mitigations.md

Mitigation Strategies Analysis for github/scientist

Mitigation Strategy: Rigorous Code Reviews for Experiment Logic within Scientist

• Description:

  1. Establish a mandatory code review process specifically for the experiment logic implemented using scientist's Experiment class and related constructs.
  2. Designate experienced developers or security-conscious team members as reviewers for this experiment logic code.
  3. Reviewers should focus on:
     • Understanding the experiment's purpose and the logic within the experiment.run() block, control() and candidate() methods.
     • Identifying potential security vulnerabilities introduced by the experiment's logic (e.g., new data access patterns within candidate(), different input handling in candidate() vs control()).
     • Ensuring adherence to secure coding practices within the experiment code that scientist orchestrates.
     • Verifying that the experiment logic, when executed by scientist, does not unintentionally expose sensitive information or create new attack vectors due to differences in control and candidate paths.
  4. Document the code review process and ensure it is consistently followed for all experiment-related code changes that utilize scientist.
  5. Use code review tools to facilitate the process and track review status for experiment implementations using scientist.

• Threats Mitigated:

  • Introduction of Vulnerable Experiment Logic in candidate() or control(): Severity: High
  • Accidental Exposure of Sensitive Data due to Experiment Logic Differences: Severity: Medium
  • Logic Errors in Experiments Leading to Security Issues when scientist runs them: Severity: Medium

• Impact:

  • Introduction of Vulnerable Experiment Logic in candidate() or control(): High reduction
  • Accidental Exposure of Sensitive Data due to Experiment Logic Differences: Medium reduction
  • Logic Errors in Experiments Leading to Security Issues when scientist runs them: Medium reduction

• Currently Implemented: Partial - Code reviews are generally implemented for production code in the [Project Name] repository using [Code Review Tool, e.g., GitHub Pull Requests]. However, specific focus on security aspects within the experiment logic orchestrated by scientist during reviews might be inconsistent.

• Missing Implementation: Formalize the code review process specifically for experiment code using scientist, including a checklist or guidelines for reviewers to focus on security aspects relevant to the experiment logic within control() and candidate() methods. Ensure consistent application of security-focused reviews for all experiment implementations using scientist.

Mitigation Strategy: Gradual Experiment Rollout and Canary Deployments for Scientist-Driven Experiments

• Description:

  1. Utilize gradual rollout strategies specifically for experiments implemented using scientist. Start with a small percentage of users or traffic exposed to the candidate() behavior orchestrated by scientist.
  2. Incrementally increase the experiment exposure over time, carefully monitoring for errors, performance issues, and security vulnerabilities as scientist directs more traffic to the candidate() path.
  3. Implement canary deployments for experiments using scientist, allowing you to test the candidate() behavior in a limited production environment before wider rollout and quickly rollback if issues are detected when scientist is actively running the experiment.
  4. Canary deployments involve routing a small subset of production traffic to the experiment version where scientist is actively comparing control() and candidate() while the majority of traffic continues to the control behavior.
  5. Monitor canary deployments closely for any adverse effects arising from the candidate() logic executed by scientist before proceeding with wider rollout.

• Threats Mitigated:

  • Large-Scale Impact of Vulnerabilities or Errors in candidate() Logic when Scientist is Active: Severity: High
  • Denial-of-Service or Performance Degradation due to Issues in candidate() Logic orchestrated by Scientist: Severity: Medium
  • Difficulty in Rolling Back Problematic Experiments Run by Scientist: Severity: Medium

• Impact:

  • Large-Scale Impact of Vulnerabilities or Errors in candidate() Logic when Scientist is Active: High reduction
  • Denial-of-Service or Performance Degradation due to Issues in candidate() Logic orchestrated by Scientist: Medium reduction
  • Difficulty in Rolling Back Problematic Experiments Run by Scientist: Medium reduction

• Currently Implemented: Partial - Feature flags are used for experiment rollout in [Feature Flag System Name, e.g., LaunchDarkly, Feature Flags in-house]. Gradual rollout is generally practiced, but formal canary deployment processes might not be consistently applied for all experiments using scientist.

• Missing Implementation: Formalize canary deployment procedures specifically for experiments implemented with scientist. Integrate canary deployments into the experiment rollout workflow for scientist-driven experiments. Enhance monitoring and alerting during canary deployments to quickly detect and respond to issues arising from the candidate() logic executed by scientist.

Mitigation Strategy: Robust Feature Flag Management and Control for Scientist Experiments

• Description:

  1. Use a robust feature flag management system (e.g., [Feature Flag System Name]) to control the activation and deactivation of experiments implemented using scientist. This includes controlling when scientist is actively running experiments and comparing control() and candidate() behaviors.
  2. Implement granular access controls for feature flag management to restrict who can enable or disable feature flags that control scientist experiments, preventing unauthorized activation or deactivation of experiments.
  3. Enforce multi-factor authentication (MFA) for access to the feature flag management system used to control scientist experiments.
  4. Implement audit logging for all feature flag changes related to scientist experiments, including who made the change, when, and what experiment flags were modified.
  5. Regularly review feature flag configurations related to scientist experiments and remove or archive flags that are no longer needed for active experiments.

• Threats Mitigated:

  • Unauthorized Activation or Deactivation of Scientist Experiments: Severity: Medium
  • Accidental or Malicious Changes to Scientist Experiment Configurations (feature flags): Severity: Medium
  • Lack of Audit Trail for Control Actions on Scientist Experiments: Severity: Low
  • Stale Feature Flags for Scientist Experiments Leading to Confusion or Security Issues: Severity: Low

• Impact:

  • Unauthorized Activation or Deactivation of Scientist Experiments: Medium reduction
  • Accidental or Malicious Changes to Scientist Experiment Configurations (feature flags): Medium reduction
  • Lack of Audit Trail for Control Actions on Scientist Experiments: Low reduction
  • Stale Feature Flags for Scientist Experiments Leading to Confusion or Security Issues: Low reduction

• Currently Implemented: Partial - Feature flags are managed using [Feature Flag System Name]. Access controls are in place, but MFA might not be enforced for all users. Audit logging is likely available in the feature flag system, but its completeness and review frequency for flags controlling scientist experiments might vary.

• Missing Implementation: Enforce MFA for access to the feature flag management system, especially for users managing flags controlling scientist experiments. Regularly review audit logs of feature flag changes related to scientist experiments. Implement a process for regularly reviewing and cleaning up stale feature flags associated with scientist experiments.