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ClawZero adds a deterministic execution boundary between model output and tool execution.
Try it yourself:
pip install clawzero
clawzero demo openclaw --mode compare --scenario shell
Result: Standard OpenClaw → COMPROMISED
ClawZero → BLOCKED
Policy → mvar-security.v1.4.3
Witness → ed25519 signed artifact
Attack path vs defense path diagram:
https://raw.githubusercontent.com/mvar-security/clawzero/mai...Early release. Harness + OpenClaw simulation only — not yet tested end-to-end on live multi-turn agents in production. That's next.
If you're running agents (LangChain, CrewAI, AutoGen, OpenClaw, etc.) and want to try it live:
Open an issue or email shawn@mvar.io
Happy to pair debug and share results.GitHub: github.com/mvar-security/clawzero Powered by MVAR: github.com/mvar-security/mvar
Curious what people think about moving enforcement outside the model loop vs prompt filtering / LLM judges — especially if Jensen Huang drops something agent-related at GTC today ;)