Skip to content

Latest commit

 

History

History
92 lines (76 loc) · 9.17 KB

threat-modeling.md

File metadata and controls

92 lines (76 loc) · 9.17 KB

Threat Model Analysis for shopify/sarama

Threat: Unauthorized Kafka Access via Stolen Credentials (Used by Sarama)

  • Threat: Unauthorized Kafka Access via Stolen Credentials (Used by Sarama)

    • Description: An attacker gains access to credentials (username/password, API keys, TLS certificates) that are used by the Sarama client to connect to Kafka. The attacker could then use a different Kafka client (or even a modified version of Sarama) with these stolen credentials to connect directly to the Kafka cluster, bypassing any application-specific logic. The vulnerability isn't within Sarama's code, but Sarama is the configured mechanism for connection, and its configuration holds the vulnerable credentials.
    • Impact:
      • Data breach: Sensitive data within Kafka topics is exposed.
      • Data corruption: Attacker can inject malicious messages or delete existing data.
      • Service disruption: Attacker can delete topics or disrupt consumer groups.
    • Sarama Component Affected: Config object (specifically, fields related to authentication: Config.Net.SASL, Config.Net.TLS).
    • Risk Severity: Critical
    • Mitigation Strategies:
      • Never hardcode credentials in the application code or configuration files loaded by Sarama.
      • Use environment variables to store credentials.
      • Employ a secrets management solution (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) and integrate it with the application to dynamically provide credentials to Sarama.
      • Implement strong password policies and multi-factor authentication for Kafka users (if supported by the Kafka cluster).
      • Regularly rotate credentials.
      • Monitor Kafka access logs for suspicious activity originating from the application's configured identity.

Threat: Man-in-the-Middle (MitM) Attack due to Disabled TLS or Incorrect Certificate Validation in Sarama

  • Threat: Man-in-the-Middle (MitM) Attack due to Disabled TLS or Incorrect Certificate Validation in Sarama

    • Description: An attacker intercepts the network traffic between the Sarama client and the Kafka brokers. If TLS is disabled within Sarama's configuration, or if TLS is enabled but improperly configured (e.g., InsecureSkipVerify = true in the tls.Config), the attacker can read and modify messages in transit without detection. This is a direct vulnerability in how Sarama is used to establish the connection.
    • Impact:
      • Data breach: Sensitive data transmitted between the application and Kafka is exposed.
      • Data corruption: Attacker can modify messages in transit, leading to incorrect data processing.
      • Loss of message integrity: The application cannot trust the data received from Kafka.
    • Sarama Component Affected: Config.Net.TLS configuration options. Specifically, Config.Net.TLS.Enable, Config.Net.TLS.Config, and the InsecureSkipVerify field within the Go standard library's tls.Config struct (which Sarama uses).
    • Risk Severity: Critical
    • Mitigation Strategies:
      • Always enable TLS encryption: Set Config.Net.TLS.Enable = true when connecting to a TLS-enabled Kafka cluster.
      • Provide the correct CA certificate(s) to Config.Net.TLS.Config.
      • Never set InsecureSkipVerify = true in a production environment. This bypasses crucial security checks.
      • Ensure the Kafka brokers are configured to require TLS connections.

Threat: Denial of Service (DoS) via Resource Exhaustion (Client-Side, Sarama Configuration)

  • Threat: Denial of Service (DoS) via Resource Exhaustion (Client-Side, Sarama Configuration)

    • Description: Improper configuration of Sarama's internal buffers, connection limits, or retry mechanisms can lead to the client application consuming excessive resources (memory, CPU, file descriptors, network connections). This is a direct consequence of how Sarama is configured and used, making the application vulnerable to crashes or unresponsiveness, even without malicious intent (e.g., under heavy, legitimate load).
    • Impact:
      • Application unavailability: The application using Sarama becomes unable to process Kafka messages.
      • Potential data loss: Messages may be lost if the application crashes before committing offsets (depending on consumer configuration).
      • System instability: Resource exhaustion can impact other applications running on the same host.
    • Sarama Component Affected: Various Config options related to resource management:
      • Config.Producer.Flush: Controls buffering and flushing behavior for producers.
      • Config.Consumer.Fetch: Controls the amount of data fetched by consumers.
      • Config.Net.MaxOpenRequests: Limits concurrent requests.
      • Config.Producer.Retry, Config.Consumer.Retry, Config.Metadata.Retry: Control retry behavior, which can consume resources if not limited.
    • Risk Severity: High
    • Mitigation Strategies:
      • Carefully tune Sarama's configuration parameters based on expected load, message sizes, and available resources. There's no one-size-fits-all; testing is crucial.
      • Implement rate limiting and backpressure mechanisms within the application to prevent overwhelming Sarama.
      • Monitor application resource usage (memory, CPU, open connections) and set alerts.
      • Use appropriate timeouts for network operations within Sarama's configuration.
      • Implement circuit breakers or other resilience patterns in the application to handle temporary broker unavailability gracefully.

Threat: Message Loss due to Insufficient Producer Acknowledgements (Sarama Configuration)

  • Threat: Message Loss due to Insufficient Producer Acknowledgements (Sarama Configuration)

    • Description: If the Sarama producer is configured with a low RequiredAcks setting (e.g., NoResponse), messages may be lost if a broker fails before replicating the message. The producer, as configured through Sarama, sends the message and doesn't wait for sufficient confirmation, leading to potential data loss. This is a direct result of Sarama's configuration.
    • Impact:
      • Data loss: Messages sent by the producer are not persisted to Kafka.
      • Data inconsistency: Downstream systems may receive incomplete or inconsistent data.
    • Sarama Component Affected: Config.Producer.RequiredAcks.
    • Risk Severity: High (depending on the criticality of the data; can be Critical for some use cases)
    • Mitigation Strategies:
      • Set Config.Producer.RequiredAcks to WaitForAll (or at least WaitForLocal) for strong durability guarantees. The appropriate setting depends on the application's requirements.
      • Implement error handling for producer failures (check the Errors() channel of the AsyncProducer). This allows the application to react to failures.
      • Consider using idempotent producers (Config.Producer.Idempotent = true) to prevent duplicate messages in case of retries, especially when using WaitForAll.

Threat: Deadlock in Asynchronous Operations (Improper Sarama Usage)

  • Threat: Deadlock in Asynchronous Operations (Improper Sarama Usage)

    • Description: Improper use of channels when interacting with Sarama's AsyncProducer or ConsumerGroup can lead to deadlocks within the application. For example, failing to read from the Errors() or Successes() channels of an AsyncProducer can cause the producer to block indefinitely, even if Kafka is functioning correctly. This is a direct consequence of how the application interacts with Sarama's asynchronous APIs.
    • Impact:
      • Application hangs: The application becomes completely unresponsive and stops processing messages.
      • Resource exhaustion: Goroutines and other resources may be leaked, potentially impacting the entire system.
    • Sarama Component Affected: AsyncProducer, ConsumerGroup, and their associated channels (Errors(), Successes(), Notifications()).
    • Risk Severity: High
    • Mitigation Strategies:
      • Always read from the Errors() and Successes() channels of an AsyncProducer in a separate goroutine. This is essential for correct operation.
      • Always handle the Notifications() channel of a ConsumerGroup in a separate goroutine.
      • Use buffered channels where appropriate to avoid blocking.
      • Use timeouts when reading from channels to prevent indefinite waits.
      • Thoroughly test concurrent code for potential deadlocks using tools like the Go race detector and deadlock detection tools.