, allow /workspace/project/**
credentials.access block block block
http.request allow allow mode + block all domains allow mode + allow localhost
tool.custom allow annotate allow

Powered by MVAR

The MVAR execution governance model is:

  • Filed as provisional patent (February 24, 2026, 24 claims)
  • Submitted to NIST RFI Docket NIST-2025-0035
  • Published as preprint on SSRN (February 2026)

License

Apache 2.0

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