threat-modeling.md

Threat Model Analysis for macrozheng/mall

Threat: Rogue Microservice Registration

• Threat: Rogue Microservice Registration

  • Description: An attacker registers a malicious microservice with the service discovery mechanism (e.g., Eureka, Consul) to intercept legitimate traffic intended for mall services. The attacker could steal data (order details, customer information), manipulate responses (change prices, confirm fake orders), or cause a denial of service.
  • Impact: Data breach, data manipulation, service disruption, loss of customer trust, financial loss.
  • Affected Component: Service Discovery (Eureka/Consul), Spring Cloud Gateway, all mall microservices (e.g., mall-order, mall-product, mall-auth).
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Implement mutual TLS (mTLS) between all mall microservices.
    • Secure the service discovery mechanism itself: strong passwords, network segmentation (separate network for internal services), strict access control.
    • Implement service-to-service authentication using JWTs or other secure tokens, verifying the identity of each microservice.
    • Regularly audit service registrations and configurations within Eureka/Consul.

Threat: Malicious Message Injection (RabbitMQ)

• Threat: Malicious Message Injection (RabbitMQ)

  • Description: An attacker gains access to the RabbitMQ message broker used by mall and injects malicious messages. These messages could trigger unintended actions within mall's microservices, such as creating fraudulent orders, canceling legitimate orders, manipulating inventory data, or triggering unauthorized promotions.
  • Impact: Financial loss, data corruption (order database, inventory database), operational disruption, reputational damage.
  • Affected Component: RabbitMQ, mall-order, mall-promotion, mall-inventory, and any other mall microservice consuming messages from RabbitMQ.
  • Risk Severity: High
  • Mitigation Strategies:
    • Use strong, unique passwords for RabbitMQ users specifically configured for mall.
    • Implement message signing and verification (e.g., using digital signatures) within the mall microservices to ensure message authenticity and integrity. This requires code changes in message producers and consumers.
    • Use dedicated RabbitMQ queues with appropriate access controls (ACLs) for different message types and mall microservice consumers.
    • Implement input validation and sanitization for all message handlers within the mall microservices.
    • Monitor RabbitMQ queues used by mall for unusual activity (high message rates, unexpected message types).

Threat: JWT Forgery

• Threat: JWT Forgery

  • Description: An attacker compromises the JWT secret key used by mall-auth or finds a vulnerability in the JWT library used by mall. This allows them to forge JWT tokens, granting them unauthorized access to mall's resources or allowing them to impersonate other mall users.
  • Impact: Unauthorized access to sensitive data (customer data, order data), privilege escalation, account takeover, reputational damage.
  • Affected Component: mall-auth, Spring Security, all mall microservices relying on JWT authentication.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Use a strong, randomly generated, and long JWT secret key specific to the mall deployment.
    • Store the secret key securely outside of the mall codebase (e.g., using a secrets management solution like HashiCorp Vault, AWS Secrets Manager, or environment variables securely injected at runtime). Never commit the secret key to the code repository.
    • Implement short token expiration times (e.g., 15-30 minutes) and use refresh tokens for longer sessions within mall-auth.
    • Consider using asymmetric keys (public/private key pairs) for JWT signing within mall-auth.
    • Validate the JWT signature and claims (issuer, audience, expiration) on every request within each mall microservice.
    • Regularly rotate the JWT secret key.

Threat: Elasticsearch Data Tampering

• Threat: Elasticsearch Data Tampering

  • Description: An attacker gains direct access to the Elasticsearch cluster used by mall and modifies indexed data (product details, prices, search results). This could lead to misinformation, price manipulation, or denial of service by corrupting the mall-search index.
  • Impact: Data corruption, financial loss (due to incorrect pricing), reputational damage, service disruption (search functionality).
  • Affected Component: Elasticsearch, mall-search, mall-product.
  • Risk Severity: High
  • Mitigation Strategies:
    • Enable Elasticsearch security features (authentication, authorization, TLS) for the cluster used by mall.
    • Restrict network access to the Elasticsearch cluster (firewall, security groups), allowing access only from authorized mall microservices.
    • Use strong passwords and role-based access control (RBAC) within Elasticsearch, limiting mall-search's access to only necessary indices and operations.
    • Implement data validation and sanitization before indexing data within mall-product and mall-search.
    • Regularly audit Elasticsearch data and configurations specific to the mall indices.

Threat: Redis Data Tampering

• Threat: Redis Data Tampering

  • Description: An attacker gains access to the Redis server used by mall and modifies cached data (product prices, user sessions, inventory counts). This could lead to inconsistencies, incorrect pricing displayed to customers, or unauthorized access.
  • Impact: Data inconsistency, financial loss (incorrect pricing), potential privilege escalation (session hijacking), service disruption.
  • Affected Component: Redis, mall-common (likely where Redis caching logic resides), any mall microservice using Redis (e.g., mall-product, mall-order, mall-auth).
  • Risk Severity: High
  • Mitigation Strategies:
    • Enable Redis authentication (password protection) for the Redis instance used by mall.
    • Use TLS for communication between mall microservices and Redis.
    • Restrict network access to the Redis instance, allowing connections only from authorized mall microservices.
    • Use Redis ACLs (Access Control Lists) for fine-grained access control, limiting the operations each mall microservice can perform on Redis.
    • Avoid storing highly sensitive data directly in Redis; encrypt sensitive data within the mall microservices before caching.
    • Implement appropriate cache eviction policies within mall-common to prevent data staleness.

Threat: Microservice Communication Tampering

• Threat: Microservice Communication Tampering

  • Description: An attacker intercepts and modifies requests/responses between mall's microservices. This could lead to data corruption, manipulation of business logic (e.g., changing order totals), or unauthorized access to data.
  • Impact: Data corruption, data breach, service disruption, financial loss.
  • Affected Component: All mall microservices, Spring Cloud Gateway.
  • Risk Severity: High
  • Mitigation Strategies:
    • Use HTTPS (TLS) for all inter-service communication between mall microservices. This is a configuration change within each microservice and the gateway.
    • Implement message integrity checks (e.g., using checksums or digital signatures) within the mall microservices. This requires code changes.
    • Consider using a service mesh (e.g., Istio, Linkerd) for enhanced security and observability, although this adds complexity.

Threat: Direct Database Access

• Threat: Direct Database Access

  • Description: An attacker gains direct access to the database (MySQL or MongoDB) used by mall and bypasses application-level security controls. They could then modify, delete, or steal data (customer information, order details, product data).
  • Impact: Data breach, data corruption, data loss, complete system compromise.
  • Affected Component: MySQL, MongoDB, all mall microservices interacting with the database (e.g., mall-order, mall-product, mall-user).
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Use strong, unique passwords for database users specifically for the mall application.
    • Restrict network access to the database servers, allowing connections only from authorized mall microservices.
    • Implement database-level auditing and monitoring, specifically tracking access and changes from mall microservices.
    • Use database user accounts with least privilege access (grant only necessary permissions to each mall microservice). For example, mall-order should only have access to order-related tables.
    • Regularly back up the mall database.
    • Consider using database encryption at rest.

Threat: Elasticsearch Denial of Service

• Threat: Elasticsearch Denial of Service

  • Description: An attacker sends a large volume of complex or unoptimized search requests to mall-search, overwhelming the Elasticsearch cluster and causing performance degradation or denial of service for the mall application's search functionality.
  • Impact: Service disruption (search functionality unavailable), performance degradation, inability for customers to search for products.
  • Affected Component: Elasticsearch, mall-search.
  • Risk Severity: High
  • Mitigation Strategies:
    • Optimize Elasticsearch queries for performance within mall-search.
    • Implement rate limiting and throttling for search requests, either at the API Gateway or within mall-search itself.
    • Monitor Elasticsearch cluster resource usage (CPU, memory, disk I/O) and scale resources as needed for the mall deployment.
    • Use circuit breakers within mall-search to prevent cascading failures.
    • Implement query validation and sanitization within mall-search to prevent malicious or overly complex queries.

Threat: RabbitMQ Denial of Service

• Threat: RabbitMQ Denial of Service

  • Description: An attacker floods the RabbitMQ instance used by mall with messages, or slow consumers cause a buildup of messages, leading to queue exhaustion and service disruption for mall's asynchronous operations.
  • Impact: Service disruption, message loss, delayed processing of orders and other operations within mall.
  • Affected Component: RabbitMQ, mall-order, mall-promotion, mall-inventory (and any other mall microservices using RabbitMQ).
  • Risk Severity: High
  • Mitigation Strategies:
    • Implement rate limiting for message producers within the relevant mall microservices.
    • Ensure consumers (within mall microservices) are properly scaled to handle the expected message load.
    • Use message acknowledgments and retries appropriately within the mall microservices.
    • Monitor RabbitMQ queue lengths and resource usage for the queues used by mall.
    • Implement dead-letter queues within mall's RabbitMQ configuration to handle undeliverable messages.

Threat: Microservice Resource Exhaustion

• Threat: Microservice Resource Exhaustion

  • Description: An attacker targets a specific mall microservice (e.g., mall-order, mall-product) with a large number of requests, exhausting its resources (CPU, memory, network connections) and causing it to crash or become unresponsive.
  • Impact: Service disruption (specific mall functionality unavailable), performance degradation, potential cascading failures to other mall services.
  • Affected Component: Any mall microservice.
  • Risk Severity: High
  • Mitigation Strategies:
    • Implement rate limiting and throttling for all mall microservice endpoints, either at the API Gateway or within each microservice itself.
    • Use circuit breakers within each mall microservice to prevent cascading failures.
    • Monitor resource usage (CPU, memory, network) for each mall microservice and scale resources as needed.
    • Implement auto-scaling based on resource utilization for the mall deployment.

Threat: Spring Security Misconfiguration

• Threat: Spring Security Misconfiguration

  • Description: Incorrectly configured Spring Security roles and permissions within mall-auth or other mall microservices allow users with limited privileges to access unauthorized resources or perform unauthorized actions within the mall application.
  • Impact: Privilege escalation, unauthorized access to data, unauthorized actions (e.g., modifying orders, changing user roles).
  • Affected Component: mall-auth, Spring Security, all mall microservices relying on Spring Security.
  • Risk Severity: High
  • Mitigation Strategies:
    • Carefully define roles and permissions in Spring Security within mall-auth and other microservices, based on the principle of least privilege.
    • Use method-level security annotations (e.g., @PreAuthorize, @PostAuthorize) within the mall microservices to enforce fine-grained access control.
    • Regularly review and audit Spring Security configurations across all mall microservices.
    • Use a well-defined and documented authorization policy for the entire mall application.
    • Thoroughly test authorization rules within the mall application.

Threat: Internal Endpoint Exposure

• Threat: Internal Endpoint Exposure

  • Description: The Spring Cloud Gateway used by mall is misconfigured, exposing internal mall microservice endpoints that should not be directly accessible from the outside.
  • Impact: Unauthorized access to internal mall services, potential for data breaches or manipulation.
  • Affected Component: Spring Cloud Gateway, all mall microservices.
  • Risk Severity: High
  • Mitigation Strategies:
    • Carefully configure routing rules in the API Gateway to expose only the necessary public endpoints for the mall application.
    • Implement authentication and authorization at the gateway level to protect internal mall services.
    • Use a whitelist approach for allowed routes within the gateway configuration.
    • Regularly review and audit gateway configurations specific to the mall deployment.