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attack-surface.md

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Attack Surface Analysis for encode/django-rest-framework

Attack Surface: Insecure Deserialization

  • Description: Attackers craft malicious input that, when deserialized by the API, executes arbitrary code, modifies data, or causes a denial of service.
  • How DRF Contributes: DRF's serializers handle the conversion of data between Python objects and formats like JSON. If not configured carefully, they can be tricked into instantiating arbitrary objects or executing malicious code during deserialization. This is the core DRF contribution to this vulnerability.
  • Example: An attacker sends a JSON payload containing a specially crafted object that, when deserialized using a vulnerable serializer (especially one using pickle or a custom deserializer with insufficient validation), triggers the execution of a system command.
  • Impact: Remote Code Execution (RCE), Data Corruption, Denial of Service (DoS).
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Strict Input Validation: Implement rigorous validation within serializers using DRF's built-in and custom validators. Validate data types, lengths, formats, and allowed values before deserialization.
    • Avoid pickle: Never use pickle or similar unsafe serialization formats with untrusted data.
    • Safe Deserialization (YAML): If using YAML, use yaml.safe_load().
    • Limit Nested Data: Minimize the complexity of data structures accepted by serializers.
    • Whitelisting Fields: Explicitly define allowed fields using fields or exclude in the serializer's Meta class. Avoid fields = '__all__'.

Attack Surface: Data Exposure via Serializers

  • Description: Sensitive data is unintentionally exposed in API responses due to poorly configured serializers.
  • How DRF Contributes: Serializers are the mechanism in DRF that defines how model data is represented in API responses. This is a direct and fundamental DRF responsibility.
  • Example: A user profile serializer includes the user's password hash or internal database IDs in the API response.
  • Impact: Information Disclosure, Potential for further attacks (e.g., privilege escalation).
  • Risk Severity: High
  • Mitigation Strategies:
    • Explicit Field Control: Use fields or exclude in the serializer's Meta class to precisely control output.
    • Read-Only Fields: Mark sensitive fields as read_only=True.
    • Separate Serializers: Create different serializers for different use cases (create, list, detail).
    • SerializerMethodField Caution: Ensure methods associated with SerializerMethodField don't expose sensitive data.

Attack Surface: Authentication Bypass

  • Description: Attackers gain unauthorized access to API endpoints due to misconfigured or missing authentication.
  • How DRF Contributes: DRF provides the authentication classes (e.g., SessionAuthentication, TokenAuthentication, JWTAuthentication) that are used to secure endpoints. Misconfiguration or omission is a direct DRF-related issue.
  • Example: An API endpoint requiring authentication is accidentally left unprotected, or SessionAuthentication is used without CSRF protection (which, while a Django feature, is directly relevant when using DRF's SessionAuthentication).
  • Impact: Unauthorized Data Access, Data Modification, Account Takeover.
  • Risk Severity: High
  • Mitigation Strategies:
    • Choose Appropriate Authentication: Select authentication classes that fit the security requirements.
    • CSRF Protection: If using SessionAuthentication, ensure Django's CSRF protection is enabled.
    • Secure Token Handling: For TokenAuthentication or JWTAuthentication, use secure token practices (HTTPS, HttpOnly cookies, short-lived tokens, revocation).
    • Explicit DEFAULT_AUTHENTICATION_CLASSES: Set this in settings to avoid unexpected behavior.

Attack Surface: Authorization Bypass (Insufficient Permission Checks)

  • Description: Authenticated users can access or modify data they shouldn't be able to, due to missing or incorrect permission checks.
  • How DRF Contributes: DRF provides the permission classes (e.g., IsAuthenticated, IsAdminUser, DjangoModelPermissions, custom permissions) that are the core mechanism for authorization. Misapplication or omission is a direct DRF issue.
  • Example: A user with "read" permissions can modify data because the view lacks a permission_classes = [IsAdminUser] check.
  • Impact: Unauthorized Data Access/Modification, Privilege Escalation.
  • Risk Severity: High
  • Mitigation Strategies:
    • Consistent Permission Application: Apply appropriate permission classes to all views requiring authorization.
    • DjangoObjectPermissions (with caution): Ensure correct implementation of permission methods in models.
    • Custom Permission Classes: Create custom classes for complex authorization logic.
    • Thorough Testing: Write tests to verify permission checks.
    • Explicit DEFAULT_PERMISSION_CLASSES: Set this in settings.

Attack Surface: Unvalidated Filter Parameters

  • Description: Attackers inject malicious filter queries that cause performance issues, expose data, or lead to SQL injection.
  • How DRF Contributes: DRF's filtering capabilities (e.g., DjangoFilterBackend, SearchFilter, OrderingFilter) are directly provided by the framework. Their misuse is a DRF-specific concern.
  • Example: An attacker uses a filter parameter to inject a SQL query that bypasses authentication or retrieves all user data.
  • Impact: Information Disclosure, Denial of Service, Potential SQL Injection (if raw SQL is used).
  • Risk Severity: High
  • Mitigation Strategies:
    • Whitelist Filter Fields: Define allowed fields using filterset_fields or a custom FilterSet.
    • Validate Filter Values: Implement validation for filter values.
    • Limit Search Fields: Restrict searchable fields using search_fields.
    • Avoid Raw SQL: Rely on DRF's filtering and the Django ORM.

Attack Surface: Vulnerable Dependencies

  • Description: DRF or its dependencies have known security vulnerabilities.
  • How DRF Contributes: DRF, like any software, can have vulnerabilities. It also relies on other packages. This is a direct concern as it's the framework being used.
  • Example: A vulnerability in a DRF serializer library allows for remote code execution.
  • Impact: Varies depending on the vulnerability (RCE, DoS, Information Disclosure, etc.).
  • Risk Severity: Varies (High to Critical)
  • Mitigation Strategies:
    • Regular Updates: Keep DRF and all dependencies up-to-date.
    • Dependency Scanning: Use tools like pip-audit, safety, or Dependabot.
    • Vetting Packages: Evaluate the security of new packages before adding them.