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Attack Tree Analysis for caolan/async

Objective: DoS or RCE via Async Library Exploitation

Attack Tree Visualization

Goal: DoS or RCE via Async Library Exploitation ├── 1. Denial of Service (DoS) [HIGH RISK] │ ├── 1.1. Resource Exhaustion (CPU/Memory) [HIGH RISK] │ │ ├── 1.1.1. async.forever Loop with Uncontrolled Input [HIGH RISK][CRITICAL] │ │ ├── 1.1.2. async.parallel / async.series with Excessive Tasks [HIGH RISK][CRITICAL] │ │ ├── 1.1.3. async.queue with Unbounded Queue and Slow Workers [HIGH RISK] │ │ └── 1.1.5. Improper Error Handling Leading to Unreleased Resources [CRITICAL] │ └── 1.2. Event Loop Blocking [HIGH RISK] │ ├── 1.2.1. Synchronous Operations within Async Callbacks [HIGH RISK][CRITICAL] ├── 2. Remote Code Execution (RCE) │ ├── 2.1. Callback Injection via Unvalidated Input [CRITICAL] └── 2.3 Dependency Confusion/Supply Chain Attack [CRITICAL]

Attack Tree Path: 1.1.1. async.forever Loop with Uncontrolled Input [HIGH RISK][CRITICAL]

  • Description: The async.forever function repeatedly executes a given asynchronous task. If the task's execution time or termination condition is dependent on user-supplied input, and that input is not properly validated or sanitized, an attacker can provide input that causes the task to run indefinitely or for an extremely long time. This consumes CPU resources and can lead to a Denial of Service.
  • Exploit: The attacker provides input that manipulates the logic within the async.forever callback to prevent it from ever calling its completion callback (the second argument to async.forever).
  • Mitigation:
    • Implement strict input validation and sanitization to ensure that the input cannot cause excessively long or infinite execution.
    • Introduce a timeout mechanism within the async.forever callback. If the task takes longer than a predefined threshold, force its termination.
    • If async.forever is not strictly necessary, consider using a different control flow mechanism (e.g., a loop with a finite number of iterations or a condition that is guaranteed to eventually become false).
  • Example: Imagine a callback that processes a user-uploaded file. If the processing time is proportional to the file size, and there's no file size limit, an attacker could upload a massive file, causing the async.forever callback to consume excessive CPU resources.
  • Likelihood: Medium
  • Impact: High
  • Effort: Low
  • Skill Level: Beginner
  • Detection Difficulty: Medium

Attack Tree Path: 1.1.2. async.parallel / async.series with Excessive Tasks [HIGH RISK][CRITICAL]

  • Description: async.parallel and async.series execute multiple asynchronous tasks, either concurrently or sequentially. If the number of tasks to be executed is determined by user input, and that input is not limited, an attacker can provide input that causes the application to create a very large number of tasks. This can exhaust CPU and memory resources, leading to a Denial of Service.
  • Exploit: The attacker provides input that results in a large array or object being passed to async.parallel or async.series, causing a massive number of tasks to be spawned.
  • Mitigation:
    • Limit the number of tasks that can be created based on user input. Implement a maximum threshold.
    • Use async.parallelLimit or async.seriesLimit to control the concurrency of task execution, preventing the application from being overwhelmed.
    • Implement a queue with a maximum size to manage the tasks, preventing unbounded memory growth.
  • Example: An application that processes a list of URLs provided by the user. If each URL processing spawns a task, and there's no limit on the number of URLs, an attacker could provide a huge list, overwhelming the application.
  • Likelihood: Medium
  • Impact: High
  • Effort: Low
  • Skill Level: Beginner
  • Detection Difficulty: Medium

Attack Tree Path: 1.1.3. async.queue with Unbounded Queue and Slow Workers [HIGH RISK]

  • Description: async.queue provides a queue for processing asynchronous tasks with a configurable concurrency limit. If the queue's size is not limited, and the workers processing the tasks are slow or become blocked, an attacker can flood the queue with tasks faster than they can be processed. This leads to unbounded memory growth and eventual application failure.
  • Exploit: The attacker sends a large number of requests that add tasks to the queue, exceeding the processing capacity of the workers.
  • Mitigation:
    • Use the concurrency limit feature of async.queue to control the number of workers.
    • Monitor the queue length. If it exceeds a threshold, implement backpressure mechanisms (e.g., temporarily stop accepting new tasks or reject new requests).
    • Ensure that the workers are efficient and have timeouts to prevent them from getting stuck on a single task.
  • Example: An application that uses a queue to process image uploads. If an attacker uploads many images rapidly, and the image processing is slow, the queue can grow without bound, consuming all available memory.
  • Likelihood: Medium
  • Impact: High
  • Effort: Low to Medium
  • Skill Level: Beginner to Intermediate
  • Detection Difficulty: Medium

Attack Tree Path: 1.1.5. Improper Error Handling Leading to Unreleased Resources [CRITICAL]

  • Description: If errors within async callbacks are not properly handled, resources (e.g., file handles, database connections, network sockets) may not be released. Repeated errors can lead to resource exhaustion and eventual application failure. This is not specific to async but is exacerbated by its asynchronous nature.
  • Exploit: This is not directly exploitable in the same way as input-based vulnerabilities. It's a consequence of existing programming errors. However, an attacker might be able to trigger error conditions more frequently to accelerate resource exhaustion.
  • Mitigation:
    • Implement robust error handling in all async callbacks.
    • Use try...catch...finally blocks to ensure that resources are always released, even if an error occurs.
    • Use async functions that automatically handle promise rejections (e.g., async.eachOf with an async iterator function).
  • Example: A database query within an async.each callback fails. If the database connection is not closed in a finally block, repeated failures will exhaust the connection pool.
  • Likelihood: Medium
  • Impact: Medium to High
  • Effort: Very Low
  • Skill Level: Beginner
  • Detection Difficulty: Hard

Attack Tree Path: 1.2.1. Synchronous Operations within Async Callbacks [HIGH RISK][CRITICAL]

  • Description: Performing long-running synchronous operations (e.g., heavy computation, synchronous file I/O) within an async callback blocks the Node.js event loop. This prevents other tasks from being processed and makes the application unresponsive, leading to a Denial of Service.
  • Exploit: This is not directly exploitable by external input in the same way as resource exhaustion. However, if the synchronous operation is influenced by user input (e.g., a complex regular expression match on user-supplied data), an attacker could provide input that causes the synchronous operation to take a very long time.
  • Mitigation:
    • Avoid synchronous operations within async callbacks.
    • Use asynchronous alternatives for I/O operations (e.g., fs.readFile instead of fs.readFileSync).
    • Offload heavy computation to worker threads to prevent blocking the main event loop.
  • Example: Calculating a cryptographic hash of a large user-uploaded file synchronously within an async.map callback.
  • Likelihood: Medium
  • Impact: Medium to High
  • Effort: Very Low
  • Skill Level: Beginner
  • Detection Difficulty: Medium

Attack Tree Path: 2.1. Callback Injection via Unvalidated Input [CRITICAL]

  • Description: If the application constructs async callbacks dynamically based on user input without proper sanitization, an attacker could inject malicious JavaScript code. This is highly unlikely with the standard use of async functions but could occur if the application uses eval or similar mechanisms to create functions from strings.
  • Exploit: The attacker provides input that, when incorporated into the dynamically generated callback, executes arbitrary JavaScript code.
  • Mitigation:
    • Never construct functions from unsanitized user input. This is a fundamental security principle.
    • Use parameterized queries or other safe methods for dynamic behavior.
    • Strictly validate and sanitize any input that is used to influence control flow.
  • Example: async.series([eval(userInput)]) - This is an extremely dangerous and contrived example, illustrating the vulnerability.
  • Likelihood: Very Low
  • Impact: Very High
  • Effort: Medium
  • Skill Level: Intermediate to Advanced
  • Detection Difficulty: Hard

Attack Tree Path: 2.3 Dependency Confusion/Supply Chain Attack [CRITICAL]

  • Description: An attacker publishes a malicious package to a public package registry (like npm) with a name similar to a legitimate dependency used by async (or a dependency of a dependency). If the application is misconfigured or uses a vulnerable version of a package manager, it might install the malicious package instead of the legitimate one.
  • Exploit: The attacker relies on typos or misconfigurations in the application's dependency management to trick it into installing the malicious package.
  • Mitigation: * Use package-lock.json or yarn.lock to ensure consistent dependency resolution and prevent unexpected package installations. * Verify the integrity of installed packages using checksums. * Regularly audit dependencies for known vulnerabilities. * Consider using a private package registry to control which packages can be installed.
    • Example: If async depended on a package called "helper-utils," an attacker might publish a malicious package called "helper_utils" (note the underscore) to npm.
  • Likelihood: Low
  • Impact: Very High
  • Effort: Medium to High
  • Skill Level: Intermediate to Advanced
  • Detection Difficulty: Medium to Hard