AI Security Crew

A lightweight MCP server for security reviews built for vibe coding — injects security requirements prior to code generation, scans dependencies for CVEs, and verifies generated code, all without breaking your coding rhythm.

Jump to installation:

• MCP Server — full feature set with Jira, Confluence, CVE scanning (with reachability), and threat modeling
• Claude Code Plugin — install 3 security skills globally in Claude Code (no Jira/MCP needed)
• Claude Code Skills only — manually add slash commands to a specific project

---

Claude Code Plugin

Install all three security skills directly into Claude Code — no MCP server, no Jira, no configuration required.

/plugin install Srajangpt1/ai_security_crew

This gives you three commands available in any project:

Command	When to use
/sec-review	Before coding — get risk level, OWASP guidelines, and a security prompt for AI code generation
/verify-code	After coding — review code for vulnerabilities with a checklist and prioritized fixes
/threat-model	For new features — identify threats with evidence links, mitigations, and optional threat-model.md

---

Claude Code Skills

If you prefer to add the skills to a specific project only (instead of globally), clone this repo and the slash commands in .claude/commands/ are available automatically in Claude Code when working in the project directory.

---

Tools

Pre-coding

Tool	When to Use
lightweight_security_review	Before any coding task — get security requirements and guidelines for your tech stack
assess_ticket_security	Before coding from a Jira ticket — pull security requirements directly from the ticket
perform_threat_model	For significant new features — generate a structured threat model (STRIDE, attack surfaces)

Dependency security

Tool	When to Use
verify_packages	When adding packages — confirm they exist with valid versions (catches hallucinated package names)
scan_dependencies	When adding packages — scan for CVEs and check reachability in your code

Post-coding

Tool	When to Use
verify_code_security	After generating code — AI-powered security review against OWASP guidelines

Threat model persistence

Tool	When to Use
search_previous_threat_models	Before creating a new threat model — check if one already exists in Confluence
update_threat_model_file	After perform_threat_model — write the threat model to threat-model.md in the repo

Agent Workflow

The server automatically sends workflow instructions to any connecting agent (Claude, Cursor, etc.) via the MCP initialize handshake. Agents will follow this workflow without additional configuration:

1. Before coding — call lightweight_security_review (or assess_ticket_security for Jira tickets)
2. When adding packages — call verify_packages, then scan_dependencies with the code that uses them
3. After generating code — call verify_code_security and follow the review_prompt to report findings
4. For significant features — call perform_threat_model and persist with update_threat_model_file

Dependency Scanning

scan_dependencies uses OSV.dev to find CVEs and performs reachability analysis to determine if vulnerable code paths are actually called:

Status	Meaning
reachable	Vulnerable function is called in your code — action required
not_reachable	Vulnerable function is not called
not_imported	Package is not imported at all
uncertain	AI analyzed the code but could not determine reachability
no_code_provided	No code snippets were passed to the tool

Reachability is determined by (in order): OSV function-level symbols → keyword matching against the vuln summary → AI analysis via ctx.sample().

Quick Start

1. Build the image

docker build -t mcp-security-review:latest .

2. Configure your IDE

Add to your MCP config (Claude Desktop, Cursor, etc.):

{
  "mcpServers": {
    "sec-review": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "JIRA_URL",
        "-e", "JIRA_USERNAME",
        "-e", "JIRA_API_TOKEN",
        "-e", "CONFLUENCE_URL",
        "-e", "CONFLUENCE_USERNAME",
        "-e", "CONFLUENCE_API_TOKEN",
        "mcp-security-review:latest"
      ],
      "env": {
        "PATH": "/usr/local/bin:/usr/bin:/bin",
        "JIRA_URL": "https://your-domain.atlassian.net",
        "JIRA_USERNAME": "your-email@example.com",
        "JIRA_API_TOKEN": "your-token"
      }
    }
  }
}

Authentication

Supported methods:

• API Token (Jira/Confluence Cloud): JIRA_API_TOKEN, CONFLUENCE_API_TOKEN
• Personal Access Token (Server/Data Center): JIRA_PERSONAL_TOKEN, CONFLUENCE_PERSONAL_TOKEN
• OAuth 2.0 (Cloud): run docker run --rm -it mcp-security-review:latest --oauth-setup

HTTP Transport

Run as a persistent HTTP service instead of stdio:

# Streamable HTTP (recommended)
docker run --rm -p 8000:8000 mcp-security-review:latest --transport streamable-http

# SSE
docker run --rm -p 8000:8000 mcp-security-review:latest --transport sse

Security Guidelines

Includes 101 OWASP Cheat Sheets loaded automatically into security assessments. Add your own org-specific guidelines:

python3 scripts/add_custom_guideline.py

Or manually create markdown files in src/mcp_security_review/security/guidelines/docs/:

category: your_category
priority: high
tags: tag1, tag2, tag3

# Your Guideline Title
...

See docs/ADDING_CUSTOM_GUIDELINES.md for details.

Contributing

1. Check CONTRIBUTING.md for development setup.
2. Make changes and submit a pull request.

Pre-commit hooks enforce code quality (Ruff, Prettier, Pyright). Run uv run pytest before submitting.

Security

Never commit API tokens. See SECURITY.md for best practices.

License

Licensed under MIT — see LICENSE.