Security Best Practices Analyzer

Analyze codebases for security vulnerabilities and generate structured findings reports covering authentication, injection, XSS, CSRF, secrets, and framework-specific hardening across Python, JavaScript/TypeScript, and Go.

Overview

This skill operates in three modes depending on context:

Mode 1 -- Secure generation (default): When writing new code, apply every security rule from the reference checklist. Enforce parameterized queries, proper auth middleware, secure cookie flags, and secrets-from-environment patterns without being asked.

Mode 2 -- Passive review (always on): While editing or reading code for other tasks, flag critical vulnerabilities found in touched or nearby files. Focus on high-impact issues: hardcoded secrets, SQL injection, command injection, disabled auth, permissive CORS. Do not overwhelm the user with low-severity findings during unrelated work.

Mode 3 -- Active audit (on request): When the user asks for a security review or report, perform a systematic scan of the codebase and produce a structured findings report ordered by severity.

What it analyzes:

• •Authentication and authorization enforcement
• •Injection vectors (SQL, command, template, path traversal, deserialization)
• •Cross-site scripting (XSS) and cross-site request forgery (CSRF)
• •Secrets management and credential exposure
• •File upload handling
• •Security headers and TLS configuration
• •Framework-specific misconfigurations

What it produces:

• •Structured findings with severity, file path, line number, evidence, and remediation
• •Executive summary with critical/high/medium/low counts
• •Prioritized fix recommendations

Analysis Checklist

Authentication and Authorization

• • Auth enforced via middleware or dependency injection, not per-handler opt-in
• • Password hashing uses bcrypt, argon2, or scrypt with appropriate cost factor
• • JWT validation checks signature, expiry, issuer, and audience
• • Resource ownership verified on every state-changing request
• • Session tokens regenerated after privilege changes
• • Rate limiting applied to authentication endpoints

Injection Prevention

• • SQL uses parameterized queries or ORM exclusively (no string concatenation)
• • No use of eval(), exec(), os.system(), child_process.exec() with user input
• • Template rendering uses auto-escaping (no user input as template source)
• • File paths validated and canonicalized (reject .. sequences)
• • No deserialization of untrusted data (pickle.load, yaml.load without SafeLoader)

XSS Prevention

• • Framework auto-escaping enabled (React JSX, Vue templates, Jinja2 |e)
• • No use of dangerouslySetInnerHTML, v-html, or innerHTML with user data
• • Rich content sanitized through DOMPurify or bleach
• • Content-Security-Policy header configured

CSRF Protection

• • Token-based CSRF protection when cookies carry authentication
• • Django: {% csrf_token %} and middleware enabled
• • Express: csurf or csrf-csrf middleware configured
• • Next.js: Origin header verification on server actions

Secrets Management

• • All secrets loaded from environment variables or secret managers
• • No hardcoded credentials in source, config files, or Docker images
• • .gitignore excludes .env, key files, and credential stores
• • No secrets in log output or error messages

File Upload Security

• • MIME type and magic bytes validated (not just file extension)
• • Files stored outside web root with generated random filenames
• • Size limits enforced server-side
• • Upload directory not executable

Framework Hardening

• • FastAPI: docs_url=None and redoc_url=None in production
• • Django: DEBUG=False, ALLOWED_HOSTS set, SECRET_KEY from environment
• • Flask: app.secret_key from environment, debug=False in production
• • Express: helmet() middleware for security headers
• • Next.js: Server Actions validate input server-side
• • Go: Server and client timeouts set, crypto/rand used (not math/rand)

Metrics

Severity Classification

Vulnerability Categories

Scanning Heuristics

Search for these patterns to detect vulnerabilities:

# Command injection
grep -rn 'eval(' --include='*.py' --include='*.js' --include='*.ts'
grep -rn 'os\.system(' --include='*.py'
grep -rn 'subprocess.*shell=True' --include='*.py'
grep -rn 'child_process\.exec(' --include='*.js' --include='*.ts'

# SQL injection
grep -rn 'execute.*format\|execute.*f"' --include='*.py'
grep -rn 'fmt\.Sprintf.*SELECT' --include='*.go'

# XSS sinks
grep -rn 'dangerouslySetInnerHTML' --include='*.jsx' --include='*.tsx'
grep -rn 'v-html' --include='*.vue'
grep -rn 'innerHTML' --include='*.js' --include='*.ts'

# Unsafe deserialization
grep -rn 'pickle\.load' --include='*.py'
grep -rn 'yaml\.load(' --include='*.py'

# Secrets and misconfigurations
grep -rn 'DEBUG\s*=\s*True' --include='*.py'

Report Format

Finding Structure

[SEC-NNN] Severity: CRITICAL|HIGH|MEDIUM|LOW
Category: Injection|Auth|XSS|CSRF|Secrets|Config|Upload
File: path/to/file.py:42
Finding: One-sentence vulnerability description
Evidence: `code_snippet()`
Impact: What an attacker could achieve
Fix: Remediation steps

Full Report Structure

# Security Best Practices Report

**Repository:** /path/to/repo
**Date:** 2026-02-04
**Scope:** Python (FastAPI), JavaScript (React)

## Executive Summary

- Critical: 2 findings
- High: 3 findings
- Medium: 5 findings
- Low: 4 findings

## Critical Findings

[SEC-001] ...

## High Findings

[SEC-002] ...

## Recommendations

1. Fix SQL injection in auth handler (SEC-001)
2. Remove hardcoded API key (SEC-003)

Workflow

Step 1: Detect languages and frameworks

Inspect the repository for package.json (Node.js), requirements.txt or pyproject.toml (Python), go.mod (Go), and framework indicators in imports and configs.

Step 2: Load security reference

Read references/cross-language-security.md and apply rules relevant to the detected stack.

Step 3: Choose operating mode

• •Writing new code: Mode 1 (secure generation), apply rules silently
• •Editing existing code: Mode 2 (passive review), flag critical issues only
• •User requests review: Mode 3 (active audit), full structured report

Step 4: Scan for vulnerabilities (Mode 3)

Run scanning heuristics using Grep and Bash. Organize findings by severity with file paths and line numbers.

Step 5: Generate report (Mode 3)

Write to security_best_practices_report.md or user-specified location. Structure by severity. Summarize to user and offer to begin fixes.

Step 6: Apply fixes

Fix one finding at a time. Write comments explaining security rationale. Verify fixes do not break functionality. Follow project commit and testing conventions.

Overrides and Exceptions

Projects may bypass specific rules for legitimate reasons. When documented in project config or CLAUDE.md, respect the override. When undocumented, inform the user once and suggest adding documentation.

Safety Constraints

• •MUST NOT output, log, or commit secrets (API keys, passwords, tokens, signing keys)
• •MUST NOT weaken protections as a "fix" (disabling auth, permissive CORS, skipping validation)
• •MUST provide evidence for every finding (file path, line number, code snippet)
• •MUST treat uncertainty honestly: note "verify at runtime" when infrastructure protections may exist
• •CORS is not an authentication mechanism; CSRF applies only when browsers auto-attach cookies

TLS and HSTS Guidance

Do not flag missing TLS in development contexts. Do not set Secure cookie flags unless the application runs over HTTPS. Do not recommend HSTS without discussing long-term implications. Provide environment-based toggles for production-only security headers.

Examples

Example 1: Full security audit

User: Run a security review on this Python FastAPI project.
Assistant: I'll analyze the codebase for security vulnerabilities.

Detected: Python 3.11, FastAPI 0.104, SQLAlchemy 2.0, React 18

Scanning authentication, injection, XSS, CSRF, secrets...

Security Best Practices Report
==============================
Critical: 1 | High: 2 | Medium: 3 | Low: 2

[SEC-001] CRITICAL - SQL Injection
File: app/db/queries.py:87
Finding: Raw string interpolation in SQL query
Evidence: `cursor.execute(f"SELECT * FROM users WHERE id = {user_id}")`
Fix: Use parameterized query: `cursor.execute("SELECT * FROM users WHERE id = %s", (user_id,))`

[SEC-002] HIGH - Hardcoded Secret
File: app/config.py:12
Finding: JWT secret hardcoded in source
Evidence: `JWT_SECRET = "my-secret-key-123"`
Fix: Load from environment: `JWT_SECRET = os.environ["JWT_SECRET"]`

Full report written to security_best_practices_report.md.
Want me to start fixing these findings?

Example 2: Passive detection during development

User: Add a new endpoint to fetch user profile by ID.
Assistant: I'll add the endpoint. While working on this file, I noticed
a critical issue in a nearby function:

[SEC-003] CRITICAL - Command Injection
File: app/utils/export.py:23
Finding: os.system() called with user-controlled input
Evidence: `os.system(f"pdf-convert {filename}")`
Fix: Use subprocess.run(["pdf-convert", filename], shell=False)

Want me to fix this while I'm here?

[Proceeds to add the requested endpoint with parameterized queries
and proper auth middleware applied by default]

Interpretation Guide