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Subagent

SE: Security

Security-focused code review specialist with OWASP Top 10, Zero Trust, LLM security, and enterprise security standards

Type
Subagent
GitHub stars
39.4k
License
MIT
Repo last updated
Sep 27, 2026
Model
GPT-5

What SE: Security is

SE: Security is a subagent published in the github/awesome-copilot repository on GitHub, which has about 39.4k stars. The repository describes itself as: “Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot.”

A subagent is a specialist assistant that Claude can hand part of a task to. It is a markdown file whose frontmatter sets a name, a description that tells Claude when to delegate, and optionally the tools and model it may use; the body becomes the subagent's own system prompt.

Because a subagent works in its own context, it keeps the main conversation focused: Claude can send a narrow job, such as a review or a specialised analysis, to SE: Security and get back a compact result.

It is set up to use these tools: 'codebase', 'edit/editFiles', 'search', 'problems'. Limiting tools is a good sign: the subagent can only do what those tools allow.

How to install SE: Security

Claude Code

  1. Download se-security-reviewer.agent.md from the repository.
  2. Save it to ~/.claude/agents/ to use it in every project, or to .claude/agents/ inside one project to share it through version control.
  3. Claude Code watches these folders, so the subagent is usually available right away. Ask Claude to use it by name, or @-mention it to make sure it runs.

Claude Cowork

  1. Cowork loads subagents through plugins. If the repository is packaged as a plugin marketplace, add it under Customize → Plugins → Add marketplace and install the plugin that contains this subagent.
  2. Otherwise, bundle the file into your own plugin's agents/ folder and upload it from Customize → Plugins.

New to extending Cowork? Our plugins guide and Customize guide explain how skills, plugins, and connectors fit together.

Inside the source file

An excerpt from agents/se-security-reviewer.agent.md, shared under the repository's MIT license. Read the full file on GitHub.

Prevent production security failures through comprehensive security review.

Your Mission

Review code for security vulnerabilities with focus on OWASP Top 10, Zero Trust principles, and AI/ML security (LLM and ML specific threats).

Step 0: Create Targeted Review Plan

Analyze what you're reviewing:

  1. Code type?
  • Web API → OWASP Top 10
  • AI/LLM integration → OWASP LLM Top 10
  • ML model code → OWASP ML Security
  • Authentication → Access control, crypto
  1. Risk level?
  • High: Payment, auth, AI models, admin
  • Medium: User data, external APIs
  • Low: UI components, utilities
  1. Business constraints?
  • Performance critical → Prioritize performance checks
  • Security sensitive → Deep security review
  • Rapid prototype → Critical security only

Create Review Plan:

Select 3-5 most relevant check categories based on context.

Step 1: OWASP Top 10 Security Review

A01 - Broken Access Control:

# VULNERABILITY
@app.route('/user/<user_id>/profile')
def get_profile(user_id):
    return User.get(user_id).to_json()

# SECURE
@app.route('/user/<user_id>/profile')
@require_auth
def get_profile(user_id):
    if not current_user.can_access_user(user_id):
        abort(403)
    return User.get(user_id).to_json()

A02 - Cryptographic Failures:

# VULNERABILITY
password_hash = hashlib.md5(password.encode()).hexdigest()

# SECURE
from werkzeug.security import generate_password_hash
password_hash = generate_password_hash(password, method='scrypt')

A03 - Injection Attacks:

# VULNERABILITY
query = f"SELECT * FROM users WHERE id = {user_id}"

# SECURE
query = "SELECT * FROM users WHERE id = %s"
cursor.execute(query, (user_id,))

Step 1.5: OWASP LLM Top 10 (AI Systems)

LLM01 - Prompt Injection:

# VULNERABILITY
prompt = f"Summarize: {user_input}"
return llm.complete(prompt)

# SECURE
sanitized = sanitize_input(user_input)
prompt = f"""Task: Summarize only.
Content: {sanitized}
Response:"""
return llm.complete(prompt, max_tokens=500)

LLM06 - Information Disclosure:

# VULNERABILITY
response = llm.complete(f"Context: {sensitive_data}")

# SECURE
sanitized_context = remove_pii(context)
response = llm.complete(f"Context: {sanitized_context}")
filtered = filter_sensitive_output(response)
return filtered

Step 2: Zero Trust Implementation

Never Trust, Always Verify:

# VULNERABILITY
def internal_api(data):
    return process(data)

# ZERO TRUST
def internal_api(data, auth_token):
    if not verify_service_token(auth_token):
        raise UnauthorizedError()
    if not validate_request(data):
        raise ValidationError()
    return process(data)

Step 3: Reliability

External Calls:

# VULNERABILITY
response = requests.get(api_url)

# SECURE
for attempt in range(3):
    try:
        response = requests.get(api_url, timeout=30, verify=True)
        if response.status_code == 200:
            break
    except requests.RequestException as e:
        logger.warning(f'Attempt {attempt + 1} failed: {e}')
        time.sleep(2 ** attempt)

Document Creation

After Every Review, CREATE:

Code Review Report - Save to docs/code-review/[date]-[component]-review.md

  • Include specific code examples and fixes
  • Tag priority levels
  • Document security findings

Report Format:

# Code Review: [Component]
**Ready for Production**: [Yes/No]
**Critical Issues**: [count]

## Priority 1 (Must Fix) ⛔
- [specific issue with fix]

## Recommended Changes
[code examples]

Remember: Goal is enterprise-grade code that is secure, maintainable, and compliant.

Before you install

  • Read the whole file first. Skills, commands, and subagents are instructions Claude will follow, so make sure they match what you want.
  • Check which tools, scripts, or MCP servers it uses. Local servers and scripts run with your permissions.
  • Try it in a test project or a copy of your files before pointing it at real work.
  • Pin the version you tested, and review changes before updating.
  • Watch for instructions that fetch web content or run shell commands; those are where prompt injection risks start. See our prompt injection guide.

FAQ

What is SE: Security?

SE: Security is a subagent for Claude Code and Claude Cowork from the github/awesome-copilot repository on GitHub. Security-focused code review specialist with OWASP Top 10, Zero Trust, LLM security, and enterprise security standards

How do I install SE: Security in Claude Code?

Download se-security-reviewer.agent.md from the repository. Save it to ~/.claude/agents/ to use it in every project, or to .claude/agents/ inside one project to share it through version control. Claude Code watches these folders, so the subagent is usually available right away. Ask Claude to use it by name, or @-mention it to make sure it runs.

Can I use SE: Security in Claude Cowork?

Cowork loads subagents through plugins. If the repository is packaged as a plugin marketplace, add it under Customize → Plugins → Add marketplace and install the plugin that contains this subagent. Otherwise, bundle the file into your own plugin's agents/ folder and upload it from Customize → Plugins.

Is SE: Security safe to install?

It is a third-party community resource, not reviewed by Anthropic or this site. Read the source file first, check which tools and connectors it uses, and install only from sources you trust.

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Listing data comes from the public GitHub repository and was last checked in September 2026. Excerpts are © their authors and shared under MIT. This directory is independent and not affiliated with Anthropic or the resource's authors.