threat-modeling.md

Threat Model Analysis for caolan/async

Threat: Race Conditions in Asynchronous Operations

Description: An attacker might exploit race conditions by sending concurrent requests or manipulating timing to take advantage of unsynchronized asynchronous operations managed by async. For example, if async.parallel is used to perform actions that modify shared data without proper synchronization, concurrent requests could lead to data corruption or inconsistent state. An attacker could leverage this to bypass security checks or manipulate data in unintended ways.

Impact: Data corruption, inconsistent application state leading to authorization bypass (e.g., gaining unauthorized access), information disclosure (e.g., accessing data intended for another user), or denial of service (e.g., crashing the application due to corrupted state).

Async Component Affected: async.series, async.parallel, async.waterfall, async.queue, general asynchronous workflows managed by async when dealing with shared resources.

Risk Severity: High

Mitigation Strategies:

• Carefully design asynchronous workflows, especially when handling shared resources or mutable state.
• Utilize async control flow functions (series, waterfall, queue with concurrency control) to enforce sequential execution or limit concurrency where necessary to prevent race conditions.
• Implement application-level locking or synchronization mechanisms if concurrent access to shared resources is unavoidable and cannot be managed by async's built-in control flow.
• Conduct rigorous testing, including concurrency and load testing, to identify and resolve potential race conditions in asynchronous operations.

Threat: Unbounded Asynchronous Task Queues (DoS)

Description: An attacker could flood the application with requests specifically designed to add tasks to an async.queue without any limits on queue size or task acceptance rate. By continuously adding tasks, the attacker can exhaust server resources (memory, CPU) as the queue grows indefinitely and workers attempt to process them. This leads to a Denial of Service, making the application unresponsive or crashing it entirely. For instance, in a system processing user uploads via async.queue, an attacker could submit a massive number of upload requests to overwhelm the queue.

Impact: Denial of Service, application unavailability, server overload, potentially impacting other services on the same infrastructure due to resource exhaustion.

Async Component Affected: async.queue.

Risk Severity: High

Mitigation Strategies:

• Implement strict input validation and sanitization to prevent malicious input from generating excessive tasks.
• Set explicit limits on the async.queue size to prevent unbounded growth.
• Implement mechanisms to reject new tasks when the queue reaches its capacity, potentially with backpressure or error handling.
• Implement rate limiting to control the rate at which tasks are added to the queue, especially from specific IP addresses or users, to prevent abuse.
• Monitor resource usage (CPU, memory, queue length) and set up alerts to detect potential DoS attacks targeting the async.queue.