The Hidden Async Await Traps in C# That Even Experienced Developers Miss

December 11, 2025 · Asad Ali

Async can make or break a system. I have seen beautifully designed architectures fall apart in production because a single async misuse silently caused deadlocks, thread starvation, or cascading latency. After building and operating distributed systems for years, these are the async traps that consistently hurt experienced teams the most.

When async awaits behave differently than you think

Async code looks simple and elegant. But behind the syntax lies a scheduling model that can surprise you under concurrency. Most traps do not appear in unit tests or local environments. They show up only when your system is under real load.

TLDR: Async is not syntax sugar. It is a concurrency contract. Misunderstand it and your system will eventually fail under pressure.

1 Mixing async with blocking calls

This is the number one cause of deadlocks I have debugged over the years. Mixing .Result or .Wait() with async code blocks threads and disrupts the task scheduler.

// dangerous under ASP.NET Core
var user = _repository.GetUserAsync(id).Result;

In server environments, this can quickly lead to thread starvation. ASP.NET Core uses a thread pool; when threads get stuck waiting synchronously, incoming requests slow down or time out.

Warning: Every .Result or .Wait() in your codebase is either a production bug or a future incident waiting to happen.

Case study snapshot

In one of my past projects, a single .Result inside a retry loop caused the API to freeze during peak traffic. The team thought the database was down. In reality the thread pool was starved. Removing the blocking call restored throughput instantly.

2 Forgetting to pass CancellationToken through the stack

Cancellation tokens are not optional. They are a requirement in modern .NET services. Without them, long-running async operations keep consuming CPU, memory, and I/O even after a client disconnects.

public async Task ProcessAsync()
{
    var result = await _httpClient.GetAsync(url); // no cancellation support
}

You want cancellation propagation like this:

public async Task ProcessAsync(CancellationToken token)
{
    var result = await _httpClient.GetAsync(url, token);
    await _repository.SaveAsync(result, token);
}
Best practice: Every async API you design should accept a CancellationToken unless you have an extremely good reason not to.

3 Async void destroying error visibility

async void is a silent killer. It swallows exceptions and prevents callers from knowing when an operation fails.

public async void SendEmail() // do not do this
{
    await _service.DispatchAsync();
}

This breaks monitoring and incident detection. Any failure inside async void disappears unless handled manually.

Anti-pattern: Using async void outside UI event handlers. It hides failures and corrupts diagnostic pipelines.

Micro punchline

Async void is the closest thing to a black hole in C Sharp.

4 Async inside LINQ queries

LINQ does not understand async. Developers often try:

var users = ids.Select(async id => await _repo.GetUserAsync(id));

This creates an IEnumerable rather than processing users. Developers expect sequential or parallel execution but get neither. The behavior depends entirely on how the caller handles the task sequence.

The correct pattern:

var tasks = ids.Select(id => _repo.GetUserAsync(id));
var users = await Task.WhenAll(tasks);
Warning: Async inside LINQ is easy to misuse. Make the asynchronous boundary explicit and visible.

5 Fire and forget without supervision

Fire-and-forget is not inherently bad. But in distributed systems it must be supervised. Launching tasks without tracking or observing them leads to unpredictable failures.

_ = Task.Run(async () =>
{
    await _emailService.SendAsync(orderId);
});

If the email service throws, the task dies quietly. No logs. No traces. No retries.

Architecture insight: Fire and forget requires: structured logging, telemetry, retries, and often a background worker, not a Task.Run.

Mini checklist for safe async code

  • ✔ Avoid .Result and .Wait()
  • ✔ Pass CancellationToken everywhere
  • ✔ Never use async void for server code
  • ✔ Do not hide async inside LINQ
  • ✔ Structure fire-and-forget with supervision

Debugging insight

Debugging insight: If you see high CPU and low throughput under load, suspect thread pool starvation caused by blocking async.

Final thoughts

Async is powerful but unforgiving. Small mistakes compound quickly at scale. The traps I listed here are subtle, common, and extremely expensive during production incidents. The good news is that once you internalize these patterns, async becomes predictable and even enjoyable to work with.

Your goal is simple: write async code that behaves the same under load as it does in your unit tests. That requires discipline, understanding, and a willingness to avoid shortcuts. These habits will make your services more resilient and your incident timelines much shorter.