threat-modeling.md

Threat Model Analysis for 99designs/gqlgen

Threat: Schema Complexity Exploitation (DoS)

• Description: An attacker crafts excessively complex GraphQL queries, leveraging deeply nested types and connections defined in the schema. These queries overwhelm the gqlgen query parser and execution engine, consuming excessive server resources (CPU, memory). This leads to service degradation or complete unavailability for legitimate users. This is directly related to how gqlgen parses and executes GraphQL queries based on the schema.
• Impact: Application unavailability, performance degradation, financial loss due to downtime, reputational damage.
• Affected gqlgen component: GraphQL Execution Engine (query parser, executor), Schema Definition.
• Risk Severity: High
• Mitigation Strategies:
  • Implement query complexity analysis middleware in gqlgen to reject overly complex queries.
  • Define maximum query depth and complexity limits.
  • Carefully design the GraphQL schema to avoid unnecessary nesting and complexity.
  • Monitor server resource usage and set up alerts for unusual spikes.
  • Consider rate limiting requests to the GraphQL endpoint.

Threat: GraphQL Injection Vulnerabilities in gqlgen Parser/Executor

• Description: An attacker crafts malicious GraphQL queries designed to exploit potential vulnerabilities within gqlgen's query parser or execution engine. This could involve bypassing validation, triggering unexpected behavior, or potentially achieving code execution if vulnerabilities exist in gqlgen itself. This threat is directly within gqlgen's core components.
• Impact: Potential for arbitrary code execution, data access, or DoS if vulnerabilities exist in gqlgen.
• Affected gqlgen component: GraphQL Execution Engine (query parser, executor).
• Risk Severity: Medium (Low probability, but potentially Critical impact if exploited - considering it High for this list due to potential impact)
• Mitigation Strategies:
  • Keep gqlgen updated to the latest version to benefit from security patches and bug fixes.
  • Monitor security advisories and vulnerability databases related to gqlgen and its dependencies.
  • Report any suspected vulnerabilities in gqlgen to the maintainers.
  • While less direct control, contributing to gqlgen's security through code reviews and vulnerability reporting can indirectly improve application security.

Threat: Context Data Manipulation or Misuse

• Description: An attacker, potentially through compromised middleware interacting with gqlgen's context, or by exploiting vulnerabilities in custom context handling logic within gqlgen's request lifecycle, manipulates or misuses data stored in the gqlgen context. This could involve altering user roles, authentication status, or other critical request-scoped information, leading to authorization bypasses or other security breaches in resolvers. While context is a general concept, gqlgen's reliance and specific implementation of context passing makes this a relevant threat.
• Impact: Authorization bypass, privilege escalation, data corruption, inconsistent application state, potential data breaches.
• Affected gqlgen component: Context Handling (middleware integration with gqlgen, resolver context access), Resolver Implementation.
• Risk Severity: High
• Mitigation Strategies:
  • Treat context data as read-only within resolvers unless explicitly intended for modification in specific, controlled scenarios.
  • Thoroughly review and test middleware that sets context values for security implications, especially how they interact with gqlgen.
  • Enforce clear documentation and consistent practices for context handling across the application, within the gqlgen request lifecycle.
  • Implement input validation and sanitization even for data retrieved from the context if it originates from external sources.

Threat: Vulnerabilities in Generated Code

• Description: An attacker benefits from vulnerabilities inadvertently introduced into the code generated by gqlgen. Flaws in gqlgen's code generation logic could result in insecure resolvers, models, or other application components, creating exploitable weaknesses. This is a direct consequence of using gqlgen's code generation feature.
• Impact: Vulnerabilities in generated code could lead to various security issues, including injection vulnerabilities, logic errors, or insecure defaults.
• Affected gqlgen component: Code Generation, Generated Code (resolvers, models, etc.).
• Risk Severity: Medium (considering it High for this list due to potential impact and direct relation to gqlgen's functionality)
• Mitigation Strategies:
  • Keep gqlgen updated to the latest version to benefit from bug fixes and improvements in code generation.
  • Review the generated code, especially resolvers, to ensure it is secure and follows best practices.
  • Report any suspected code generation flaws to the gqlgen maintainers.

Threat: Configuration Errors during Code Generation (Insecure Defaults)

• Description: Developers misconfigure gqlgen during code generation, particularly in gqlgen.yml. This leads to the generation of code with insecure defaults or behaviors, such as misconfigured resolvers or incorrect type mappings, which can introduce vulnerabilities. This is directly related to gqlgen's configuration and code generation process.
• Impact: Insecure defaults in generated code, potentially leading to authorization bypasses, data exposure, or other vulnerabilities.
• Affected gqlgen component: Code Generation Configuration (gqlgen.yml), Generated Code.
• Risk Severity: Medium (considering it High for this list due to potential impact and direct relation to gqlgen's configuration)
• Mitigation Strategies:
  • Carefully review and understand all configuration options in gqlgen.yml.
  • Follow security best practices when configuring gqlgen, avoiding insecure defaults.
  • Use code review and testing to verify that the generated code behaves as expected and is secure based on the configuration.
  • Use linters and static analysis tools to detect potential configuration issues.