threat-modeling.md

Threat Model Analysis for fzaninotto/faker

Threat: Production Data Contamination

• Description: An attacker, through social engineering or exploiting a misconfiguration, convinces a developer to run a script containing faker calls against the production database. Alternatively, a developer accidentally executes a test script intended for the development environment against the production database. This could overwrite real user data with fake data.
• Impact: Data loss, data corruption, service disruption, reputational damage, potential legal and financial consequences. Real user accounts could be deleted or modified, leading to loss of access and functionality.
• Affected Faker Component: All data generation functions (e.g., name(), address(), email(), text(), etc.) across all providers. The core issue is misuse of the library, not a specific component vulnerability.
• Risk Severity: Critical
• Mitigation Strategies:
  • Strict Environment Separation: Implement robust separation between development, testing, and production environments. Use different database credentials, servers, and network access controls.
  • Database Access Controls: Restrict database access for development and testing accounts. Production database credentials should never be accessible from development environments. Use the principle of least privilege.
  • Code Reviews: Mandatory code reviews for any script that interacts with databases, with a specific focus on identifying and preventing the use of faker in production contexts.
  • Automated Build Processes: Use automated build and deployment pipelines that exclude faker from production builds. This prevents accidental inclusion of the library.
  • Developer Training: Educate developers on the risks of using faker and the importance of environment separation.
  • "Dry Run" Mode: If possible, implement a "dry run" mode for database scripts, where changes are simulated but not actually applied.
  • Database Backups: Maintain regular, tested backups of the production database to allow for recovery in case of accidental data modification.

Threat: Predictable Data Exploitation (High Severity Case)

• Description: An attacker observes that faker is used with a default or easily guessable seed and that the generated data is used for security-sensitive purposes (e.g., generating temporary passwords, session tokens, or cryptographic keys – misuse of faker). They use this knowledge to predict the generated values and bypass security controls.
• Impact: Bypass of security controls (e.g., predicting session IDs or one-time passwords), leading to unauthorized access or account takeover.
• Affected Faker Component: The seeding mechanism (Faker\Factory::create() and the seed() method). Any provider that relies on the random number generator is affected.
• Risk Severity: High
• Mitigation Strategies:
  • Never Use Faker for Security-Sensitive Data: faker is not designed for generating cryptographically secure values. Use dedicated cryptographic libraries for generating passwords, tokens, keys, etc.
  • Cryptographically Secure Seeding (Even for Non-Critical Uses): Even if the data isn't directly security-sensitive, use a cryptographically secure random number generator to seed faker to avoid any potential predictability issues.
  • Application-Level Uniqueness: Enforce uniqueness at the application level.