mitigations.md

Mitigation Strategies Analysis for faker-ruby/faker

Mitigation Strategy: Seed Management

Description:

1. Random Seeds for General Testing: For most test scenarios, explicitly initialize faker with a new, cryptographically secure random seed for each test run. Use Random.new_seed to generate the seed and then initialize Faker::Config.random with a Random object using that seed.

   Faker::Config.random = Random.new(Random.new_seed)

2. Explicit Seeds for Reproducibility: When reproducibility is required, use an explicit seed, but never hardcode it. Retrieve the seed from an environment variable or a secure configuration store.

   if ENV['FAKER_SEED']
     Faker::Config.random = Random.new(ENV['FAKER_SEED'].to_i)
   else
     Faker::Config.random = Random.new(Random.new_seed) # Fallback to random
   end

3. Seed Rotation: Even for explicit seeds used for reproducibility, implement a policy to periodically change these seeds. The frequency depends on the sensitivity of the data and risk assessment.
4. Avoid Default Seed: Explicitly avoid relying on the default faker seed. Always set a seed, either random or a controlled explicit one.

• Threats Mitigated:

  • Predictable Data (Severity: Medium): Directly addresses the predictability of faker's output when using the default or a fixed seed.
  • Test Flakiness (Severity: Low): Ensures test independence by avoiding reliance on a specific, uncontrolled faker data sequence.
  • Replay Attacks (Severity: Low - specific scenarios): Mitigates replay attacks if faker data is used in contexts like nonce generation.

• Impact:

  • Predictable Data: Risk significantly reduced by controlling the seed.
  • Test Flakiness: Risk significantly reduced.
  • Replay Attacks: Risk mitigated in relevant scenarios.

• Currently Implemented:

  • Random seeds are used by default in all test suites (via a global before(:all) hook in RSpec).
  • Environment variable FAKER_SEED is used for setting explicit seeds.

• Missing Implementation:

  • Formal seed rotation policy is not defined.
  • Documentation of explicit seeds and their purposes needs improvement.

Mitigation Strategy: Controlled Data Generation

Description:

1. Minimal Data: Generate only the minimum amount of data needed. Avoid unnecessary calls to faker methods.
2. Specific Generators: Always use the most specific faker generator available for the data type you need. Favor:
   • Faker::Lorem.sentence over Faker::Lorem.paragraph
   • Faker::Internet.email over Faker::Lorem.word for email fields
   • Faker::Address.zip_code over Faker::Number.number(digits: 5)
   • ...and so on. Consult the faker documentation for the most appropriate generator.
3. Custom Generators: If faker does not provide a generator that meets your specific requirements (e.g., a particular data format, a constrained range of values, or a specific distribution), create a custom generator. This gives you the most precise control over the generated data.

   module CustomFaker
     def self.product_code
       "PRD-" + Faker::Number.number(digits: 6).to_s
     end
   end

• Threats Mitigated:

  • Performance Issues (Severity: Medium): Reduces the overhead of generating unnecessary data.
  • Resource Exhaustion (Severity: Medium): Minimizes the risk of faker consuming excessive resources.
  • Data Inconsistency (Severity: Low): Using specific and custom generators improves data consistency and validity.
  • Denial of Service (DoS) (Severity: Medium - in testing environments): Limits the potential for faker to be used to cause a DoS in the testing environment.

• Impact:

  • Performance Issues: Risk significantly reduced.
  • Resource Exhaustion: Risk significantly reduced.
  • Data Inconsistency: Risk reduced.
  • Denial of Service: Risk mitigated in testing environments.

• Currently Implemented:

  • Developers are encouraged to use specific generators.
  • Some custom generators exist.

• Missing Implementation:

  • Formal guidelines for data generation limits are not documented.
  • More comprehensive use of custom generators could be beneficial.

Mitigation Strategy: Locale Awareness

Description:

1. Explicit Locale: Always explicitly set the desired locale using Faker::Config.locale. Do not rely on the default locale.

   Faker::Config.locale = 'en-US' # Or any other required locale

2. Consistent Locale: Use a consistent locale throughout your testing environment to avoid unexpected variations in data.
3. Multi-Locale Testing (if applicable): If your application supports multiple locales, include tests that specifically set different faker locales to ensure correct handling of internationalized data.

   it 'handles different locales' do
     ['en-US', 'fr-FR', 'ja-JP'].each do |locale|
       Faker::Config.locale = locale
       # ... test logic ...
     end
   end

• Threats Mitigated:

  • Data Format Errors (Severity: Low): Prevents issues caused by unexpected data formats (dates, numbers, etc.) due to incorrect locale settings.
  • Character Encoding Issues (Severity: Medium): Reduces the risk of character encoding problems.
  • Localization Bugs (Severity: Low): Helps identify localization bugs early.

• Impact:

  • Data Format Errors: Risk significantly reduced.
  • Character Encoding Issues: Risk reduced.
  • Localization Bugs: Risk reduced.

• Currently Implemented:

  • Faker::Config.locale is set to 'en-US' in the test environment setup.

• Missing Implementation:

  • Multi-locale testing is not currently implemented.
  • Documentation on locale usage could be improved.