Magpie Tutorial: Features, Use Cases, How It Works

TL;DR, What is Magpie?

Magpie is OpenRaven's open-source cloud security posture management (CSPM) tool. If you're managing security and compliance in dynamic cloud environments (like AWS, GCP, and Azure), Magpie can help you tackle “cloud security posture drift”—the continuous emergence of misconfigurations and security gaps. The tool offers automated cloud asset discovery and policy-based assessments, giving you the visibility to secure your infrastructure without the cost and complexity of commercial alternatives. The team at OpenRaven develops and maintains Magpie to give security and engineering teams an extensible tool for managing their cloud security posture.

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At-A-Glance

GitHub: https://github.com/openraven/magpie

License: Apache-2.0

Primary Language: Java

Stars: 192 ⭐

Last Release: August 2024

Topics/Tags: cspm, cloud-security, aws, gcp, azure, ransomware, supply-chain-attacks

Common use cases

1. Continuous Compliance Monitoring: You can deploy Magpie to continuously check your cloud infrastructure against standards like CIS Benchmarks, SOC 2, and PCI DSS. The tool automates resource discovery and policy application, giving you constant visibility into your compliance status and alerting you to violations.

2. Unified Multi-Cloud Security Assessment: If your infrastructure spans AWS, GCP, and Azure, Magpie provides a single view for security posture management. You can apply consistent security policies across all platforms, which simplifies managing different cloud environments.

3. DevSecOps Pipeline Integration: You can embed Magpie into CI/CD pipelines to shift security left. The tool's CLI and API allow you to run automated security checks against infrastructure-as-code templates before deployment, helping prevent misconfigurations from reaching production.

4. Incident Response and Forensic Analysis: During a security incident, your team can use Magpie's asset inventory and historical data to investigate quickly. The tool helps identify affected resources, analyze misconfigurations that led to a breach, and confirm that your fixes worked.

5. Custom Security Policy Enforcement: With Magpie's flexible Python rule engine, you can write and enforce your own security and governance requirements. The engine's flexibility lets you go beyond industry standards to build custom policies for your specific risks or operational needs.

How does Magpie work?

Magpie uses a modular, plugin-based architecture run by a core engine. The process starts with cloud-specific plugins that use native APIs to discover all assets and services across platforms like AWS, GCP, and Azure. The discovered inventory of cloud services and their configurations is then stored in a PostgreSQL database for analysis. Finally, Magpie’s Security Policy and Rules Engine checks this data against predefined and custom rules to find security misconfigurations and potential threats.

• Cloud-Specific Plugins: Magpie uses separate plugins (e.g., magpie-aws, magpie-gcp) to handle platform-specific API calls, which allows for extensible multi-cloud support.

• Pipelines and FIFOs: Magpie uses a scalable architecture where data flows through layers separated by FIFOs (local queues) or Kafka for scale.

• Policy & Rules Engine: The core engine processes the collected asset data, running security policies written in Python and Java to find vulnerabilities and misconfigurations.

• Pre-built Security Rules: Magpie comes with a library of out-of-the-box rules covering CIS Benchmarks, AWS Security Fundamentals, and ransomware detection policies.

Core Capabilities:

1. Multi-Cloud Asset Discovery: Magpie provides cloud asset discovery across AWS, GCP, and Azure. The tool's extensible architecture lists all services and resources to create a detailed inventory for security analysis. A clear inventory is key for tracking assets in complex deployments and forms the basis for managing your security posture. Discovered data can be stored in PostgreSQL for historical analysis or exported as JSON to use with other security tools.

2. Application-Level Visibility: Beyond scanning infrastructure, Magpie’s DMAP engine provides application-level insights. As a serverless function, DMAP analyzes compute instances to identify non-native applications. Magpie's ability to see beyond infrastructure configuration and uncover the software stack helps you get a more accurate assessment of your full attack surface.

3. Flexible Security Policy Engine: Magpie has a security policy engine that supports rules written in Java and Python, giving you flexibility for creating custom controls. The tool includes pre-built rules for industry standards like the AWS CIS Benchmarks to provide immediate value. The engine's extensibility allows you to codify internal governance policies and adapt to unique requirements, making Magpie a useful tool for DevSecOps compliance.

4. Threat and Ransomware Detection: Magpie includes detection rules that identify attacks like cloud ransomware and supply chain compromises. These rules analyze configurations for patterns that indicate a targeted attack, offering a layer of defense beyond static checks. A threat-centric approach helps you identify early signs of compromise and gives you security monitoring capabilities for modern attack vectors.

5. Modular Architecture for Unified Management: Magpie’s modular plugin architecture helps you consistently manage security across multiple clouds. Dedicated plugins for each provider handle platform-specific API calls, so your teams can apply unified policies across all environments from one place. A modular architecture simplifies managing cloud security posture drift by providing a single view of risks and ensuring you enforce governance standards everywhere.

Limitations

1. Requires In-House Expertise: As an open-source tool, Magpie requires engineering resources for deployment, configuration, and maintenance. Writing custom rules in Java packages demands more specialized skills than you might need for managed commercial solutions.

2. Potential for Operational Overhead: Running Magpie creates cloud service costs from serverless executions, database usage, and frequent API calls. You must actively manage these operational costs, as the total cost of ownership is not zero.

3. Lacks Automated Remediation: The tool focuses on discovery and detection but lacks the built-in, automated remediation features common in commercial CSPMs. Teams must create their own workflows to fix identified security issues.

4. Primary Focus on IaaS/PaaS: Magpie's capabilities are centered on cloud infrastructure and platform services. The tool's visibility into container orchestration platforms or SaaS application security postures is not explicitly detailed, which could create security blind spots.

5. Dependency on Cloud Provider APIs: The tool's effectiveness depends on cloud provider APIs. API changes, rate limiting, or outages can directly impact Magpie's discovery and analysis functions, requiring ongoing plugin maintenance and updates.

Getting Started:

Prerequisites: You must have Docker installed on your system.

Step 1: Pull the latest image:

docker pull quay.io/openraven/magpie:latest

Step 2: Run with AWS creds and limit to S3 (example):

docker run -a stdout -a stderr

--env MAGPIE_CONFIG="{'/plugins/magpie.aws.discovery/config/services':

['s3']}"

-e AWS_ACCESS_KEY_ID -e AWS_SECRET_ACCESS_KEY -e AWS_SESSION_TOKEN

quay.io/openraven/magpie:latest

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