Skip to content

Latest commit

 

History

History
163 lines (122 loc) · 124 KB

sec-design-deep-analysis.md

File metadata and controls

163 lines (122 loc) · 124 KB

Locust Security Analysis: Deep Dive

1. Objective, Scope, and Methodology

Objective: To conduct a thorough security analysis of the Locust load testing tool, focusing on its key components, architecture, data flow, and deployment scenarios. The analysis aims to identify potential security vulnerabilities, assess their impact, and propose actionable mitigation strategies. The primary goal is to improve the overall security posture of Locust and provide guidance to users on secure deployment and usage. We will specifically analyze the security implications of:

  • The Web UI (Flask-based).
  • The Master-Worker communication.
  • The execution of user-provided Locustfiles.
  • The build and deployment processes.
  • Integration with external systems (reporting).

Scope: This analysis covers the Locust core codebase, its web UI, the master-worker architecture, the build process (as described by the provided GitHub Actions workflow), and common deployment scenarios (especially Kubernetes). It does not cover the security of the target systems being tested, as that is outside Locust's control. It also does not cover every possible third-party library used by Locust, but focuses on major dependencies and their security implications.

Methodology:

  1. Architecture and Component Inference: Based on the provided C4 diagrams, codebase information (GitHub repository, workflow files), and official Locust documentation, we infer the architecture, components, and data flow.
  2. Threat Modeling: For each identified component and interaction, we perform threat modeling using a combination of STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) and consideration of the business and security posture outlined in the security design review.
  3. Vulnerability Identification: We identify potential vulnerabilities based on the threat modeling and known security best practices.
  4. Impact Assessment: We assess the potential impact of each vulnerability, considering factors like confidentiality, integrity, and availability.
  5. Mitigation Strategies: We propose actionable and tailored mitigation strategies for each identified vulnerability, prioritizing practical and effective solutions.

2. Security Implications of Key Components

2.1 Web UI (Flask-based)

  • Architecture: The web UI is built using the Flask web framework. It interacts with the Locust master process via RPC (Remote Procedure Call). It's served over HTTP by default.

  • Threat Modeling:

    • Cross-Site Scripting (XSS): If user-provided input (e.g., test names, configuration parameters) is not properly sanitized before being displayed in the web UI, an attacker could inject malicious JavaScript code. This could lead to session hijacking, defacement, or redirection to malicious sites. (STRIDE: Tampering, Information Disclosure)
    • Cross-Site Request Forgery (CSRF): Without CSRF protection, an attacker could trick a logged-in user into performing actions on the Locust web UI without their knowledge (e.g., starting or stopping tests, changing configurations). (STRIDE: Tampering)
    • Authentication Bypass: The default lack of authentication allows anyone with network access to the web UI to control the load tests. This is a significant risk in shared environments or if the UI is exposed to the internet. (STRIDE: Spoofing, Elevation of Privilege)
    • Injection Attacks (other than XSS): Depending on how user input is used in backend logic (e.g., constructing file paths, database queries), other injection attacks might be possible. (STRIDE: Tampering)
    • Denial of Service (DoS): The web UI itself could be targeted with a DoS attack, making it unavailable for legitimate users. (STRIDE: Denial of Service)

  • Vulnerabilities:

    • Lack of built-in authentication (by default).
    • Potential for XSS vulnerabilities if input sanitization is inadequate.
    • Potential for CSRF vulnerabilities if proper tokens are not used.
    • Potential for other injection attacks depending on backend logic.

  • Impact: High. Compromise of the web UI could allow an attacker to control the load tests, potentially causing damage to the target system or exfiltrating sensitive data (if present in the Locustfile or test results).

  • Mitigation Strategies:

    • Implement Authentication: Strongly recommend adding built-in authentication options (e.g., basic auth, OAuth 2.0, OpenID Connect) or providing clear integration guides for existing authentication systems. This should be configurable and easily enabled.
    • Implement CSRF Protection: Use Flask's built-in CSRF protection mechanisms (e.g., flask_wtf.csrf). Ensure all state-changing requests (POST, PUT, DELETE) are protected.
    • Implement Content Security Policy (CSP): A strict CSP can significantly mitigate XSS vulnerabilities by restricting the sources from which the browser can load resources (scripts, stylesheets, images, etc.). This should be a default setting.
    • Input Validation and Sanitization: Thoroughly validate and sanitize all user-provided input before it is used in the web UI or passed to the backend. Use a dedicated sanitization library. Escape HTML output appropriately.
    • Rate Limiting: Implement rate limiting on the web UI endpoints to mitigate DoS attacks.
    • Regular Security Audits: Conduct regular security audits of the web UI code, including penetration testing.

2.2 Master-Worker Communication

  • Architecture: The master process communicates with worker processes via RPC. By default, this communication is unencrypted. The specific RPC mechanism isn't detailed, but it's likely a custom protocol or a library like ZeroMQ.

  • Threat Modeling:

    • Man-in-the-Middle (MitM) Attack: Without encryption, an attacker on the same network could intercept and potentially modify the communication between the master and worker processes. This could lead to altered test configurations, manipulated results, or even code injection. (STRIDE: Tampering, Information Disclosure)
    • Replay Attacks: An attacker could capture and replay legitimate RPC messages to disrupt the load test or cause unintended actions. (STRIDE: Tampering)
    • Denial of Service (DoS): The RPC communication channels could be targeted with a DoS attack, preventing the master from coordinating the workers. (STRIDE: Denial of Service)

  • Vulnerabilities:

    • Lack of encryption by default.
    • Potential for replay attacks if the RPC mechanism doesn't include proper sequencing or nonces.

  • Impact: Medium to High. Compromise of the master-worker communication could allow an attacker to manipulate the load test, potentially leading to inaccurate results or even damage to the target system.

  • Mitigation Strategies:

    • Enforce TLS Encryption: Provide a built-in option to enable TLS encryption for all master-worker communication. This should be strongly recommended in the documentation, and ideally, be the default setting. Use strong cipher suites and protocols.
    • Authentication of Master and Workers: Implement mutual authentication between the master and worker processes using certificates or pre-shared keys. This prevents unauthorized workers from joining the test or a rogue master from controlling legitimate workers.
    • Message Integrity Checks: Use message authentication codes (MACs) or digital signatures to ensure the integrity of RPC messages and prevent tampering.
    • Replay Protection: Include sequence numbers or timestamps in RPC messages to prevent replay attacks.
    • Network Segmentation: Deploy master and worker processes in a secure network segment, limiting network access to only authorized hosts. Use Kubernetes network policies if deploying in a Kubernetes environment.

2.3 Execution of User-Provided Locustfiles

  • Architecture: Locust executes arbitrary Python code provided by the user in the Locustfile. This code defines the simulated user behavior and interacts with the target system.

  • Threat Modeling:

    • Malicious Code Execution: The most significant risk. A malicious or poorly written Locustfile could contain code that:
      • Attempts to access or modify resources on the Locust host machine (e.g., reading files, executing system commands).
      • Contains vulnerabilities that could be exploited by the target system (e.g., SQL injection, command injection).
      • Launches attacks against other systems (not the intended target).
      • Consumes excessive resources on the Locust host, leading to a denial of service. (STRIDE: All)

  • Vulnerabilities:

    • Inherent trust in user-provided code.

  • Impact: Very High. Malicious code execution could lead to complete compromise of the Locust host machine, damage to the target system, or attacks against other systems.

  • Mitigation Strategies:

    • Documentation and Warnings: Clearly document the risks associated with running untrusted Locustfiles. Emphasize that users are responsible for the security of their code.
    • Sandboxing (Limited): While full sandboxing of Python code is difficult, consider using techniques like:
      • Restricting Imports: Limit the Python modules that can be imported in Locustfiles. This can prevent access to potentially dangerous functions (e.g., os, subprocess). Provide a whitelist of allowed modules.
      • Resource Limits: Use operating system mechanisms (e.g., ulimit on Linux, resource limits in Docker/Kubernetes) to limit the resources (CPU, memory, file descriptors) that Locust worker processes can consume.
      • Running as a Non-Root User: Run Locust worker processes as a non-root user with limited privileges. This minimizes the potential damage from malicious code.
    • Code Review: If possible, implement a code review process for Locustfiles before they are executed, especially in shared environments.
    • Security Linters: Encourage users to use security linters (e.g., Bandit) to identify potential vulnerabilities in their Locustfiles.
    • Input Sanitization Libraries: Provide or recommend libraries that help users sanitize input and prevent injection attacks in their Locustfiles.
    • Consider using a separate process/container for each user's Locustfile: This provides stronger isolation, but may impact performance.

2.4 Build and Deployment Processes

  • Architecture: The build process uses GitHub Actions (linting with flake8, testing with pytest, packaging). Deployment can be on a local machine, dedicated servers, Docker containers, or Kubernetes.

  • Threat Modeling:

    • Compromised Build Environment: If the GitHub Actions environment is compromised, an attacker could inject malicious code into the Locust package. (STRIDE: Tampering)
    • Vulnerable Dependencies: Locust depends on third-party libraries (e.g., Flask, gevent). If these libraries have vulnerabilities, Locust could be indirectly affected. (STRIDE: Various, depending on the vulnerability)
    • Unsigned Packages: If the packages uploaded to PyPI are not signed, an attacker could potentially replace them with malicious versions. (STRIDE: Tampering)
    • Insecure Deployment Configuration: Misconfigured deployments (e.g., exposing the web UI without authentication, using default credentials) could create vulnerabilities. (STRIDE: Various)

  • Vulnerabilities:

    • Lack of SAST and dependency scanning in the CI/CD pipeline.
    • Potential for unsigned packages.
    • Deployment-specific vulnerabilities depending on the chosen method.

  • Impact: Medium to High. Compromised builds or insecure deployments could lead to the execution of malicious code or unauthorized access to the Locust system.

  • Mitigation Strategies:

    • SAST Integration: Integrate Static Application Security Testing (SAST) tools (e.g., Bandit, Snyk) into the GitHub Actions workflow to scan the Locust codebase for vulnerabilities.
    • Dependency Scanning: Integrate dependency scanning tools (e.g., pip-audit, Dependabot) to identify and update vulnerable dependencies. Automate this process.
    • SBOM Generation: Generate a Software Bill of Materials (SBOM) for each release to provide transparency about the included components and their versions.
    • Package Signing: Sign releases uploaded to PyPI using a tool like twine and a GPG key. This ensures the integrity of the packages.
    • Secure Deployment Guides: Provide detailed and secure deployment guides for each supported deployment method (local, Docker, Kubernetes). These guides should cover topics like:
      • Authentication and authorization for the web UI.
      • TLS encryption for master-worker communication.
      • Network security configurations.
      • Resource limits.
      • Running as a non-root user.
    • Kubernetes Security Best Practices: If deploying to Kubernetes, follow Kubernetes security best practices, including:
      • Using RBAC to restrict access to Locust resources.
      • Implementing network policies to limit network traffic.
      • Using pod security policies (or a Pod Security Admission controller) to enforce security constraints on pods.
      • Regularly updating Kubernetes and its components.
      • Using a secure container registry.
      • Scanning container images for vulnerabilities.

2.5 Integration with External Systems (Reporting)

  • Architecture: Locust can send test results to external reporting systems (e.g., Graphite, Prometheus).

  • Threat Modeling:

    • Data Exfiltration: If the communication with the reporting system is unencrypted, an attacker could intercept the test results. While test results themselves might not be highly sensitive, they could reveal information about the target system's performance and architecture. (STRIDE: Information Disclosure)
    • Compromised Reporting System: If the reporting system is compromised, an attacker could potentially use it to launch attacks against Locust or other systems. (STRIDE: Various)

  • Vulnerabilities:

    • Potential for unencrypted communication.
    • Reliance on the security of the external reporting system.

  • Impact: Low to Medium. Data exfiltration or compromise of the reporting system could have some impact, but it's generally less critical than vulnerabilities in the core Locust components.

  • Mitigation Strategies:

    • Encrypted Communication: Use encrypted communication (e.g., TLS) when sending data to external reporting systems.
    • Authentication: Use authentication mechanisms (e.g., API keys, credentials) to secure access to the reporting system.
    • Secure the Reporting System: Ensure the reporting system itself is properly secured, following its own security best practices.
    • Data Minimization: Only send the necessary data to the reporting system. Avoid sending any sensitive information.

3. Summary of Recommendations

The following table summarizes the key recommendations, prioritized by their importance:

| Recommendation | Priority | Component(s) Affected | Description

This detailed analysis provides a comprehensive overview of the security considerations for Locust, covering its architecture, components, data flow, and deployment scenarios. It identifies specific threats, vulnerabilities, and actionable mitigation strategies to improve the security posture of the tool and guide users in its secure deployment and usage. The breakdown by component allows for targeted improvements and a clear understanding of the security implications of each part of the system.