# slowmist/openclaw-security-practice-guide

This guide is designed for OpenClaw itself (Agent-facing), not as a traditional human-only hardening checklist.

Repository: https://github.com/slowmist/openclaw-security-practice-guide
Canonical: https://ross.abutalabs.com/products/openclaw-security-practice-guide
Language: Shell
License: MIT
License Family: permissive
Last push: 2026-04-06T05:58:43+00:00

## Health v2 (maintenance only)
Score: 49/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 76, release rhythm 35, longevity 13
- inputs: {"age_days": 184, "days_push": 149, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2857, forks 195 (observed 2026-08-28T04:07:25.896402+00:00)

## What it is
A security practice guide for OpenClaw, a high-privilege autonomous AI agent, built around an Agentic Zero-Trust Architecture rather than a traditional host-hardening checklist. It includes shell-based deployment tooling and is designed to be fed directly to the agent so it can evaluate and deploy its own defense matrix against prompt injection, supply chain poisoning, and destructive operations.

## Use cases
- secure an autonomous AI agent running with root privileges
- protect against prompt injection in AI agent workflows
- harden OpenClaw against supply chain poisoning
- set up nightly auditing for AI agent operations
- deploy a zero-trust defense matrix for an AI agent
- add human-approval gates for high-risk agent actions

## When to choose
- you run OpenClaw or a similar high-privilege autonomous agent and want agent-facing security guardrails
- you want the agent itself to evaluate and deploy its own defenses with minimal manual setup
- you need a structured threat model covering prompt injection, destructive commands, and supply chain risks

## When to avoid
- you need a traditional human-only server hardening checklist
- you expect the guide to make your agent fully secure - it explicitly does not
- you use a weak or older model, since the guide relies on strong reasoning models to interpret and execute it correctly

## Facets
- artifact type: learning-resource
- maturity: active
- function: security, agent-framework, developer-tools, documentation
- domain: security, developer-tools, large-language-models
- platform: cli, cross-platform
- tags: ai-agent-security, zero-trust, prompt-injection, openclaw, security-hardening, autonomous-agents, shell-scripts, defense-matrix, ai-agents, linux, macos

## Member repositories
- slowmist/openclaw-security-practice-guide (main) score 49

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:25.896402+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:36:30.430784+00:00, confidence not recorded.
  - readme: https://github.com/slowmist/openclaw-security-practice-guide (fetched 2026-08-28T04:07:25.896402+00:00, sha 8b6e6217ae7b)
- Data as of 2026-08-30T08:39:29.467469+00:00.