attack-surface.md

Attack Surface Analysis for caolan/async

Attack Surface: Improper Error Handling in Asynchronous Flows

• Description: Failure to properly handle errors within asynchronous operations managed by async, leading to silent failures, unexpected states, or crashes.
• How async contributes to the attack surface: async provides control flow mechanisms, but error handling is developer-dependent within callbacks. Lack of proper error handling in async flows can lead to critical failures.
• Example: In an async.waterfall processing financial transactions, if a step involving payment gateway communication fails and the error is ignored, the transaction might proceed without payment confirmation, leading to financial loss.
• Impact: Data corruption, application instability, financial loss, security bypasses due to incomplete critical operations.
• Risk Severity: High
• Mitigation Strategies:
  • Mandatory error checks in all callbacks: Enforce error checking in every callback function used within async flows.
  • Utilize final callbacks for global error handling: Implement final callbacks in async.series, async.parallel, etc., to catch and handle any unhandled errors propagating through the flow.
  • Implement circuit breaker pattern: For critical asynchronous operations, use a circuit breaker pattern to prevent cascading failures and provide graceful degradation in case of repeated errors.
  • Automated testing with error injection: Develop automated tests that intentionally introduce errors into asynchronous flows to verify error handling logic.

Attack Surface: Logic Vulnerabilities due to Asynchronous Complexity

• Description: Logical errors in the design of complex asynchronous workflows using async, leading to race conditions, incorrect execution order, or critical business logic flaws.
• How async contributes to the attack surface: async facilitates complex asynchronous logic, but intricate flows can be prone to logical errors if not carefully designed and tested. Incorrect use of async can introduce critical flaws.
• Example: An application uses async.parallel to update user roles and permissions concurrently. A logical flaw in the flow might lead to a race condition where a user's permissions are partially updated, granting them unintended elevated privileges.
• Impact: Privilege escalation, unauthorized access to sensitive data or functionality, business logic bypasses leading to security breaches.
• Risk Severity: High
• Mitigation Strategies:
  • Formal verification of asynchronous logic: For critical workflows, consider using formal verification techniques or model checking to analyze the asynchronous logic for potential flaws.
  • Detailed documentation and flow diagrams: Create comprehensive documentation and flow diagrams for complex async workflows to improve understanding and identify potential logical errors.
  • Rigorous integration testing with concurrency focus: Implement extensive integration tests that specifically target concurrent execution scenarios to detect race conditions and logical flaws in asynchronous flows.
  • Code reviews by multiple developers with asynchronous expertise: Ensure code reviews for async logic are conducted by developers experienced in asynchronous programming and potential concurrency issues.

Attack Surface: Denial of Service (DoS) through Asynchronous Task Queues

• Description: Abuse of async.queue or async.parallelLimit by overwhelming the application with tasks, leading to resource exhaustion and service unavailability.
• How async contributes to the attack surface: async.queue and async.parallelLimit manage task processing, but without proper safeguards, they can be exploited for DoS attacks by flooding the task system.
• Example: A password reset service uses async.queue to send password reset emails. If rate limiting is insufficient, an attacker can flood the service with password reset requests for numerous accounts, overwhelming the email sending queue and potentially causing delays or failures for legitimate password resets, effectively denying service.
• Impact: Service unavailability, inability for legitimate users to access critical functionalities, reputational damage.
• Risk Severity: High
• Mitigation Strategies:
  • Strict input validation and sanitization for task parameters: Validate and sanitize all inputs that influence tasks added to async.queue or async.parallelLimit to prevent malicious task creation.
  • Aggressive rate limiting and request throttling: Implement robust rate limiting and request throttling mechanisms to restrict the number of tasks submitted, especially from single IP addresses or user accounts.
  • Queue size limits and backpressure mechanisms: Configure async.queue with maximum queue size limits and implement backpressure mechanisms to reject or delay new tasks when the queue is full.
  • Resource-based queue management: Dynamically adjust queue concurrency or task acceptance based on real-time resource utilization (CPU, memory) to prevent overload.

Attack Surface: Callback/Function Injection (Indirect)

• Description: Indirect function injection vulnerabilities where user-controlled data influences the callbacks or functions executed within async flows, leading to arbitrary code execution.
• How async contributes to the attack surface: async executes callbacks provided by the application. If these callbacks are constructed using untrusted input, async becomes a vector for executing injected code.
• Example: An application uses user input to dynamically select a data transformation function to be used within an async.map operation. If input validation is missing, an attacker could inject a malicious function name, leading to arbitrary code execution on the server when async.map processes the data.
• Impact: Arbitrary code execution, complete system compromise, data breaches, full control over the application and server.
• Risk Severity: Critical
• Mitigation Strategies:
  • Absolutely avoid dynamic callback/function construction from user input: Never directly use user input to determine which functions or callbacks are executed within async flows.
  • Strict whitelisting of allowed functions/callbacks: If function selection is necessary, use a predefined whitelist of safe functions and strictly validate user input against this whitelist.
  • Input validation and sanitization as a primary defense: Thoroughly validate and sanitize all user inputs to prevent injection attempts at all levels of the application, especially before they can influence function calls.
  • Principle of least privilege and sandboxing: Run the application with the minimum necessary privileges and consider sandboxing techniques to limit the impact of potential code execution vulnerabilities.

Attack Surface: Dependency Chain Vulnerabilities

• Description: Critical vulnerabilities discovered in the async library itself or its dependencies, which could be directly exploited in applications using vulnerable versions.
• How async contributes to the attack surface: By depending on async, applications inherit any vulnerabilities present in the library. Critical vulnerabilities in async can have widespread impact.
• Example: A remote code execution vulnerability is discovered in a widely used version of async. Applications using this vulnerable version become immediately susceptible to remote attacks.
• Impact: Remote code execution, data breaches, complete system compromise, widespread exploitation across affected applications.
• Risk Severity: Critical (depending on the nature of the vulnerability)
• Mitigation Strategies:
  • Proactive dependency updates and patching: Implement a robust process for regularly updating async and all other dependencies to the latest versions to patch known vulnerabilities promptly.
  • Automated dependency scanning and vulnerability alerts: Use automated tools to continuously scan project dependencies for known vulnerabilities and set up alerts for new security advisories.
  • Security audits of dependencies: Periodically conduct security audits of project dependencies, including async, to identify and assess potential risks.
  • Software Composition Analysis (SCA) integration: Integrate SCA tools into the development pipeline to automatically manage and monitor open-source dependencies for security and license compliance.