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Attack Tree Analysis for 99designs/gqlgen

Objective: Compromise Application using gqlgen vulnerabilities.

Attack Tree Visualization

Compromise Application (Root Goal) [CRITICAL NODE] - High Impact if achieved ├───(OR)─ Exploit GraphQL Specific Vulnerabilities │ └───(OR)─ Query Complexity Attack [HIGH RISK PATH] - DoS is a significant impact, relatively easy to execute │ ├───(AND)─ Deeply Nested Queries [CRITICAL NODE] - Common and effective DoS technique │ ├───(AND)─ Wide Queries (fetching many fields) [CRITICAL NODE] - Another common DoS technique │ └───(AND)─ Aliasing to increase processing [CRITICAL NODE] - More sophisticated DoS, but still effective ├───(OR)─ Exploit Resolver Logic Vulnerabilities (Application Specific, but relevant to gqlgen context) [HIGH RISK PATH] - Direct impact on application logic and data │ └───(OR)─ Business Logic Flaws in Resolvers [HIGH RISK PATH] - Common and high impact │ ├───(AND)─ Analyze Resolver Code for Logic Errors [CRITICAL NODE] - Code review and testing are essential │ ├───(AND)─ Authentication/Authorization Bypass in Resolvers [CRITICAL NODE] - High impact, common vulnerability │ ├───(AND)─ Insecure Data Access in Resolvers [CRITICAL NODE] - High impact, common vulnerability │ └───(AND)─ Input Validation Issues in Resolvers [CRITICAL NODE] - High impact, common vulnerability └───(OR)─ Schema Design Vulnerabilities (GraphQL Specific) [HIGH RISK PATH] - Fundamental to GraphQL security └───(OR)─ Insecure Schema Definition [HIGH RISK PATH] - Direct impact on data exposure ├───(AND)─ Identify Overly Permissive or Sensitive Data Exposure [CRITICAL NODE] - Schema review is crucial ├───(AND)─ Exposing sensitive fields without proper authorization [CRITICAL NODE] - Common schema design flaw └───(AND)─ Allowing access to data that should be restricted [CRITICAL NODE] - Common schema design flaw

Attack Tree Path: High-Risk Path: Query Complexity Attack

Attack Vector Name: GraphQL Query Complexity Exploitation

  • Likelihood: Medium
  • Impact: Medium to High (Denial of Service, Server Overload)
  • Effort: Low to Medium
  • Skill Level: Medium
  • Detection Difficulty: Medium
  • Description: Attackers craft excessively complex GraphQL queries that consume significant server resources during processing. This can lead to server overload, slow response times for legitimate users, and potentially complete denial of service. The complexity can be achieved through:
    • Deeply Nested Queries: Queries that traverse relationships to many levels, requiring the server to fetch and process data across multiple layers.
    • Wide Queries (Fetching Many Fields): Queries that request a large number of fields for each node, increasing the data retrieval and processing load.
    • Aliasing to Increase Processing: Using aliases to request the same field multiple times within a single query, effectively multiplying the processing effort for the server.
  • Mitigation Strategies:
    • Implement Query Complexity Analysis and Limits: Use libraries or custom logic to calculate the complexity of incoming queries based on factors like depth, breadth, and field selections.
    • Enforce Query Depth Limiting: Restrict the maximum nesting level allowed in queries.
    • Implement Timeout Mechanisms: Set timeouts for query execution to prevent long-running queries from monopolizing resources.
    • Apply Rate Limiting: Limit the number of requests from a single IP address or user within a specific time frame.

Attack Tree Path: Critical Node: Deeply Nested Queries (within Query Complexity Attack)

  • Attack Vector Name: Deeply Nested GraphQL Queries
  • Likelihood: Medium
  • Impact: Medium to High (Denial of Service, Server Overload)
  • Effort: Low to Medium
  • Skill Level: Medium
  • Detection Difficulty: Medium
  • Description: Attackers construct GraphQL queries with excessive nesting levels. For example, a query might repeatedly traverse relationships like user { posts { comments { author { ... } } } } to an extreme depth. This forces the server to perform numerous database lookups and data processing operations, leading to resource exhaustion.
  • Mitigation Strategies:
    • Implement Query Depth Limiting: Specifically limit the maximum depth of nested queries allowed by the GraphQL server.
    • Query Complexity Analysis: Factor query depth into the overall complexity score and enforce limits.
    • Monitoring and Alerting: Monitor query depth and alert on queries exceeding predefined thresholds.

Attack Tree Path: Critical Node: Wide Queries (fetching many fields) (within Query Complexity Attack)

  • Attack Vector Name: Wide GraphQL Queries (Excessive Field Selection)
  • Likelihood: Medium
  • Impact: Medium to High (Denial of Service, Server Overload)
  • Effort: Low to Medium
  • Skill Level: Medium
  • Detection Difficulty: Medium
  • Description: Attackers craft GraphQL queries that request a very large number of fields for each type in the query. For instance, a query might select almost every available field for a User or Product type. This increases the amount of data the server needs to retrieve, serialize, and transmit, leading to increased resource consumption and potential DoS.
  • Mitigation Strategies:
    • Query Complexity Analysis: Factor the number of selected fields into the complexity score and enforce limits.
    • Field Limiting (Less Common, More Restrictive): In extreme cases, consider limiting the maximum number of fields that can be selected in a single query (this can impact legitimate use cases).
    • Monitoring and Alerting: Monitor the number of fields requested in queries and alert on unusually wide queries.

Attack Tree Path: Critical Node: Aliasing to increase processing (within Query Complexity Attack)

  • Attack Vector Name: GraphQL Query Aliasing for Complexity Amplification
  • Likelihood: Low to Medium
  • Impact: Medium to High (Denial of Service, Server Overload)
  • Effort: Medium
  • Skill Level: Medium
  • Detection Difficulty: Medium
  • Description: Attackers use GraphQL aliases to request the same computationally expensive field or resolver multiple times within a single query. For example, a query might use aliases to request a complex calculation or data aggregation operation repeatedly. This multiplies the server-side processing required for a seemingly simple query, leading to resource exhaustion and DoS.
  • Mitigation Strategies:
    • Query Complexity Analysis: Ensure complexity analysis accounts for aliasing and correctly calculates the increased processing load from repeated field requests, even with aliases.
    • Limit Aliases (Less Common, More Restrictive): Consider limiting the number of aliases allowed in a single query (this can impact legitimate use cases).
    • Monitoring and Alerting: Monitor the use of aliases in queries and alert on queries with excessive alias usage, especially for computationally intensive fields.

Attack Tree Path: High-Risk Path: Exploit Resolver Logic Vulnerabilities

  • Attack Vector Name: GraphQL Resolver Logic Exploitation
  • Likelihood: Medium
  • Impact: Medium to High (Unauthorized Access, Data Breach, Data Manipulation, DoS)
  • Effort: Medium
  • Skill Level: Medium
  • Detection Difficulty: Medium
  • Description: Resolvers are the functions that implement the business logic behind GraphQL fields. Vulnerabilities in resolver code are a primary attack vector. This includes:
    • Business Logic Flaws in Resolvers: Errors or oversights in the resolver's logic that can be exploited to bypass intended functionality or gain unauthorized access.
    • Authentication/Authorization Bypass in Resolvers: Lack of proper authentication or authorization checks within resolvers, allowing unauthorized users to access or modify data.
    • Insecure Data Access in Resolvers: Resolvers that directly access databases or other data sources without proper security measures, leading to potential data breaches or manipulation.
    • Input Validation Issues in Resolvers: Resolvers that do not properly validate input data, making them vulnerable to injection attacks (e.g., SQL injection, NoSQL injection) or other input-based vulnerabilities.
  • Mitigation Strategies:
    • Secure Resolver Implementation: Follow secure coding practices when writing resolvers.
    • Robust Authentication and Authorization: Implement strong authentication and authorization mechanisms and enforce them within resolvers.
    • Input Validation in Resolvers: Thoroughly validate all input data received by resolvers.
    • Secure Data Access Practices: Use secure data access methods (e.g., parameterized queries, ORMs with built-in security features) in resolvers.
    • Code Reviews: Conduct thorough code reviews of resolver logic to identify potential vulnerabilities.
    • Security Testing: Perform security testing, including penetration testing and vulnerability scanning, focusing on GraphQL endpoints and resolvers.

Attack Tree Path: Critical Node: Analyze Resolver Code for Logic Errors (within Resolver Logic Vulnerabilities)

  • Attack Vector Name: Business Logic Vulnerabilities in GraphQL Resolvers
  • Likelihood: Medium
  • Impact: Low to High (Varies greatly depending on the flaw - can range from minor information disclosure to critical data manipulation or unauthorized actions)
  • Effort: Medium to High (Requires code review, reverse engineering, dynamic testing)
  • Skill Level: Medium to High (Code analysis, security expertise)
  • Detection Difficulty: Medium to High (Code review needed, dynamic analysis harder)
  • Description: Resolvers may contain flaws in their business logic that are not immediately obvious. These flaws can be exploited by attackers to achieve unintended outcomes, such as bypassing access controls, manipulating data in unexpected ways, or triggering errors that reveal sensitive information.
  • Mitigation Strategies:
    • Thorough Code Reviews: Conduct detailed code reviews of all resolver logic, focusing on business logic correctness and security implications.
    • Unit and Integration Testing: Implement comprehensive unit and integration tests for resolvers, including test cases that specifically target edge cases and potential logic flaws.
    • Security-Focused Design: Design resolvers with security in mind, considering potential attack vectors and implementing defensive programming techniques.

Attack Tree Path: Critical Node: Authentication/Authorization Bypass in Resolvers (within Resolver Logic Vulnerabilities)

  • Attack Vector Name: GraphQL Resolver Authentication and Authorization Bypass
  • Likelihood: Medium
  • Impact: High (Unauthorized Access to data and functionality)
  • Effort: Medium
  • Skill Level: Medium
  • Detection Difficulty: Medium
  • Description: Resolvers may fail to properly authenticate users or enforce authorization rules. This can allow attackers to bypass authentication mechanisms or access resources they are not authorized to view or modify. This is a common web application vulnerability that applies directly to GraphQL resolvers.
  • Mitigation Strategies:
    • Implement Robust Authentication: Use established authentication mechanisms (e.g., JWT, OAuth) and ensure they are correctly integrated with the GraphQL application.
    • Enforce Authorization in Resolvers: Implement authorization checks within resolvers to verify that the current user has the necessary permissions to access the requested data or perform the requested action.
    • Centralized Authorization Logic: Consider centralizing authorization logic (e.g., using policy-based authorization frameworks) to ensure consistency and reduce the risk of errors in individual resolvers.
    • Testing and Auditing: Thoroughly test authentication and authorization mechanisms and regularly audit resolver code for potential bypass vulnerabilities.

Attack Tree Path: Critical Node: Insecure Data Access in Resolvers (within Resolver Logic Vulnerabilities)

  • Attack Vector Name: Insecure Data Access in GraphQL Resolvers
  • Likelihood: Medium
  • Impact: High (Data Breach, Data Manipulation, Data Integrity Issues)
  • Effort: Medium
  • Skill Level: Medium
  • Detection Difficulty: Medium
  • Description: Resolvers may access databases or other data sources in an insecure manner. This can include:
    • Directly embedding user input into database queries (leading to injection vulnerabilities).
    • Using overly permissive database access credentials.
    • Failing to properly sanitize or validate data retrieved from data sources.
    • Exposing sensitive data in error messages or logs.
  • Mitigation Strategies:
    • Parameterized Queries or ORMs: Use parameterized queries or Object-Relational Mappers (ORMs) to prevent injection vulnerabilities when interacting with databases.
    • Principle of Least Privilege for Data Access: Grant resolvers only the necessary database permissions required for their functionality.
    • Data Sanitization and Validation: Sanitize and validate data retrieved from data sources before using it in resolvers or returning it to clients.
    • Secure Error Handling and Logging: Avoid exposing sensitive data in error messages or logs. Implement secure logging practices.

Attack Tree Path: Critical Node: Input Validation Issues in Resolvers (within Resolver Logic Vulnerabilities)

  • Attack Vector Name: Input Validation Vulnerabilities in GraphQL Resolvers
  • Likelihood: Medium to High
  • Impact: Medium to High (Data Integrity Issues, DoS, Exploitation of Backend Systems, Potential for Injection Attacks)
  • Effort: Low to Medium
  • Skill Level: Medium
  • Detection Difficulty: Medium
  • Description: Resolvers may fail to properly validate input data received from GraphQL queries or mutations. This can lead to various vulnerabilities, including:
    • Data Integrity Issues: Invalid or malicious input can corrupt data in the application's backend.
    • Denial of Service (DoS): Malicious input can cause resolvers to crash or consume excessive resources.
    • Exploitation of Backend Systems: Invalid input can be used to trigger vulnerabilities in backend systems or databases.
    • Injection Attacks: Lack of input validation can make resolvers vulnerable to injection attacks (e.g., SQL injection, NoSQL injection, Command Injection).
  • Mitigation Strategies:
    • Input Validation in Resolvers: Implement robust input validation logic within resolvers to check for expected data types, formats, ranges, and other constraints.
    • Schema-Based Validation (Basic): While gqlgen schema provides basic type validation, it's often insufficient for complex validation rules. Use schema validation as a first line of defense but rely on resolver validation for more comprehensive checks.
    • Consider Custom Scalar Types with Validation: For complex input types, consider using custom scalar types with built-in validation logic to enforce data integrity at the schema level.
    • Document Input Validation Requirements: Clearly document input validation requirements for developers to ensure consistent validation practices across resolvers.

Attack Tree Path: High-Risk Path: Schema Design Vulnerabilities

  • Attack Vector Name: Insecure GraphQL Schema Design
  • Likelihood: Medium
  • Impact: Medium to High (Information Disclosure, Unauthorized Access)
  • Effort: Medium
  • Skill Level: Medium
  • Detection Difficulty: Medium
  • Description: A poorly designed GraphQL schema can expose sensitive data or create unintended access paths. This includes:
    • Insecure Schema Definition: The schema itself is designed in a way that is inherently insecure, such as exposing sensitive fields without proper authorization or allowing access to data that should be restricted.
    • Overly Permissive or Sensitive Data Exposure: The schema exposes more data than necessary, including sensitive fields or relationships that should be restricted to authorized users or roles.
    • Exposing sensitive fields without proper authorization: Sensitive fields (e.g., email addresses, personal information, internal IDs) are included in the schema without adequate authorization controls, making them accessible to unauthorized users.
    • Allowing access to data that should be restricted: The schema allows access to entire data sets or types that should be restricted based on user roles or permissions.
  • Mitigation Strategies:
    • Principle of Least Privilege in Schema Design: Design the schema to expose only the data that is absolutely necessary for the application's functionality.
    • Careful Consideration of Data Exposure: Thoroughly review the schema to identify and minimize the exposure of sensitive data.
    • Authorization at Schema Level: Implement authorization mechanisms at the schema level (e.g., using directives or custom logic) to control access to sensitive fields and types based on user roles or permissions.
    • Schema Reviews: Conduct regular security reviews of the GraphQL schema to identify and address potential design vulnerabilities.

Attack Tree Path: Critical Node: Identify Overly Permissive or Sensitive Data Exposure (within Schema Design Vulnerabilities)

  • Attack Vector Name: Overly Permissive GraphQL Schema - Sensitive Data Exposure
  • Likelihood: Medium
  • Impact: Medium to High (Information Disclosure, Potential for further attacks based on exposed data)
  • Effort: Medium (Schema analysis)
  • Skill Level: Medium (Schema design understanding)
  • Detection Difficulty: Medium (Requires schema review)
  • Description: The GraphQL schema may inadvertently expose sensitive data fields or relationships that should be protected. This can occur due to:
    • Lack of awareness of sensitive data within the schema.
    • Overly broad schema design that includes unnecessary fields.
    • Failure to apply proper authorization controls to sensitive fields.
  • Mitigation Strategies:
    • Schema Review and Auditing: Conduct thorough reviews and audits of the GraphQL schema to identify and classify sensitive data fields and relationships.
    • Data Classification: Implement a data classification system to categorize data based on sensitivity levels.
    • Principle of Least Privilege: Design the schema to expose only the necessary data, minimizing the exposure of sensitive information.
    • Authorization Controls: Implement robust authorization controls to restrict access to sensitive fields and types based on user roles and permissions.

Attack Tree Path: Critical Node: Exposing sensitive fields without proper authorization (within Schema Design Vulnerabilities)

  • Attack Vector Name: Unprotected Sensitive Fields in GraphQL Schema
  • Likelihood: Medium
  • Impact: High (Information Disclosure of sensitive data)
  • Effort: Medium
  • Skill Level: Medium
  • Detection Difficulty: Medium
  • Description: Sensitive data fields (e.g., personal information, financial details, internal system identifiers) are included in the GraphQL schema but are not protected by adequate authorization mechanisms. This allows unauthorized users to query and retrieve sensitive information.
  • Mitigation Strategies:
    • Authorization Directives: Use GraphQL directives (if supported by gqlgen or custom directives) to enforce authorization rules directly within the schema definition for sensitive fields.
    • Resolver-Based Authorization: Implement authorization checks within resolvers for sensitive fields to ensure that only authorized users can access them.
    • Field-Level Access Control: Implement fine-grained access control mechanisms to manage access to individual fields based on user roles and permissions.
    • Schema Documentation and Review: Clearly document sensitive fields in the schema and conduct regular reviews to ensure proper authorization is in place.

Attack Tree Path: Critical Node: Allowing access to data that should be restricted (within Schema Design Vulnerabilities)

  • Attack Vector Name: Overly Permissive Access to Restricted Data in GraphQL Schema
  • Likelihood: Medium
  • Impact: High (Unauthorized Access to restricted data and functionality)
  • Effort: Medium
  • Skill Level: Medium
  • Detection Difficulty: Medium
  • Description: The GraphQL schema may grant access to entire data types or functionalities that should be restricted to specific user roles or permissions. This can occur if authorization is not properly implemented at the schema level or in resolvers, leading to unauthorized access to sensitive resources.
  • Mitigation Strategies:
    • Type-Level Authorization: Implement authorization controls at the GraphQL type level to restrict access to entire types based on user roles or permissions.
    • Relationship-Based Authorization: Enforce authorization rules based on relationships between types and users to control access to related data.
    • Schema Design for Access Control: Design the schema with access control in mind, carefully considering which types and fields should be accessible to different user groups.
    • Testing and Validation: Thoroughly test and validate authorization rules to ensure that access to restricted data is properly controlled.