DockSec

AI-powered Docker security scanner that explains vulnerabilities in plain English

Quick Start • Features • Installation • Usage • Contributing

🏆 Officially recognized as an OWASP Incubator Project

Trusted by the global security community • 14,000+ downloads

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What is DockSec?

DockSec is an OWASP Incubator Project that combines traditional Docker security scanners (Trivy, Hadolint, Docker Scout) with AI to provide context-aware security analysis. Instead of dumping 200 CVEs and leaving you to figure it out, DockSec:

• Prioritizes what actually matters
• Explains vulnerabilities in plain English
• Suggests specific fixes for YOUR Dockerfile
• Generates professional security reports

Think of it as having a security expert review your Dockerfiles.

Why OWASP Recognition Matters

Being recognized as an OWASP Incubator Project means:

• ✅ Peer-reviewed by security professionals
• ✅ Community-driven development and governance
• ✅ Trusted by enterprises and security teams worldwide
• ✅ Open source with transparent security practices
• ✅ Active maintenance and regular updates

Join thousands of developers using DockSec to secure their containers.

How It Works

DockSec workflow: From scanning to actionable insights

DockSec follows a simple pipeline:

1. Scan - Runs Trivy, Hadolint, and Docker Scout on your images/Dockerfiles
2. Analyze - AI processes all findings and correlates them with your setup
3. Recommend - Get plain English explanations with specific line-by-line fixes
4. Report - Export results in JSON, PDF, HTML, or Markdown formats

Quick Start

# Install
pip install docksec

# Scan your Dockerfile
docksec Dockerfile

# Scan with image analysis
docksec Dockerfile -i myapp:latest

# Scan without AI (no API key needed)
docksec Dockerfile --scan-only

Features

• Smart Analysis: AI explains what vulnerabilities mean for your specific setup
• Multiple LLM Providers: Support for OpenAI, Anthropic Claude, Google Gemini, and Ollama (local models)
• Multiple Scanners: Integrates Trivy, Hadolint, and Docker Scout
• Security Scoring: Get a 0-100 score to track improvements
• Multiple Formats: Export reports as HTML, PDF, JSON, or CSV
• No AI Required: Works offline with --scan-only mode
• CI/CD Ready: Easy integration into build pipelines

Installation

Requirements: Python 3.12+, Docker (for image scanning)

pip install docksec

For AI features, choose your preferred LLM provider:

OpenAI (Default)

export OPENAI_API_KEY="your-key-here"

Anthropic Claude

export ANTHROPIC_API_KEY="your-key-here"
export LLM_PROVIDER="anthropic"
export LLM_MODEL="claude-3-5-sonnet-20241022"

Google Gemini

export GOOGLE_API_KEY="your-key-here"
export LLM_PROVIDER="google"
export LLM_MODEL="gemini-1.5-pro"

Ollama (Local Models)

# First, install and run Ollama: https://ollama.ai
# Then pull a model: ollama pull llama3.1
export LLM_PROVIDER="ollama"
export LLM_MODEL="llama3.1"
# Optional: customize Ollama URL
export OLLAMA_BASE_URL="http://localhost:11434"

External tools (optional, for full scanning):

# Install Trivy and Hadolint
python -m docksec.setup_external_tools

# Or install manually:
# - Trivy: https://aquasecurity.github.io/trivy/
# - Hadolint: https://github.com/hadolint/hadolint

Usage

Basic Scanning

# Analyze Dockerfile with AI recommendations
docksec Dockerfile

# Scan Dockerfile + Docker image
docksec Dockerfile -i nginx:latest

# Get only scan results (no AI)
docksec Dockerfile --scan-only

# Scan image without Dockerfile
docksec --image-only -i nginx:latest

# Use specific LLM provider and model
docksec Dockerfile --provider anthropic --model claude-3-5-sonnet-20241022

# Use local Ollama model
docksec Dockerfile --provider ollama --model llama3.1

CLI Options

Option	Description
dockerfile	Path to Dockerfile
-i, --image	Docker image to scan
-o, --output	Output file path
--provider	LLM provider (openai, anthropic, google, ollama)
--model	Model name (e.g., gpt-4o, claude-3-5-sonnet-20241022)
--ai-only	AI analysis only (no scanning)
--scan-only	Scanning only (no AI)
--image-only	Scan image without Dockerfile

Configuration

Create a .env file for advanced configuration:

# LLM Provider Configuration
LLM_PROVIDER=openai                    # Options: openai, anthropic, google, ollama
LLM_MODEL=gpt-4o                       # Model to use
LLM_TEMPERATURE=0.0                    # Temperature (0-1)

# API Keys
OPENAI_API_KEY=your-openai-key
ANTHROPIC_API_KEY=your-anthropic-key
GOOGLE_API_KEY=your-google-key

# Ollama Configuration (for local models)
OLLAMA_BASE_URL=http://localhost:11434

# Scanning Configuration
TRIVY_SCAN_TIMEOUT=600
DOCKSEC_DEFAULT_SEVERITY=CRITICAL,HIGH

See full configuration options.

Example Output

🔍 Scanning Dockerfile...
⚠️  Security Score: 45/100

Critical Issues (3):
  • Running as root user (line 12)
  • Hardcoded API key detected (line 23)
  • Using vulnerable base image

💡 AI Recommendations:
  1. Add non-root user: RUN useradd -m appuser && USER appuser
  2. Move secrets to environment variables or build secrets
  3. Update FROM ubuntu:20.04 to ubuntu:22.04 (fixes 12 CVEs)

📊 Full report: results/nginx_latest_report.html

Architecture

Dockerfile → [Trivy + Hadolint + Scout] → AI Analysis → Reports

DockSec runs security scanners locally, then uses AI to:

1. Combine and deduplicate findings
2. Assess real-world impact for your context
3. Generate actionable remediation steps
4. Calculate security score

Supported AI Providers:

• OpenAI: GPT-4o, GPT-4 Turbo, GPT-3.5 Turbo
• Anthropic: Claude 3.5 Sonnet, Claude 3 Opus
• Google: Gemini 1.5 Pro, Gemini 1.5 Flash
• Ollama: Llama 3.1, Mistral, Phi-3, and other local models

All scanning happens on your machine. Only scan results (not your code) are sent to the AI provider when using AI features.

Roadmap

• Multiple LLM provider support (OpenAI, Anthropic, Google, Ollama)
• Docker Compose support
• Kubernetes manifest scanning
• GitHub Actions integration
• Custom security policies

See open issues or suggest features in discussions.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Quick links:

• Report a bug
• Request a feature
• View roadmap

Documentation

• Installation Guide
• Configuration Options
• Changelog
• Security Policy

Troubleshooting

"No OpenAI API Key provided"
→ Set appropriate API key for your provider (OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY) or use --scan-only mode

"Unsupported LLM provider"
→ Valid providers: openai, anthropic, google, ollama. Set with --provider flag or LLM_PROVIDER env var

"Hadolint not found"
→ Run python -m docksec.setup_external_tools

"Python version not supported"
→ DockSec requires Python 3.12+. Use pyenv install 3.12 to upgrade.

"Connection refused" with Ollama
→ Make sure Ollama is running: ollama serve and the model is pulled: ollama pull llama3.1

"Where are my scan results?"
→ Results are saved to results/ directory in your DockSec installation
→ Customize location: export DOCKSEC_RESULTS_DIR=/custom/path

For more issues, see Troubleshooting Guide.

License

MIT License - see LICENSE for details.

Recognition & Community

DockSec is proud to be an OWASP Incubator Project, recognized by the Open Web Application Security Project for its contribution to application security.

What This Means

• Vetted by Security Experts: OWASP projects undergo rigorous review
• Community Trust: Join thousands of security professionals using OWASP tools
• Enterprise Ready: OWASP recognition provides confidence for enterprise adoption
• Long-term Sustainability: Backed by a global nonprofit foundation

Links

• OWASP Project Page: https://owasp.org/www-project-docksec/
• PyPI: https://pypi.org/project/docksec/
• Issues: https://github.com/advaitpatel/DockSec/issues
• Discussions: https://github.com/advaitpatel/DockSec/discussions

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Built with ❤️ by Advait Patel