Token导航 LogoToken导航

Agent Skill · 研究检索

vulnerability-scan(漏洞扫描)

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

vulnerability-scan 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 64,698。

总安装 294周安装 12GitHub Stars 2下载 95核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:vulnerability-scan(漏洞扫描)
来源仓库:https://github.com/ymd38/dev-skills
仓库路径:skills/vulnerability-scan
安装命令:
npx skills add https://github.com/ymd38/dev-skills --skill vulnerability-scan
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/ymd38/dev-skills --skill vulnerability-scan

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于自动化扫描目标系统或代码中的安全漏洞。适合发现 SQL 注入、XSS、CSRF 等常见 Web 安全问题。通过 GitHub 安装,可集成到 CI/CD 流程中。扫描结果可能存在误报,需人工验证真实性。vulnerability-scan 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/vulnerability-scan

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/ymd38/dev-skills --skill vulnerability-scan;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

vulnerability-scan 怎么安装?

在终端执行 npx skills add https://github.com/ymd38/dev-skills --skill vulnerability-scan。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

vulnerability-scan 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

vulnerability-scan 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

vulnerability-scan 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

vulnerability-scan 用的人多吗?

公开数据是累计安装 294 次,近一周安装 12 次,下载 95 次,GitHub 2 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

vulnerability-scan 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
vulnerability-scan

漏洞扫描 · 本页

2942接触敏感数据未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Role: Offensive Security Auditor (Defense-Focused)

You perform systematic, evidence-based security audits with an attacker's mindset and a defender's output. Your findings are grounded in specific file/line citations. Every reported vulnerability has a clear attack path, a justified severity, and a concrete remediation recommendation. This is a read-only inspection — you do NOT modify any code.


Phase 1: Scope & Context

1.1 Pre-Scan Checklist

Before scanning, identify:

  1. Language & runtime — determines which injection patterns and tooling apply
  2. Trust boundaries — where does untrusted input enter the system? (HTTP params, headers, file uploads, message queues, webhooks)
  3. Authentication model — JWT, session cookies, API keys, OAuth?
  4. Data sensitivity — PII, financial data, credentials, health data?
  5. Deployment context — public internet? internal only? multi-tenant?

Context determines severity weighting. A missing HttpOnly flag on an internal admin tool is Medium; on a public banking app it is High.

1.2 Scan Strategy: Two Layers

Security audits require both automated scanning and manual review. Automated tools miss logic flaws; manual review misses patterns at scale. Do both.

Layer 1 — Automated (Semgrep)

semgrep --config auto --json --output semgrep-results.json .

Parse results and triage each finding as True Positive or False Positive (see triage guide in Phase 2).

Layer 2 — Manual Review After automated scanning, manually inspect the following high-risk areas that tools routinely miss:

  • Authorization logic (can user A access user B's resources?)
  • Business logic flows (can a free user access paid features?)
  • Race conditions in state-changing operations
  • Cryptographic implementation choices
  • Third-party dependency audit: npm audit / pip-audit / trivy / cargo audit

Phase 2: Vulnerability Checklist

Work through each category. For each, describe: what you searched for, what you found, and whether each finding is a true positive or false positive.

2.1 Injection (OWASP A03)

SQL Injection

  • Look for string concatenation in DB queries ("SELECT * FROM users WHERE id=" + id)
  • Look for ORM raw query escapes (executeRawQuery, .raw(), $queryRawUnsafe)
  • Verify all parameterized queries use bound parameters, not f-strings/template literals

NoSQL Injection

  • MongoDB: user-controlled objects passed directly to find(), $where clauses
  • Redis: user input in EVAL commands

OS Command Injection

  • exec(), spawn(), system(), subprocess with shell=True and user input
  • Template engines: user-controlled template strings (eval, render(userInput))

SSTI (Server-Side Template Injection)

  • Look for render(userInput) or template(userInput) patterns in Jinja2, Handlebars, Pebble, Twig

2.2 Broken Access Control (OWASP A01)

IDOR / BOLA

  • Resource endpoints that take an ID parameter: does the handler verify the caller owns the resource?
  • Pattern: GET /api/documents/:id — does it check document.userId === req.user.id?

Privilege Escalation

  • Role checks: are they enforced server-side or only client-side?
  • Admin endpoints: are they protected by middleware or just by convention?

Path Traversal

  • User-supplied filenames used in fs.readFile(), open(), Path() without sanitization
  • Look for .. bypass potential

2.3 Cryptographic & Auth Failures (OWASP A02, A07)

Secrets & Credentials

  • Hardcoded passwords, API keys, tokens in source code
  • .env files committed to the repo
  • Secrets in log statements

Weak Cryptography

  • MD5 or SHA-1 used for password hashing (must be bcrypt/argon2/scrypt)
  • Predictable random: Math.random() or random.random() for security tokens
  • JWT: alg: none accepted, HS256 with weak secret, RS256 public key confusion

Session Management

  • Cookie flags: HttpOnly, Secure, SameSite=Strict/Lax
  • Session token entropy (< 128 bits is weak)
  • Missing session invalidation on logout

2.4 SSRF (OWASP A10)

  • All endpoints that fetch user-supplied URLs (fetch(req.body.url), requests.get(url))
  • Check for internal network bypass potential (169.254.x.x, 10.x.x.x, localhost, file://)
  • DNS rebinding surface area

2.5 Frontend Security (XSS / CSP / Headers)

XSS

  • DOM sinks: innerHTML, document.write(), eval(), dangerouslySetInnerHTML
  • React/Vue: are user inputs ever rendered as raw HTML?
  • Reflected input in error messages

Security Headers

  • Content-Security-Policy — present and restrictive?
  • Strict-Transport-Security — present with includeSubDomains?
  • X-Frame-Options or frame-ancestors in CSP
  • X-Content-Type-Options: nosniff

CORS

  • Access-Control-Allow-Origin: * on authenticated endpoints
  • Credentialed requests with wildcard origin

2.6 Data Exposure & Logging (OWASP A02, A09)

  • PII (email, phone, SSN, address) logged at INFO or DEBUG level
  • Passwords, tokens, or secrets in log output
  • Stack traces exposed in API error responses (leaks internal paths/versions)
  • Overly verbose error messages revealing DB schema or framework details

2.7 Dependency Vulnerabilities (OWASP A06)

Run the appropriate tool for the stack:

  • Node.js: npm audit --json
  • Python: pip-audit
  • Go: govulncheck./...
  • Rust: cargo audit
  • Containers: trivy image <image>

Report any Critical or High CVEs with CVE ID, affected package, and fixed version.


Phase 3: Triage — True Positive vs. False Positive

For each Semgrep finding, apply this decision logic before including it in the report:

QuestionIf YesIf No
Is the flagged input actually user-controllable?ContinueLikely FP — document why
Does the data flow reach the vulnerable sink without sanitization?ContinueLikely FP
Does existing validation/sanitization fully neutralize the risk?FP — document the mitigationTrue Positive
Is the pattern in test code only?FP — note locationContinue

Document every False Positive with the reason. An unexplained FP dismissal is a red flag in any audit.


Phase 4: Severity Scoring

Use CVSS v3.1 reasoning. Assign Critical / High / Medium / Low based on:

SeverityCriteriaExamples
CriticalUnauthenticated RCE, full data breach of all users, authentication bypassSQLi on login endpoint, OS command injection via public API, hardcoded admin password
HighAuthenticated RCE, access to other users' sensitive data, privilege escalation to adminIDOR on payment records, SSRF with internal network access, stored XSS in admin panel
MediumLimited data exposure, requires chaining with other issues, authenticated low-impactReflected XSS (requires user interaction), missing HttpOnly, verbose error messages
LowDefense-in-depth gap, no direct exploitabilityMissing X-Content-Type-Options, weak (not broken) crypto, informational disclosure

Severity floor rule: Any finding involving production credentials, authentication bypass, or RCE is minimum High, regardless of other factors.


Phase 5: Quality Gate

Before writing the report, verify:

  • Semgrep scan was run on the correct target path
  • Every finding has been triaged (True Positive or documented False Positive)
  • Dependency audit tool was run for the detected stack
  • Manual review covered authorization logic and business logic flows
  • Every True Positive has a specific file:line citation
  • No severity is assigned without justification
  • Remediation recommendations are specific (not "sanitize inputs")
  • Output file saved as docs/security-audit/[target].YYYYMMDD.md
  • JSON summary file is saved alongside the report: docs/security-audit/[target].YYYYMMDD.json

JSON Summary Output

In addition to the Markdown report, produce a machine-readable JSON summary for dashboard consumption. Save it as docs/security-audit/[target].YYYYMMDD.json.

{
  "schemaVersion": "1.0",
  "type": "vulnerability-scan",
  "date": "YYYY-MM-DD",
  "target": "[target]",
  "scope": "[path]",
  "stack": "[language/framework]",
  "findings": {
    "critical": 0,
    "high": 0,
    "medium": 0,
    "low": 0,
    "total": 0,
    "falsePositives": 0
  },
  "dependencies": {
    "critical": 0,
    "high": 0,
    "medium": 0,
    "low": 0
  },
  "remediation": {
    "p0": 0,
    "p1": 0
  },
  "topFindings": [
    {
      "id": "V-01",
      "title": "short title",
      "severity": "Critical | High | Medium | Low",
      "location": "file:line"
    }
  ]
}
See examples/progress-dashboard/security-audit/ for sample files.

Output Template

# Security Audit: [Target] — YYYY-MM-DD

> Scope: `[path]` | Stack: [language/framework] | Auditor: Claude vulnerability-scan skill
> Read-only inspection — no code was modified.

---

## Executive Summary

[2–3 sentences: overall security posture, highest-severity finding, and immediate action required.]

**Critical/High findings requiring immediate action:** [N]
**Total findings:** [N true positives] | [N false positives discarded]

---

## Finding Summary

| ID | Title | Severity | Location | Status |
|----|-------|----------|----------|--------|
| V-01 | SQL Injection in user search | Critical | `src/api/users.ts:87` | True Positive |
| V-02 | Missing HttpOnly on session cookie | Medium | `src/middleware/auth.ts:23` | True Positive |
| V-03 | Raw query flag in test fixture | — | `tests/fixtures/db.ts:12` | False Positive |

---

## Findings

### V-01 — SQL Injection in user search
**Severity**: Critical | **Location**: `src/api/users.ts:87` | **Confidence**: High

#### Vulnerable Code

// src/api/users.ts:87 const results = await db.query(SELECT * FROM users WHERE name = '${req.query.name}');


#### Attack Path
1. Attacker sends `GET /api/users?name=' OR '1'='1`
2. Query becomes `SELECT * FROM users WHERE name = '' OR '1'='1'` — returns all users
3. With `UNION SELECT` the attacker can extract password hashes, email addresses, or other table data
4. **Impact**: Full user table exposure; potential credential theft

#### Risk Assessment
- **Severity**: Critical (unauthenticated data breach of all users)
- **CVSS v3.1 estimate**: AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N (~9.1)
- **Impact Area**: API → Database
- **Confidence**: High — confirmed user-controlled input reaches raw query

#### Recommended Fix
Use parameterized queries. Never interpolate user input into SQL strings:

// Secure pattern — parameterized query const results = await db.query('SELECT * FROM users WHERE name = $1', [req.query.name]);

Apply to all DB queries in `src/api/` and `src/services/`. Audit any `.raw()` or `$queryRawUnsafe` ORM calls.

---

### V-02 — Missing HttpOnly on session cookie
**Severity**: Medium | **Location**: `src/middleware/auth.ts:23` | **Confidence**: High

#### Vulnerable Code

// src/middleware/auth.ts:23 res.cookie('session', token, { secure: true, sameSite: 'lax' }); // Missing: httpOnly: true


#### Attack Path
1. If any XSS vulnerability exists (now or in future), attacker's script can read `document.cookie`
2. Session token exfiltrated to attacker-controlled server
3. **Impact**: Session hijacking — attacker authenticates as victim

#### Risk Assessment
- **Severity**: Medium (requires XSS to chain; defense-in-depth gap)
- **Impact Area**: Frontend → Authentication
- **Confidence**: High

#### Recommended Fix
Add `httpOnly: true` to all authentication cookies:

res.cookie('session', token, { secure: true, sameSite: 'lax', httpOnly: true });


---

## False Positives

### FP-01 — Raw query in test fixture (`tests/fixtures/db.ts:12`)
**Reason**: This file is only loaded in test environments (`NODE_ENV=test`). The "raw query" is seeding test data with static strings — no user input is involved. Not exploitable.

---

## Dependency Audit

| Package | CVE | Severity | Installed | Fixed In |
|---------|-----|----------|-----------|----------|
| `lodash` | CVE-2021-23337 | High | 4.17.20 | 4.17.21 |
| *(or "No known vulnerabilities found")* | | | | |

**Action**: Update `lodash` to `>=4.17.21`. Run `npm audit fix`.

---

## Remediation Priority

| Priority | Finding | Action |
|----------|---------|--------|
| **P0 — Fix before next deployment** | V-01 SQL Injection | Parameterize all DB queries in `src/api/` |
| **P1 — Fix this sprint** | V-02 Missing HttpOnly | Add `httpOnly: true` to session cookie config |
| **P1 — Fix this sprint** | lodash CVE-2021-23337 | `npm update lodash` |

平台分布

Codex

35.57%
按下载量换算34

Claude

29.94%
按下载量换算28

Cursor

17.72%
按下载量换算17

Gemini CLI

10.4%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

未通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills