Python Security Scan

Comprehensive security vulnerability scanner for Python projects including Flask, Django, and FastAPI applications. Detects OWASP Top 10 vulnerabilities, injection flaws, insecure deserialization, authentication issues, hardcoded secrets, and framework-specific security problems. Audits dependencies for known CVEs and generates actionable security reports.

16 stars

Best use case

Python Security Scan is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Comprehensive security vulnerability scanner for Python projects including Flask, Django, and FastAPI applications. Detects OWASP Top 10 vulnerabilities, injection flaws, insecure deserialization, authentication issues, hardcoded secrets, and framework-specific security problems. Audits dependencies for known CVEs and generates actionable security reports.

Teams using Python Security Scan should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/python-security-scan/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/development/python-security-scan/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/python-security-scan/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Python Security Scan Compares

Feature / AgentPython Security ScanStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Comprehensive security vulnerability scanner for Python projects including Flask, Django, and FastAPI applications. Detects OWASP Top 10 vulnerabilities, injection flaws, insecure deserialization, authentication issues, hardcoded secrets, and framework-specific security problems. Audits dependencies for known CVEs and generates actionable security reports.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Python Security Scan Skill

This skill enables comprehensive security scanning of Python projects based on OWASP guidelines, Python security best practices, and framework-specific vulnerabilities.

## When to Use This Skill

- Security audits of Python applications
- Code review for security vulnerabilities
- Pre-deployment security checks
- Dependency vulnerability assessment
- Detecting hardcoded secrets and credentials
- Framework-specific security reviews (Flask, Django, FastAPI)

## Supported Frameworks

This skill automatically detects and applies framework-specific checks for:

- **Flask** - Template injection, session security, CORS, extensions
- **Django** - ORM injection, CSRF, template security, settings
- **FastAPI** - Dependency injection, Pydantic validation, OAuth2
- **General Python** - Core language vulnerabilities applicable to all projects

## Scan Types

### 1. Quick Scan
Fast scan focusing on critical vulnerabilities:
- Hardcoded secrets, API keys, and credentials
- Dangerous function usage (`eval`, `exec`, `pickle.loads`)
- Command injection via `subprocess`, `os.system`
- SQL injection patterns
- Known vulnerable dependencies

### 2. Full Scan
Comprehensive security assessment covering:
- All OWASP Top 10:2025 categories
- Python-specific vulnerabilities
- Framework-specific security issues
- Injection vulnerabilities (SQL, NoSQL, Command, LDAP)
- Insecure deserialization
- Authentication and authorization flaws
- Cryptographic failures
- Security misconfigurations
- Dependency audit (CVE check)
- Environment variable and secrets exposure

### 3. Targeted Scan
Focus on specific vulnerability categories:
- `--injection` - SQL/NoSQL/Command/LDAP injection
- `--deserialization` - Pickle, YAML, JSON deserialization
- `--auth` - Authentication/authorization issues
- `--secrets` - Hardcoded credentials
- `--deps` - Dependency vulnerabilities
- `--crypto` - Cryptographic issues
- `--flask` - Flask-specific vulnerabilities
- `--django` - Django-specific vulnerabilities
- `--fastapi` - FastAPI-specific vulnerabilities

## Scan Procedure

### Step 1: Project Discovery
1. Identify project type and framework:
   - Check for `requirements.txt`, `Pipfile`, `pyproject.toml`, `setup.py`
   - Detect Flask (`from flask import`), Django (`django.conf`), FastAPI (`from fastapi import`)
2. Locate configuration files
3. Map the codebase structure

### Step 2: Framework Detection
```python
# Detection patterns
Flask: "from flask import", "Flask(__name__)"
Django: "django.conf.settings", "INSTALLED_APPS", "manage.py"
FastAPI: "from fastapi import", "FastAPI()"
```

### Step 3: Dependency Audit
Run the dependency audit script:
```bash
./scripts/dependency-audit.sh /path/to/project
```
Or manually:
```bash
pip-audit
# or
safety check
```

### Step 4: Secret Scanning
Scan for hardcoded secrets:
```bash
python scripts/secret-scanner.py /path/to/project
```

**Important: Environment File Handling**
- By default, real `.env` files are **SKIPPED** (`.env`, `.env.local`, `.env.production`, etc.)
- These files contain actual secrets and should not be in version control
- Only `.env.example` and `.env.template` files are analyzed for documentation quality
- Use `--include-env-files` flag only if explicitly requested by user

The scanner will:
1. Scan source code for hardcoded secrets
2. Analyze `.env.example` templates to check:
   - Which sensitive variables are documented
   - Whether variables have descriptions (comments)
   - If placeholder values look like real secrets
   - Suggestions for missing common variables (SECRET_KEY, DATABASE_URL, etc.)

### Step 5: Pattern Analysis
For each file in the codebase, check against patterns in:
- `references/python-vulnerabilities.md` - Core Python issues
- `references/injection-patterns.md` - Injection flaws
- `references/deserialization.md` - Insecure deserialization
- `references/flask-security.md` - Flask vulnerabilities
- `references/django-security.md` - Django vulnerabilities
- `references/fastapi-security.md` - FastAPI vulnerabilities

### Step 6: Report Generation
Generate a security report using:
- `assets/report-template.md` - Report structure

## Severity Classification

| Severity | Description | Action Required |
|----------|-------------|-----------------|
| CRITICAL | Exploitable vulnerability with severe impact | Immediate fix required |
| HIGH | Significant security risk | Fix before deployment |
| MEDIUM | Potential security issue | Fix in next release |
| LOW | Minor security concern | Consider fixing |
| INFO | Security best practice suggestion | Optional improvement |

## Key Files to Scan

### Always Check
- `**/*.py` - All Python source files
- `requirements.txt`, `Pipfile`, `pyproject.toml` - Dependencies
- `setup.py`, `setup.cfg` - Package configuration
- `config.py`, `settings.py` - Configuration files
- `**/secrets*`, `**/credentials*` - Obvious secret locations

### Environment Files
- `.env.example`, `.env.template` - **SCAN** for template analysis
- `.env`, `.env.local`, `.env.production` - **SKIP** by default (contain real secrets)

**Note:** Real `.env` files should never be committed to version control. The scanner analyzes `.env.example` templates to ensure proper documentation of required variables.

### High Priority Locations
- `app.py`, `main.py`, `wsgi.py` - Entry points
- `**/views.py`, `**/routes.py` - Request handlers
- `**/api/**/*.py` - API endpoints
- `**/auth*`, `**/login*` - Authentication code
- `**/models.py` - Database models
- `**/serializers.py` - Data serialization
- `**/middleware.py` - Middleware code

### Framework-Specific
**Flask:**
- `app.py`, `__init__.py` - Application factory
- `**/blueprints/**` - Blueprint routes
- `templates/**` - Jinja2 templates

**Django:**
- `settings.py`, `**/settings/*.py` - Django settings
- `urls.py` - URL configuration
- `**/views.py` - View functions/classes
- `**/forms.py` - Form definitions
- `templates/**` - Django templates

**FastAPI:**
- `main.py` - Application entry
- `**/routers/**` - API routers
- `**/dependencies.py` - Dependency injection
- `**/schemas.py` - Pydantic models

## Output Format

Findings should be reported as:

```
[SEVERITY] Category: Description
  File: path/to/file.py:lineNumber
  Code: <relevant code snippet>
  Risk: <explanation of the security risk>
  Fix: <recommended remediation>
```

## Integration with CI/CD

This skill can generate output compatible with:
- GitHub Security Advisories
- SARIF format for GitHub Code Scanning
- JSON for custom integrations
- JUnit XML for CI pipelines

## References

Load additional context as needed:
- `references/owasp-top-10.md` - OWASP Top 10:2025 quick reference
- `references/python-vulnerabilities.md` - Python-specific vulnerabilities
- `references/injection-patterns.md` - Injection vulnerability patterns
- `references/deserialization.md` - Insecure deserialization patterns
- `references/flask-security.md` - Flask security guide
- `references/django-security.md` - Django security guide
- `references/fastapi-security.md` - FastAPI security guide

Related Skills

python-workflow

16
from diegosouzapw/awesome-omni-skill

Python project workflow guidelines. Triggers: .py, pyproject.toml, uv, pip, pytest, Python. Covers package management, virtual environments, code style, type safety, testing, configuration, CQRS patterns, and Python-specific development tasks.

python-workflow-development

16
from diegosouzapw/awesome-omni-skill

Develop Python scripts and modules for building AI workflows and integrations. Use when coding data ingestion, transformation, analysis, and automation pipelines in pilot projects requiring Python automation.

python-typing

16
from diegosouzapw/awesome-omni-skill

Migrate Python codebases to strict type checking with pyright. Use when user wants to add types, fix type errors, set up strict mode, or run a typing migration. Provides setup automation, fix patterns, discipline enforcement, and optional iteration loop support.

python-testing

16
from diegosouzapw/awesome-omni-skill

Use when implementing new Python code (follow TDD), designing test suites, reviewing test coverage, setting up pytest infrastructure, writing fixtures, mocking dependencies, or performing parametrized testing

python-testing-patterns

16
from diegosouzapw/awesome-omni-skill

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

python-specialist

16
from diegosouzapw/awesome-omni-skill

Deliver production-quality Python solutions with framework-aware patterns and tests.

python-setup-dev-environment

16
from diegosouzapw/awesome-omni-skill

Set up and run a reproducible Python dev environment with uv, ruff, mypy, and VSCode.

python-project

16
from diegosouzapw/awesome-omni-skill

Scaffold and harden Python projects using vpngw-aligned defaults (pyproject/setuptools-scm, src layout, Ruff, pytest, Typer, Pydantic) plus best practices for CLI tools, systemd services, APIs/UI apps, IaC/automation, security/networking, and AI/ML workflows.

python-programmer

16
from diegosouzapw/awesome-omni-skill

Python programmer specialising in functional programming, clean code, documentation, and code quality using ruff and uv.

python-pro

16
from diegosouzapw/awesome-omni-skill

Master Python 3.12+ with modern features, async programming,

python

16
from diegosouzapw/awesome-omni-skill

Python coding conventions and guidelines Triggers on: **/*.py

python-performance-optimization

16
from diegosouzapw/awesome-omni-skill

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.