Muhammad Waleed

OpenClaw Security Auditor

運維安全v1.0.0
作者:Muhammad Waleed更新於 2026-05-11openclaw-security-auditor@clawhub_muhammad-waleed381/openclaw-security-auditor
安全掃描#Security Audit

據 AlphaMoat 統計,截至 2026-09,OpenClaw Security Auditor 累計下載 3,201 次、獲 1 個星標,在全部 5,078 個 AI 技能中排名第 3,556 位,在「運維安全」分類(264 個)中排名第 233 位。

累計安裝
117
累計下載
3201
星標
1
全站排名
#3,556
全站前 71% · 「運維安全」前 89%
平台累計安裝累計下載星標評論
ClawHub ↗10331101-
SkillHub ↗11732011—

各平台的計數口徑不同,統計時間不一致。

這個技能是做什麼的

主文說明只提取安全相關設定和後設資料,不讀取實際金鑰,也不需要外部 API 或額外 key。報告包含風險等級和修復建議;修復操作應由使用者檢查後另行執行。

資料釋出於 2026-10-29

可以用來做什麼

  • 審計 OpenClaw 配置並生成安全報告
  • 整理配置加固建議和待辦清單

什麼時候用

  • 需要審查 OpenClaw 部署的配置風險或準備加固清單時

示例提示詞

以下是根據技能文件生成的示例,不代表實際執行結果。

說明本地審計會讀取哪些配置後設資料、如何避免讀取金鑰,以及報告包含什麼;不要讀取檔案或應用修復。

月度資料趨勢

累計下載3201
2220274732742026-03 · 累計下載 22932026-05 · 累計下載 26802026-07 · 累計下載 29642026-08 · 累計下載 30402026-09 · 累計下載 320126-0326-09
星標1
0122026-03 · 星標 12026-05 · 星標 12026-07 · 星標 12026-08 · 星標 12026-09 · 星標 126-0326-09

2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。 該技能有同名的其他版本,調整前的資料歸屬當前下載量最大的這一個。

月份累計下載環比星標
2026-093201—1
2026-083040+2.6%1
2026-072964—1
2026-052680—1
2026-032293—1

SKILL.md 原文

以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 2026-10-23。

3.4 KB · ClawHub v1.0.0
---
name: openclaw-security-audit
description: Audit OpenClaw configuration for security risks and generate a remediation report using the user's configured LLM.
metadata:
  openclaw:
    requires:
      bins: ["cat", "jq"]
    os: ["darwin", "linux", "windows"]
---
展開全部(共 119 行)
# OpenClaw Security Audit Skill

Local-only skill that audits `~/.openclaw/openclaw.json`, runs 15+ security
checks, and generates a detailed report using the user's existing LLM
configuration. No external APIs or keys required.

## When to Use This Skill

- The user asks for a security audit of their OpenClaw instance.
- The user wants a remediation checklist for configuration risks.
- The user is preparing an OpenClaw deployment and wants a hardening review.

## How It Works

1. Read config with standard tools (`cat`, `jq`).
2. Extract security-relevant settings (NEVER actual secrets).
3. Build a structured findings object with metadata only.
4. Pass findings to the user's LLM via OpenClaw's normal agent flow.
5. Generate a markdown report with severity ratings and fixes.

## Inputs

- target_config_path (optional): Path to OpenClaw config file.
  - default: ~/.openclaw/openclaw.json

## Outputs

- Markdown report including:
  - Overall risk score (0-100)
  - Findings categorized by severity (Critical/High/Medium/Low)
  - Each finding with description, why it matters, how to fix, example config
  - Prioritized remediation roadmap

## Security Checks (15+)

1. API keys hardcoded in config (vs environment variables)
2. Weak or missing gateway authentication tokens
3. Unsafe gateway.bind settings (0.0.0.0 without proper auth)
4. Missing channel access controls (allowFrom not set)
5. Unsafe tool policies (elevated tools without restrictions)
6. Sandbox disabled when it should be enabled
7. Missing rate limits on channels
8. Secrets potentially exposed in logs
9. Outdated OpenClaw version
10. Insecure WhatsApp configuration
11. Insecure Telegram configuration
12. Insecure Discord configuration
13. Missing audit logging for privileged actions
14. Overly permissive file system access scopes
15. Unrestricted webhook endpoints
16. Insecure default admin credentials

## Data Handling Rules

- Strip all secrets before analysis.
- Only report metadata such as present/missing/configured.
- Do not log or emit actual key values.
- Use local-only execution; no network calls.

## Example Findings Object (Redacted)

```json
{
  "config_path": "~/.openclaw/openclaw.json",
  "openclaw_version": "present",
  "gateway": {
    "bind": "0.0.0.0",
    "auth_token": "missing"
  },
  "channels": {
    "allowFrom": "missing",
    "rate_limits": "missing"
  },
  "secrets": {
    "hardcoded": "detected"
  },
  "tool_policies": {
    "elevated": "unrestricted"
  }
}
```

## Report Format

The report must include:

- Overall risk score (0-100)
- Severity buckets: Critical, High, Medium, Low
- Each finding: description, why it matters, how to fix, example config
- Prioritized remediation roadmap

## Skill Flow (Pseudo)

```text
read_config_path = input.target_config_path || ~/.openclaw/openclaw.json
raw_config = cat(read_config_path)
json = jq parse raw_config
metadata = extract_security_metadata(json)
findings = build_findings(metadata)
report = openclaw.agent.analyze(findings, format=markdown)
return report
```

## Notes

- Uses the user's existing OpenClaw LLM configuration (Opus, GPT, Gemini, and
  local models).
- No external APIs or special model access are required.

「運維安全」分類下的熱門技能

資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。