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attack-surface.md

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Attack Surface Analysis for hibiken/asynq

Attack Surface: 1. Redis Exposure and Access Control Breaches

  • Description: Unauthorized access to the Redis instance used by asynq.
  • How Asynq Contributes: asynq requires Redis for its operation. The security of the Redis instance is directly tied to the security of the asynq system. asynq's reliance on Redis makes this a critical concern.
  • Example: An attacker scans for open Redis ports (default 6379) and finds an unauthenticated instance used by an asynq application.
  • Impact:
    • Complete control over task queues: The attacker can read, modify, delete, and inject tasks.
    • Data exfiltration: Sensitive data passed as task arguments can be stolen.
    • Denial of Service (DoS): The attacker can flood the queue or delete all tasks.
    • Potential for further compromise: The attacker might use access to Redis to pivot to other parts of the system.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Require Authentication: Always enable Redis authentication with a strong, unique password. This is non-negotiable.
    • Network Segmentation: Restrict network access to Redis using firewalls or security groups. Only allow connections from trusted application servers and workers. This is crucial.
    • Use TLS: Encrypt communication between asynq clients/workers and Redis using TLS. This protects data in transit.
    • Redis ACLs: Implement Redis Access Control Lists (ACLs) to grant the asynq user only the necessary permissions. Avoid allcommands.
    • Regular Audits: Periodically review Redis configuration and network access rules.

Attack Surface: 2. Malicious Task Injection

  • Description: An attacker successfully enqueues a task with a malicious payload designed to exploit vulnerabilities in the task handler.
  • How Asynq Contributes: asynq is the mechanism by which the malicious task is delivered and executed. The attacker leverages asynq's core functionality (running tasks) to achieve code execution. This is a direct exploitation of asynq's purpose.
  • Example: An application has a web form that takes user input and uses it to create an asynq task. The form lacks proper input validation, allowing an attacker to inject a command into a task argument that will be executed by the worker (e.g., "; rm -rf /; #").
  • Impact:
    • Remote Code Execution (RCE): The attacker can execute arbitrary code on the worker server. This is the worst-case scenario.
    • Data Breach: The attacker can steal sensitive data from the worker or connected systems.
    • System Compromise: The attacker can gain full control of the worker server.
    • DoS: The attacker can disrupt the worker or the entire application.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Strict Input Validation: Thoroughly validate and sanitize all inputs used to construct task payloads before enqueuing the task. This is the primary and most critical defense. No exceptions.
    • Schema Validation: Use a schema validation library (e.g., jsonschema in Python, Joi in Node.js) to enforce the expected structure and data types of task arguments. This adds a strong layer of defense.
    • Principle of Least Privilege: Run worker processes with the minimum necessary privileges. Never run workers as root.
    • Sandboxing: Strongly consider running workers in isolated environments (e.g., containers, sandboxes) to limit the impact of a successful exploit. This is a crucial mitigation for RCE.
    • Secure Coding Practices: Follow secure coding guidelines within task handlers to prevent common vulnerabilities (e.g., command injection, SQL injection, path traversal).

Attack Surface: 3. Denial of Service (DoS) via Large Payloads or Excessive Tasks

  • Description: An attacker overwhelms the asynq system by submitting tasks with excessively large payloads or by enqueuing a massive number of tasks.
  • How Asynq Contributes: asynq's performance and stability are directly affected by the volume and size of tasks it processes. The attacker exploits asynq's resource consumption.
  • Example: An attacker sends thousands of tasks, each with a multi-megabyte payload, causing Redis to run out of memory and the workers to become unresponsive.
  • Impact:
    • Service Outage: The application becomes unavailable.
    • Resource Exhaustion: Redis and worker servers may crash or become unusable.
  • Risk Severity: High
  • Mitigation Strategies:
    • Payload Size Limits: Enforce strict limits on the size of task payloads at the point of task creation. Reject tasks that exceed the limit.
    • Rate Limiting: Limit the rate at which tasks can be enqueued, both globally and per user/IP address. This is essential to prevent flooding.
    • Queue Length Monitoring: Monitor the length of the asynq queues. Alert on unusually long queues. This provides early warning.
    • Resource Monitoring: Monitor Redis memory usage, worker CPU and memory usage, and network traffic. Alert on high resource utilization.
    • Horizontal Scaling: Use multiple Redis instances and worker processes to distribute the load. This increases resilience.

Attack Surface: 4. Dependency Vulnerabilities (in Asynq Itself)

  • Description: Vulnerabilities within the asynq library itself.
  • How Asynq Contributes: This is a direct vulnerability in the core component being analyzed.
  • Example: A hypothetical vulnerability in asynq's task deserialization logic allows an attacker to craft a malicious task payload that triggers remote code execution when the task is processed, even if the task handler itself is secure.
  • Impact:
    • Remote Code Execution (RCE): The attacker can execute arbitrary code on the worker server.
    • Data Breach: The attacker can steal sensitive data.
    • System Compromise: The attacker can gain full control of the worker.
  • Risk Severity: High (Potentially Critical, depending on the vulnerability)
  • Mitigation Strategies:
    • Vulnerability Scanning: Regularly scan asynq itself for known vulnerabilities. Use tools that specifically target the language and package manager used (e.g., pip-audit for Python).
    • Prompt Updates: Apply security updates to asynq immediately when they are released. This is the most important mitigation.
    • Vendor Security Advisories: Monitor security advisories from the asynq developers (Hibiken). Subscribe to any relevant mailing lists or notification channels.