sec-design-deep-analysis.md

Okay, let's perform a deep security analysis of the GitHub Scientist library based on the provided design review.

1. Objective, Scope, and Methodology

• Objective: To conduct a thorough security analysis of the GitHub Scientist library, focusing on its key components and their interactions within a larger application context. The goal is to identify potential security vulnerabilities arising from the use of Scientist, not inherent vulnerabilities within the library itself (though those will be noted if found). We aim to provide actionable mitigation strategies. We'll pay particular attention to data exposure, performance impacts that could lead to denial-of-service, and error handling that could destabilize the application.

• Scope:

  • The Scientist library's core functionality (running experiments, comparing results, publishing results).
  • The interaction between Scientist and the application using it.
  • The interaction between Scientist and the result publisher.
  • The deployment environment (Kubernetes, as specified).
  • The build process (CI/CD pipeline).
  • We exclude the security of the "Existing System/Database" and the "Result Publisher" themselves, except where Scientist's interaction with them creates a vulnerability. We assume these external systems have their own security controls.

• Methodology:

  1. Codebase and Documentation Review: Analyze the provided design document, including the C4 diagrams and security posture descriptions. Infer architectural details and data flows. Examine the Scientist library's public documentation (https://github.com/github/scientist) and, if necessary, relevant parts of the source code to clarify behavior.
  2. Threat Modeling: Identify potential threats based on the identified components, data flows, and trust boundaries. We'll use a combination of STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) and consideration of common web application vulnerabilities.
  3. Vulnerability Analysis: Assess the likelihood and impact of each identified threat, considering existing security controls.
  4. Mitigation Recommendations: Propose specific, actionable mitigation strategies to address the identified vulnerabilities. These will be tailored to the use of Scientist within the application.

2. Security Implications of Key Components

Let's break down the security implications of the key components identified in the design review, focusing on how they interact with Scientist:

• Application using Scientist:

  • Threats:

    • Information Disclosure: If the application passes sensitive data to Scientist (either as input to the code paths or as part of the results), and the result publisher is not configured correctly, this data could be leaked.
    • Denial of Service: If the candidate code path is significantly slower than the control path, or if it consumes excessive resources, this could degrade application performance or even cause a denial-of-service. Scientist's percentage-based sampling mitigates this, but doesn't eliminate it.
    • Tampering: If the candidate code path has side effects (e.g., writing to the database), and the experiment is not properly controlled, this could lead to data corruption or inconsistent application state. This is a major concern.
    • Elevation of Privilege: If the candidate code path has different authorization requirements than the control path, and this is not handled correctly, it could lead to unauthorized access to data or functionality.
    • Code Injection: If the application dynamically constructs the code to be executed in either the control or candidate path based on user input, and this input is not properly validated, it could lead to code injection vulnerabilities. Scientist itself doesn't prevent this; it relies on the application's security.

  • Existing Controls: The application is responsible for authentication, authorization, and input validation.

• Scientist Library:

  • Threats:

    • Denial of Service: While Scientist aims to minimize performance overhead, poorly written experiments or custom comparison logic could still introduce significant delays.
    • Information Disclosure: The library itself doesn't directly handle sensitive data, but it facilitates its exposure if the application and publisher are not configured correctly. The publish method is a critical point here.
    • Logic Errors: Bugs in the Scientist library itself could lead to incorrect experiment results or unexpected behavior. This is less a security vulnerability and more a reliability concern, but it could have security implications if decisions are made based on flawed experiment data.

  • Existing Controls: Percentage-based sampling, ability to ignore known differences, custom comparison logic. These are features that can be used for security, but they are not inherent security controls.

• Result Publisher:

  • Threats:

    • Information Disclosure: This is the primary threat. The publisher is responsible for handling the experiment results, which may contain sensitive data. If the publisher logs to an insecure location, sends data over an unencrypted channel, or has weak access controls, this data could be exposed.
    • Data Integrity: If the publisher is compromised, it could be used to inject false experiment results, potentially leading to incorrect decisions about code deployments.

  • Existing Controls: The design document states that the publisher must ensure sensitive data is not exposed, but it doesn't specify how. This is a critical gap.

• Control Code Path & Candidate Code Path:

  • Threats: These are essentially extensions of the "Application using Scientist" threats. The difference in behavior between these paths is what Scientist is designed to detect, but this difference can also introduce vulnerabilities.

    • Side Effects: The most significant threat is that the candidate path might have unintended side effects that the control path does not. For example, writing to a database, sending emails, or interacting with external services. Scientist does not automatically roll back these side effects.
    • Authorization Differences: If the candidate path bypasses or alters authorization checks, it could lead to privilege escalation.
    • Error Handling: Differences in error handling could lead to application instability or information disclosure.

  • Existing Controls: Relies entirely on the application's existing security.

• Kubernetes Deployment:

  • Threats: The deployment environment introduces its own set of threats, but these are largely independent of Scientist. However, if the application is compromised, the attacker could potentially access the experiment results (if they are stored within the container or accessible from it).

  • Existing Controls: Kubernetes provides various security controls (network policies, pod security policies, etc.), but these need to be configured correctly.

3. Inferring Architecture, Components, and Data Flow

Based on the documentation and the nature of the library, we can infer the following:

• Architecture: Scientist is a library that is embedded within the application. It's not a separate service or process. This means its security is intimately tied to the application's security.

• Components:

  • Scientist.science: The main entry point for defining an experiment. This likely involves registering the control and candidate code blocks (lambdas or methods).
  • Experiment: An internal object (likely a class) that manages the execution of the experiment, including sampling, running the code paths, comparing results, and publishing.
  • Result: An object that encapsulates the results of the experiment (return values, exceptions, timing information).
  • Publisher: An interface or abstract class that defines how results are published. The application provides a concrete implementation.

• Data Flow:

  1. A user request comes into the application.
  2. The application code reaches a point where an experiment is defined using Scientist.science.
  3. Scientist determines (based on sampling) whether to run the experiment.
  4. If the experiment runs:
     • The control code path is executed.
     • The candidate code path is executed.
     • The results (return values, exceptions) are captured.
     • The results are compared (using custom comparison logic, if provided).
     • A Result object is created.
     • The Result object is passed to the configured Publisher.
  5. The application continues, using the return value of the control code path.
  6. The Publisher processes the Result (e.g., logs it, sends it to a metrics system).

4. Tailored Security Considerations

Given the above, here are specific security considerations for using Scientist:

• Side Effect Management: This is the most critical consideration. Scientist is designed for read-only experiments. If the candidate code path has any side effects, you must take extreme care to prevent data corruption or inconsistent state.

  • Recommendation: Never allow the candidate code path to modify the database, external systems, or any shared state unless you have a robust mechanism for rolling back those changes. Consider using database transactions (if applicable) and wrapping the candidate code in a begin...rescue...ensure block to guarantee cleanup. Even better, refactor the code to avoid side effects in the candidate path altogether. This might involve creating a "dry run" mode or using mock objects.
  • Example (Bad):

    Scientist.science('create_user') do |experiment|
      experiment.use { User.create(params) } # Control: Creates the user
      experiment.try { UserCreator.new(params).create } # Candidate: Also creates the user!
    end

  • Example (Better - with transaction):

    Scientist.science('create_user') do |experiment|
      experiment.use { User.create(params) } # Control: Creates the user
      experiment.try do
        ActiveRecord::Base.transaction do
          UserCreator.new(params).create # Candidate: Creates the user
          raise ActiveRecord::Rollback # Always roll back the transaction
        end
      end
    end

  • Example (Best - no side effects):

      Scientist.science('create_user') do |experiment|
        experiment.use { User.new(params) } # Control: Creates user object
        experiment.try { UserCreator.new(params).build_user } # Candidate: Creates user object
      end

• Data Sanitization: The Publisher is responsible for sanitizing data. You must implement a custom publisher that explicitly removes or anonymizes any sensitive data before it is logged or sent to a monitoring system.

  • Recommendation: Create a dedicated ScientistPublisher class that inherits from a base publisher (or implements an interface). This class should have a sanitize method that takes a Result object and removes any sensitive fields. Use a whitelist approach (explicitly allow known-safe fields) rather than a blacklist approach (trying to remove known-sensitive fields).
  • Example (Bad):

    # Publishing directly to logs without sanitization
    Scientist.configure do |config|
      config.publish = ->(result) { Rails.logger.info(result.to_h) }
    end

  • Example (Good):

    class MyScientistPublisher
      def publish(result)
        sanitized_data = sanitize(result.to_h)
        Rails.logger.info(sanitized_data)
        # Or send to a monitoring service:
        # MyMonitoringService.send(sanitized_data)
      end
    
      private
    
      def sanitize(data)
        # Remove sensitive fields:
        data.delete(:user_password)
        data.delete(:credit_card_number)
        # ... etc.
        # Or, whitelist allowed fields:
        # allowed_fields = [:control_value, :candidate_value, :duration]
        # data.slice(*allowed_fields)
        data
      end
    end
    
    Scientist.configure do |config|
      config.publish = MyScientistPublisher.new
    end

• Performance Monitoring: Scientist's performance impact should be closely monitored. Use application performance monitoring (APM) tools to track the execution time of both the control and candidate code paths. Set alerts for significant performance regressions.

  • Recommendation: Integrate Scientist with your existing APM solution. Many APM tools allow you to add custom metrics. Record the execution time of the control and candidate paths, as well as the overall experiment duration. Set alerts based on thresholds (e.g., if the candidate path is more than 2x slower than the control path).

• Error Handling: Exceptions in the candidate code path should never impact the user. They should be caught, logged, and reported, but the application should continue to function using the control path's result.

  • Recommendation: Scientist likely already handles exceptions internally, but you should verify this. Ensure that your Publisher logs any exceptions that occur in the candidate path. Use an error tracking service (e.g., Sentry, Bugsnag) to monitor these exceptions.

• Authorization: If the control and candidate code paths have different authorization requirements, you must ensure that the correct authorization context is used for each path.

  • Recommendation: Avoid this situation if possible. If it's unavoidable, explicitly set the authorization context (e.g., the current user) before executing each code path. Be extremely careful to avoid privilege escalation.

• Experiment Naming: Use descriptive and consistent experiment names. This improves readability and helps with debugging and monitoring.

  • Recommendation: Use a naming convention that includes the feature being tested and the date, e.g., user_registration_refactor_20231027.

• Code Review: Scientist experiments should be subject to the same rigorous code review process as any other code change. Pay particular attention to side effects, data handling, and error handling.

• Disable in Sensitive Contexts: Do not use Scientist in contexts where even a small risk of data exposure or performance degradation is unacceptable (e.g., during critical financial transactions).

5. Mitigation Strategies

The above considerations already include detailed mitigation strategies. Here's a summary:

Threat	Mitigation Strategy
Side Effects in Candidate Path	Never allow the candidate path to modify shared state without a robust rollback mechanism (e.g., database transactions, ensure blocks). Ideally, refactor to avoid side effects entirely. Use "dry run" modes or mocks.
Information Disclosure	Implement a custom Publisher that explicitly sanitizes all data before publishing it. Use a whitelist approach for allowed fields. Never log raw Result objects.
Denial of Service	Monitor the performance of both code paths using APM tools. Set alerts for significant regressions. Use Scientist's percentage-based sampling to limit the impact of slow candidate paths. Consider circuit breakers to automatically disable experiments if they are causing problems.
Authorization Differences	Avoid this situation if possible. If unavoidable, explicitly set the authorization context before executing each code path. Be extremely careful to prevent privilege escalation.
Errors in Candidate Path	Ensure that exceptions in the candidate path are caught, logged, and reported, but do not impact the user. Use an error tracking service. The application should always use the control path's result.
Logic Errors in Scientist	Regularly update the Scientist library to the latest version. Report any suspected bugs to the maintainers.
Tampering (Data Corruption)	Same as "Side Effects in Candidate Path".
Code Injection (in Application)	This is the responsibility of the application, not Scientist. Ensure proper input validation and sanitization throughout the application.
Compromised Result Publisher	This is outside the scope of Scientist's direct control. Secure the result publisher itself (access controls, network security, etc.). Consider using a dedicated, isolated service for publishing experiment results.
Insecure Kubernetes Deployment	This is outside the scope of Scientist's direct control. Follow Kubernetes security best practices (network policies, pod security policies, image scanning, etc.).
Insecure Build Process	Use SAST tools, linters, and container image scanning in the CI/CD pipeline. Store build artifacts securely.

This deep analysis provides a comprehensive overview of the security considerations for using GitHub Scientist. By implementing the recommended mitigation strategies, the development team can significantly reduce the risks associated with refactoring code in a production environment. The most critical areas to focus on are side-effect management and data sanitization in the result publisher. Remember that Scientist is a powerful tool, but it must be used responsibly and with a strong understanding of its potential security implications.