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Attack Tree Analysis for 99designs/gqlgen

Objective: Compromise gqlgen Application

Attack Tree Visualization

Attack Goal: Compromise gqlgen Application [CRITICAL NODE] ├── OR 1: Exploit GraphQL Specific Vulnerabilities [HIGH RISK PATH - Introspection Abuse & Query Complexity] │ ├── AND 1.1: Schema Introspection Abuse [CRITICAL NODE] │ │ ├── 1.1.1: Discover Internal APIs and Data Structures [HIGH RISK PATH - Information Disclosure] │ ├── AND 1.2: Query Complexity Attacks [CRITICAL NODE] [HIGH RISK PATH - DoS] │ │ ├── 1.2.1: Denial of Service (DoS) via Resource Exhaustion [HIGH RISK PATH - DoS via Nested/Wide Queries] │ │ │ ├── 1.2.1.1: Send Deeply Nested Queries [CRITICAL NODE - Nested Queries for DoS] │ │ │ ├── 1.2.1.2: Send Wide Queries with Many Fields [CRITICAL NODE - Wide Queries for DoS] ├── OR 2: Exploit gqlgen Implementation/Usage Vulnerabilities │ ├── AND 2.3: Dependency Vulnerabilities [HIGH RISK PATH - Dependency Vulnerabilities] │ │ ├── 2.3.1: Vulnerabilities in Go Dependencies used by gqlgen or the Application [CRITICAL NODE - Dependency Vulnerabilities] │ ├── AND 2.4: Error Handling and Information Disclosure [HIGH RISK PATH - Error Disclosure] │ │ ├── 2.4.1: Verbose Error Messages Exposing Internal Details [CRITICAL NODE - Verbose Errors] ├── OR 3: Exploit Application Logic in Resolvers [HIGH RISK PATH - Resolver Logic Exploits] │ ├── AND 3.1: Resolver Input Validation Failures [CRITICAL NODE - Input Validation Failures] [HIGH RISK PATH - Injection & Business Logic Bypass] │ │ ├── 3.1.1: Injection Vulnerabilities (SQL, NoSQL, Command Injection, etc.) [CRITICAL NODE - Injection Vulnerabilities] [HIGH RISK PATH - Injection] │ │ ├── 3.1.2: Business Logic Bypass due to Input Manipulation [CRITICAL NODE - Business Logic Bypass] │ ├── AND 3.2: Authorization and Authentication Flaws [CRITICAL NODE - Authorization Flaws] [HIGH RISK PATH - Authorization Bypass] │ │ ├── 3.2.1: Missing Authorization Checks [CRITICAL NODE - Missing Authorization] [HIGH RISK PATH - Missing Authorization Checks] │ ├── AND 3.3: Data Leaks and Sensitive Data Exposure in Resolvers [CRITICAL NODE - Data Leakage] [HIGH RISK PATH - Data Exposure] │ │ ├── 3.3.1: Unintentional Exposure of Sensitive Fields [CRITICAL NODE - Sensitive Field Exposure] [HIGH RISK PATH - Sensitive Field Exposure]

Attack Tree Path: 1. Attack Goal: Compromise gqlgen Application [CRITICAL NODE]

  • Attack Vector: Successful exploitation of any vulnerability within the gqlgen application leading to a compromise.
  • Description: This is the ultimate objective of the attacker. Success means gaining unauthorized access, control, or causing significant damage to the application and potentially its underlying systems and data.
  • Potential Impact: Critical. Full system compromise, massive data breach, reputational damage, financial loss, disruption of critical services.
  • Mitigation Strategies: Implement all mitigations listed in the full attack tree, prioritize high-risk paths and critical nodes. Employ a defense-in-depth strategy.

Attack Tree Path: 2. OR 1: Exploit GraphQL Specific Vulnerabilities [HIGH RISK PATH - Introspection Abuse & Query Complexity]

  • Attack Vector: Targeting vulnerabilities inherent to GraphQL or amplified by its implementation with gqlgen.
  • Description: Attackers focus on GraphQL-specific features and weaknesses, such as schema introspection and query processing, to gain information or disrupt service.
  • Potential Impact: Medium to High. Information disclosure, Denial of Service, potential for further exploitation based on discovered information.
  • Mitigation Strategies: Disable introspection in production, implement query complexity and depth limiting, secure resolver logic, and regularly review GraphQL security best practices.

Attack Tree Path: 3. AND 1.1: Schema Introspection Abuse [CRITICAL NODE]

  • Attack Vector: Exploiting the GraphQL introspection feature to gather information about the application's schema.
  • Description: Attackers use standard GraphQL tools or queries to access the /graphql endpoint and retrieve the schema. This reveals all available queries, mutations, types, and fields, essentially mapping the entire API structure.
  • Potential Impact: Medium. Information Disclosure. Exposes internal APIs, data structures, and potential entry points for further attacks.
  • Mitigation Strategies: Disable introspection in production environments via gqlgen configuration.

Attack Tree Path: 4. 1.1.1: Discover Internal APIs and Data Structures [HIGH RISK PATH - Information Disclosure]

  • Attack Vector: Utilizing schema introspection to specifically uncover hidden or internal APIs and data structures not intended for public access.
  • Description: By analyzing the introspected schema, attackers can identify resolvers, fields, and types that might represent internal functionalities or sensitive data, even if not explicitly documented or intended for external use.
  • Potential Impact: Medium. Information Disclosure. Reveals sensitive internal details, logic, and data structures, aiding in targeted attacks.
  • Mitigation Strategies: Disable introspection in production, carefully design schema to avoid exposing internal details, implement field-level authorization even for schema elements intended to be public.

Attack Tree Path: 5. AND 1.2: Query Complexity Attacks [CRITICAL NODE] [HIGH RISK PATH - DoS]

  • Attack Vector: Overwhelming the server with computationally expensive GraphQL queries.
  • Description: Attackers craft complex queries designed to consume excessive server resources (CPU, memory, database connections) leading to performance degradation or complete service outage.
  • Potential Impact: High. Denial of Service. Application downtime, service disruption, resource exhaustion.
  • Mitigation Strategies: Implement query complexity analysis and limiting (using gqlgen extensions or custom logic), set query depth limits (gqlgen configuration), optimize resolver performance and database queries, use caching mechanisms.

Attack Tree Path: 6. 1.2.1: Denial of Service (DoS) via Resource Exhaustion [HIGH RISK PATH - DoS via Nested/Wide Queries]

  • Attack Vector: Achieving DoS specifically by sending deeply nested or wide GraphQL queries.
  • Description:
    • 1.2.1.1: Send Deeply Nested Queries [CRITICAL NODE - Nested Queries for DoS]: Queries with excessive nesting of fields, forcing the server to traverse deep object graphs, consuming CPU and memory.
    • 1.2.1.2: Send Wide Queries with Many Fields [CRITICAL NODE - Wide Queries for DoS]: Queries requesting a large number of fields, especially from resource-intensive resolvers or database queries, overloading the server and database.
  • Potential Impact: High. Denial of Service. Server overload, application downtime, database strain, resource exhaustion.
  • Mitigation Strategies: Query complexity limiting, depth limiting, field limiting, efficient data fetching in resolvers, rate limiting requests.

Attack Tree Path: 7. OR 2: Exploit gqlgen Implementation/Usage Vulnerabilities

  • Attack Vector: Targeting vulnerabilities arising from the way gqlgen is implemented or how developers use it.
  • Description: This path encompasses issues related to gqlgen's code generation, configuration mistakes, dependency vulnerabilities, and error handling implementation.
  • Potential Impact: Medium to Critical. Information disclosure, potential code execution, service disruption, data manipulation, depending on the specific vulnerability.
  • Mitigation Strategies: Regularly update gqlgen and dependencies, secure gqlgen configuration, implement robust error handling, review generated code, and follow secure coding practices when using gqlgen.

Attack Tree Path: 8. AND 2.3: Dependency Vulnerabilities [HIGH RISK PATH - Dependency Vulnerabilities]

  • Attack Vector: Exploiting known vulnerabilities in Go dependencies used by gqlgen or the application itself.
  • Description: Attackers leverage publicly disclosed vulnerabilities (CVEs) in libraries that gqlgen or the application relies on. This can lead to various impacts depending on the vulnerability.
  • Potential Impact: Medium to Critical. Standard dependency vulnerability impacts - code execution, data breach, DoS, depending on the CVE.
  • Mitigation Strategies: Regularly update Go dependencies, use dependency scanning tools (e.g., govulncheck, dep-scan) to identify and remediate vulnerable dependencies.

Attack Tree Path: 9. 2.3.1: Vulnerabilities in Go Dependencies used by gqlgen or the Application [CRITICAL NODE - Dependency Vulnerabilities]

  • Attack Vector: Specific vulnerabilities residing within the Go dependencies.
  • Description: This node represents the actual presence of vulnerable dependencies. Attackers exploit these vulnerabilities using known techniques and exploits.
  • Potential Impact: Medium to Critical. Impact directly tied to the specific CVE of the vulnerable dependency.
  • Mitigation Strategies: Dependency updates, vulnerability scanning, dependency pinning, using minimal and well-maintained dependencies.

Attack Tree Path: 10. AND 2.4: Error Handling and Information Disclosure [HIGH RISK PATH - Error Disclosure]

  • Attack Vector: Gaining sensitive information through verbose or improperly handled error messages.
  • Description: Attackers trigger errors in the application (e.g., by providing invalid input) and analyze the error responses. If error handling is not properly configured, these responses might reveal internal details.
  • Potential Impact: Medium. Information Disclosure. Exposes internal paths, database details, code structure, and potentially sensitive data in error messages.
  • Mitigation Strategies: Customize error handling in gqlgen to return generic error messages to clients in production, log detailed errors securely on the server-side, avoid exposing sensitive information in error responses.

Attack Tree Path: 11. 2.4.1: Verbose Error Messages Exposing Internal Details [CRITICAL NODE - Verbose Errors]

  • Attack Vector: Specifically targeting verbose error messages as the source of information leakage.
  • Description: The application, by default or due to developer configuration, returns detailed error messages to the client, which are intended for debugging but are exposed in production.
  • Potential Impact: Medium. Information Disclosure. Reveals specific internal details through error messages.
  • Mitigation Strategies: Customize error handling to suppress detailed error messages in production, implement proper logging and monitoring of errors on the server side.

Attack Tree Path: 12. OR 3: Exploit Application Logic in Resolvers [HIGH RISK PATH - Resolver Logic Exploits]

  • Attack Vector: Targeting vulnerabilities within the application's resolvers, where business logic and data access are implemented.
  • Description: Attackers focus on exploiting flaws in the resolver code, such as input validation failures, authorization bypasses, or data leakage issues.
  • Potential Impact: High to Critical. Data breach, data manipulation, code execution, unauthorized access, privilege escalation, depending on the vulnerability.
  • Mitigation Strategies: Secure resolver logic, implement robust input validation, parameterized queries, proper authorization checks, secure data handling, and regular security reviews of resolver code.

Attack Tree Path: 13. AND 3.1: Resolver Input Validation Failures [CRITICAL NODE - Input Validation Failures] [HIGH RISK PATH - Injection & Business Logic Bypass]

  • Attack Vector: Exploiting insufficient or missing input validation in resolvers.
  • Description: Resolvers process user inputs from GraphQL queries. If these inputs are not properly validated and sanitized, attackers can inject malicious payloads or manipulate input to bypass business logic.
  • Potential Impact: High to Critical. Injection vulnerabilities, business logic bypass, data manipulation, unauthorized actions, privilege escalation.
  • Mitigation Strategies: Implement robust input validation and sanitization in all resolvers, use parameterized queries or ORM/ODM features to prevent injection, follow the principle of least privilege for database access.

Attack Tree Path: 14. 3.1.1: Injection Vulnerabilities (SQL, NoSQL, Command Injection, etc.) [CRITICAL NODE - Injection Vulnerabilities] [HIGH RISK PATH - Injection]

  • Attack Vector: Injecting malicious code or commands through resolver inputs to be executed by the application or database.
  • Description: Lack of input sanitization allows attackers to inject SQL, NoSQL, command injection, or other types of injection payloads into resolver arguments. These payloads are then processed by the application, potentially leading to data breaches or code execution.
  • Potential Impact: High to Critical. Data breach, data manipulation, code execution, complete system compromise.
  • Mitigation Strategies: Input sanitization, parameterized queries/operations, output encoding, principle of least privilege for database access, use of secure coding practices.

Attack Tree Path: 15. 3.1.2: Business Logic Bypass due to Input Manipulation [CRITICAL NODE - Business Logic Bypass]

  • Attack Vector: Manipulating inputs to resolvers to circumvent intended business logic and authorization controls.
  • Description: Attackers craft specific input values that exploit weaknesses in the business logic implemented in resolvers. This can allow them to bypass authorization checks, perform unauthorized actions, or manipulate data in unintended ways.
  • Potential Impact: Medium to High. Unauthorized actions, data manipulation, privilege escalation, business process disruption.
  • Mitigation Strategies: Validate input against business rules, implement proper authorization checks based on business logic, thoroughly test business logic with various input scenarios.

Attack Tree Path: 16. AND 3.2: Authorization and Authentication Flaws [CRITICAL NODE - Authorization Flaws] [HIGH RISK PATH - Authorization Bypass]

  • Attack Vector: Exploiting flaws in the authorization and authentication mechanisms implemented in resolvers.
  • Description: Attackers target weaknesses in how the application verifies user identity (authentication) and controls access to resources and actions (authorization) within resolvers.
  • Potential Impact: Critical. Unauthorized access, impersonation, data breach, unauthorized actions, privilege escalation.
  • Mitigation Strategies: Implement robust authentication and authorization mechanisms, use secure authentication methods (OAuth 2.0, JWT), implement authorization checks in all resolvers, follow the principle of least privilege.

Attack Tree Path: 17. 3.2.1: Missing Authorization Checks [CRITICAL NODE - Missing Authorization] [HIGH RISK PATH - Missing Authorization Checks]

  • Attack Vector: Exploiting the absence of authorization checks in resolvers, allowing unauthorized access.
  • Description: Developers fail to implement authorization checks in resolvers, meaning any authenticated user (or even unauthenticated in some cases) can access data and perform actions regardless of their intended permissions.
  • Potential Impact: High. Unauthorized access to data and actions, data breach, privilege escalation.
  • Mitigation Strategies: Implement authorization checks in all resolvers, use context propagation to pass authentication and authorization information, utilize authorization middleware or libraries, conduct thorough authorization testing.

Attack Tree Path: 18. AND 3.3: Data Leaks and Sensitive Data Exposure in Resolvers [CRITICAL NODE - Data Leakage] [HIGH RISK PATH - Data Exposure]

  • Attack Vector: Unintentionally exposing sensitive data through resolvers.
  • Description: Resolvers might inadvertently return sensitive data in GraphQL responses, either due to schema design flaws or insecure data handling within the resolver logic.
  • Potential Impact: Medium to High. Privacy violation, data breach, compliance issues, reputational damage.
  • Mitigation Strategies: Carefully design GraphQL schema to avoid exposing sensitive fields unnecessarily, implement field-level authorization to control access to sensitive fields, use data masking or redaction techniques, avoid logging sensitive data.

Attack Tree Path: 19. 3.3.1: Unintentional Exposure of Sensitive Fields [CRITICAL NODE - Sensitive Field Exposure] [HIGH RISK PATH - Sensitive Field Exposure]

  • Attack Vector: Specifically targeting the unintentional exposure of sensitive data fields in GraphQL responses.
  • Description: The GraphQL schema or resolver logic is designed in a way that sensitive data fields are included in responses, even when not explicitly needed or intended for all users. This can be due to over-fetching data or schema design flaws.
  • Potential Impact: Medium to High. Privacy violation, data breach, compliance issues.
  • Mitigation Strategies: Carefully design GraphQL schema, implement field-level authorization, use data masking/redaction, regularly review schema and resolvers for potential sensitive data exposure.