Muhammad Waleed

OpenClaw Security Auditor

运维安全v1.0.0
作者:Muhammad Waleed更新于 2026-05-11openclaw-security-auditor@clawhub_muhammad-waleed381/openclaw-security-auditor
安全扫描#Security Audit

据 AlphaMoat 统计,截至 2026-09,OpenClaw Security Auditor 累计下载 3,201 次、获 1 个星标,在全部 5,078 个 AI 技能中排名第 3,556 位,在「运维安全」分类(264 个)中排名第 233 位。

累计安装
117
累计下载
3,201
星标
1
全站排名
#3,556
全站前 71% · 「运维安全」前 89%
平台累计安装累计下载星标评论
ClawHub ↗1033,1101-
SkillHub ↗1173,2011—

各平台的计数口径不同,统计时间不一致。

这个技能是做什么的

主文说明只提取安全相关设置和元数据,不读取实际密钥,也不需要外部 API 或额外 key。报告包含风险等级和修复建议;修复操作应由用户检查后另行执行。

资料发布于 2026-10-29

可以用来做什么

  • 审计 OpenClaw 配置并生成安全报告
  • 整理配置加固建议和待办清单

什么时候用

  • 需要审查 OpenClaw 部署的配置风险或准备加固清单时

示例提示词

以下是根据技能文档生成的示例,不代表实际运行结果。

说明本地审计会读取哪些配置元数据、如何避免读取密钥,以及报告包含什么;不要读取文件或应用修复。

月度数据趋势

累计下载3,201
2,2202,7473,2742026-03 · 累计下载 2,2932026-05 · 累计下载 2,6802026-07 · 累计下载 2,9642026-08 · 累计下载 3,0402026-09 · 累计下载 3,20126-0326-09
星标1
0122026-03 · 星标 12026-05 · 星标 12026-07 · 星标 12026-08 · 星标 12026-09 · 星标 126-0326-09

2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。 该技能有同名的其他版本,调整前的数据归属当前下载量最大的这一个。

月份累计下载环比星标
2026-093,201—1
2026-083,040+2.6%1
2026-072,964—1
2026-052,680—1
2026-032,293—1

SKILL.md 原文

以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 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"]
---
展开全部(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.

「运维安全」分类下的热门技能

数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。