mitigations.md

Mitigation Strategies Analysis for hibiken/asynq

Mitigation Strategy: Encrypt Sensitive Task Payloads using Asynq's Encryption

• Description:

  1. Identify task payloads that contain sensitive information.
  2. Configure asynq's built-in encryption by setting the EncryptionKey option within the asynq.Config struct when creating both the asynq.Client and asynq.Server.
  3. Generate a strong, randomly generated encryption key.
  4. Securely manage and distribute this encryption key to both the client (enqueuing tasks) and server (processing tasks) components of your asynq application. Ensure the same key is used on both sides.

• Threats Mitigated:

  • Data Breach via Task Queue Exposure (High Severity): If unauthorized access is gained to the Redis instance storing the asynq task queue, encryption ensures sensitive data within task payloads remains unreadable without the decryption key configured in asynq.
  • Data Tampering within Task Queue (Medium Severity): While primarily for confidentiality, encryption using authenticated modes (like AES-GCM used by asynq) can offer some protection against tampering with task payloads while they are in the queue.

• Impact:

  • Data Breach: Significantly reduces the risk. Even with Redis access, encrypted payloads are protected.
  • Data Tampering: Moderately reduces the risk by making tampering detectable (though not the primary goal).

• Currently Implemented: Encryption is implemented for user-related data processing tasks in the user_service using asynq's EncryptionKey feature.

• Missing Implementation: Encryption is not yet enabled for internal system tasks handled by asynq, such as background maintenance jobs or administrative tasks.

Mitigation Strategy: Redis Authentication for Asynq Connection

• Description:

  1. Enable authentication in your Redis server configuration using the requirepass directive. Set a strong, randomly generated password.
  2. When creating asynq.Config for both asynq.Client and asynq.Server, provide the Redis password using the Password field in the RedisClientOpt or RedisClusterClientOpt.
  3. Ensure the asynq client and server are configured with the correct Redis password to establish a connection.

• Threats Mitigated:

  • Unauthorized Access to Asynq Task Queue via Redis (High Severity): Prevents unauthorized clients or services from connecting to the Redis instance used by asynq and directly manipulating the task queue (e.g., reading, adding, deleting tasks) outside of the intended asynq application flow.

• Impact:

  • Unauthorized Access to Asynq Task Queue: Significantly reduces the risk by requiring authentication for Redis access, which is the foundation of asynq's operation.

• Currently Implemented: Redis authentication is enabled for the Redis instance used by asynq, and the password is provided to asynq.Client and asynq.Server via environment variables.

• Missing Implementation: While Redis authentication is enabled, periodic rotation of the Redis password used by asynq is not yet implemented.

Mitigation Strategy: Rate Limiting Task Enqueueing at Asynq Client

• Description:

  1. Implement rate limiting logic before tasks are enqueued using the asynq.Client. This can be done at the application level where tasks are initiated.
  2. Use a rate limiting algorithm (e.g., token bucket, leaky bucket) to control the rate at which tasks are submitted to the asynq queue.
  3. Configure rate limits based on the processing capacity of your asynq.Server instances and the desired application behavior.
  4. Handle rate limit exceeded scenarios gracefully, such as delaying task enqueueing, rejecting tasks with informative error messages, or implementing a retry mechanism with backoff.

• Threats Mitigated:

  • Asynq Task Queue Denial of Service (DoS) (High Severity): Prevents malicious or misconfigured components from overwhelming the asynq task queue by enqueuing an excessive number of tasks, potentially leading to performance degradation or service unavailability.

• Impact:

  • Asynq Task Queue Denial of Service (DoS): Significantly reduces the risk of DoS attacks targeting the task queue itself by controlling the inflow of tasks at the source.

• Currently Implemented: Rate limiting is implemented at the API gateway level, which indirectly limits the rate of user-triggered tasks enqueued via asynq.

• Missing Implementation: Fine-grained rate limiting directly within services that enqueue tasks using asynq.Client is not yet implemented. This would provide more precise control and prevent internal services from unintentionally overloading the task queue.

Mitigation Strategy: Monitoring Asynq Server and Queue Metrics

• Description:

  1. Utilize asynq's built-in monitoring capabilities and integrate with external monitoring systems.
  2. Monitor key asynq metrics such as:
     • Queue length (number of pending tasks in each queue)
     • Processing rate (tasks processed per second)
     • Error rate (task failure count)
     • Retry rate (tasks being retried)
     • Dead-letter queue size
     • Asynq server process health (CPU, memory usage)
  3. Set up alerts based on thresholds for these metrics to detect anomalies or potential issues (e.g., unusually long queue length, high error rate).

• Threats Mitigated:

  • Delayed Detection of Task Processing Issues (Medium Severity): Proactive monitoring of asynq metrics allows for early detection of performance bottlenecks, errors in task handlers, or potential DoS attempts targeting the task queue, enabling faster incident response.
  • Asynq Server Resource Exhaustion (Medium Severity): Monitoring server resource usage helps identify if asynq server processes are under stress or experiencing resource exhaustion, which could impact task processing and overall application stability.

• Impact:

  • Delayed Detection of Task Processing Issues: Moderately reduces the risk by improving visibility and response time to issues affecting task processing.
  • Asynq Server Resource Exhaustion: Moderately reduces the risk by enabling proactive identification of resource constraints.

• Currently Implemented: Basic monitoring of queue length and task error counts is implemented using Prometheus and Grafana, collecting metrics exposed by asynq.

• Missing Implementation: Monitoring needs to be expanded to include more comprehensive asynq metrics (processing latency, retry rates, DLQ size, server resource usage). Alerting rules need to be refined for better anomaly detection and reduced false positives.

Mitigation Strategy: Utilize Asynq's Dead-Letter Queue (DLQ)

• Description:

  1. Leverage asynq's built-in Dead-Letter Queue (DLQ) feature. Tasks that fail after exceeding their retry limit are automatically moved to the DLQ.
  2. Regularly monitor the DLQ for tasks that have failed permanently.
  3. Implement a process to investigate and handle tasks in the DLQ. This might involve manual retries, error analysis, or data correction.
  4. Configure appropriate retry policies for tasks to ensure that transient errors are handled by retries before tasks are moved to the DLQ.

• Threats Mitigated:

  • Unprocessed Tasks and Data Loss (Medium Severity): The DLQ prevents tasks from being indefinitely retried and potentially lost if they consistently fail. It provides a mechanism to identify and address permanently failing tasks.
  • Infinite Retry Loops and Resource Waste (Medium Severity): By moving persistently failing tasks to the DLQ, asynq avoids infinite retry loops that can consume resources and mask underlying issues.

• Impact:

  • Unprocessed Tasks and Data Loss: Moderately reduces the risk by providing a mechanism to recover and handle failed tasks that would otherwise be lost.
  • Infinite Retry Loops and Resource Waste: Moderately reduces the risk by preventing resource exhaustion due to continuously retrying tasks that are unlikely to succeed.

• Currently Implemented: The DLQ feature is enabled in asynq configuration, and tasks exceeding retry limits are moved to the DLQ.

• Missing Implementation: Automated monitoring and alerting for the DLQ size are not yet implemented. A process for regularly reviewing and handling tasks in the DLQ (e.g., a dedicated dashboard or administrative interface) is also missing.

Mitigation Strategy: Configure Asynq Server Resource Limits

• Description:

  1. Configure resource limits (CPU, memory) for the processes running asynq.Server. This can be done using operating system-level tools (e.g., ulimit on Linux) or containerization platforms (e.g., Kubernetes resource limits).
  2. Set appropriate resource limits based on the expected task load and the available resources on the server.
  3. Monitor resource usage of asynq.Server processes to ensure they are operating within the configured limits and adjust limits as needed.

• Threats Mitigated:

  • Asynq Server Resource Exhaustion due to Malicious or Runaway Tasks (Medium Severity): Prevents a single malicious task or a poorly written task handler from consuming excessive resources (CPU, memory) on the asynq server, potentially impacting other tasks or causing server instability.
  • Denial of Service (DoS) against Asynq Server (Medium Severity): Resource limits can help mitigate certain types of DoS attacks that attempt to exhaust server resources by submitting resource-intensive tasks.

• Impact:

  • Asynq Server Resource Exhaustion: Moderately reduces the risk by limiting the impact of resource-intensive tasks on the server.
  • Denial of Service (DoS) against Asynq Server: Moderately reduces the risk by limiting resource consumption during potential DoS attempts.

• Currently Implemented: Resource limits are partially implemented using container resource limits in the Kubernetes environment where asynq.Server instances are deployed.

• Missing Implementation: Resource limits are not consistently applied across all deployment environments (e.g., development, staging, production). More granular resource limits based on task priority or queue type are not yet explored.

Mitigation Strategy: Carefully Configure Asynq Task Retry Policies

• Description:

  1. Define appropriate retry policies for each task type when registering task handlers with asynq.Server.
  2. Set reasonable values for MaxRetry (maximum retry attempts) and consider using exponential backoff strategies (using asynq.RetryDelayFunc) to avoid overwhelming the system with retries after persistent failures.
  3. Avoid setting excessively high MaxRetry values or infinite retries, as this can exacerbate DoS conditions or mask underlying issues.
  4. Consider different retry policies for different task types based on their criticality and expected failure modes.

• Threats Mitigated:

  • Denial of Service (DoS) via Infinite Retry Loops (Medium Severity): Prevents scenarios where persistently failing tasks are retried indefinitely, consuming resources and potentially delaying the processing of other tasks.
  • Resource Waste due to Excessive Retries (Low Severity): Avoids unnecessary resource consumption by limiting the number of retries for tasks that are unlikely to succeed after multiple attempts.

• Impact:

  • Denial of Service (DoS) via Infinite Retry Loops: Moderately reduces the risk by preventing uncontrolled retry behavior.
  • Resource Waste due to Excessive Retries: Slightly reduces the risk by optimizing resource utilization related to task retries.

• Currently Implemented: Default retry policies are used for most tasks, with a standard MaxRetry value.

• Missing Implementation: Task-specific retry policies are not consistently defined based on task criticality and failure characteristics. Exponential backoff strategies are not widely used. A review and refinement of retry policies across all task types are needed.