Race condition 101-Everything you need to know about race condition attacks

10 min readSep 26


What is a Race Condition?

In the world of web applications, speed is crucial. Developers want websites to be lightning-fast. But there’s a hidden problem called a “race condition.” It’s like a digital race where things can go wrong.

Imagine two people wanting to update a shared document at the same time. If the computer doesn’t manage this well, their changes might mix up or get lost. That’s a race condition.

It happens when many things try to do something at once, and the timing matters. Think of it like a relay race, where the order the runners finish in decides the winner.

In this blog, we’ll learn more about race conditions, see real examples, and find out how to stop them from causing problems in our web apps.

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Race condition examples

Photo by Mika Baumeister on Unsplash

Example 1: Bank Account Balance

Imagine you have a bank account with a balance of $100, and you want to withdraw $50. However, two transactions happen simultaneously:

Transaction A: You start with $100 and want to withdraw $50, leaving you with $50.
Transaction B: At the same time, you decide to transfer $30 to a friend, leaving you with $70.
Now, let’s see what happens if there’s a race condition:

Transaction A checks your balance, which is $100.
Transaction B also checks your balance, which is still $100 because Transaction A hasn’t finished yet.
Transaction A subtracts $50, leaving you with $50.
Transaction B subtracts $30, leaving you with $70, even though you expected $50.
In this case, the race condition caused you to have more money than you should, which is a problem!

Photo by Brooke Lark on Unsplash

Example 2: Online Ticket Booking

Imagine you’re booking concert tickets online. There are only two tickets left, and both you and your friend are trying to book them at the same time:

Your Action: You select two tickets and click “Buy.”
Friend’s Action: Simultaneously, your friend also selects two tickets and clicks “Buy.”
Now, let’s see what happens with a race condition:

The system checks that there are two tickets left.
Both you and your friend’s actions pass this check because it happened at the same time.
The system proceeds to book both sets of tickets.
In this case, you and your friend both ended up with tickets even though there were only two available. This is another example of a race condition causing unexpected results.

These examples illustrate how race conditions can lead to unexpected outcomes when multiple actions compete for the same resources or data simultaneously, and the timing of these actions matters. In web applications, proper synchronization and handling of such scenarios are essential to prevent these issues.

Major types of Race conditions

  1. Limit Overrun Race Conditions
  2. Multi-Endpoint Race Conditions
  3. Time-of-Check to Time-of-Use (TOCTOU) Race Conditions
  4. Partial Construction Race Conditions
  5. Session-Based Locking Mechanisms
  6. Single-Endpoint Race Conditions
  7. Time-Sensitive Attacks
  8. Aligning Multi-Endpoint Race Windows

Limit Overrun Race Conditions: An Easy Explanation

A “Limit Overrun” race condition occurs when a program or process exceeds a predefined limit because it doesn’t check the limit properly when multiple actions happen simultaneously. Let’s break it down with a simple example.

Example: Parking Garage

Imagine a parking garage with a maximum capacity of 100 cars. To manage the number of cars, there’s a digital display at the entrance showing the current count of parked cars.

  1. Driver A: Driver A arrives at the garage and sees that there are 99 cars parked (one spot left). They enter the garage.
  2. Driver B: At the same moment, Driver B also arrives and sees the same display: 99 cars parked (one spot left). They enter the garage.

Here’s where the race condition occurs:

  1. Inside the Garage: Driver A’s car successfully enters, and the display now shows 100 cars parked.
  2. Simultaneously Inside the Garage: Driver B’s car also enters, but because it didn’t check the limit again, the display still shows 100 cars parked.

As a result, the garage now has 101 cars parked, which exceeds its maximum capacity. This happened because both drivers relied on the initial count and didn’t recheck it before entering.

In this scenario, the limit (100 cars) was overrun because the system didn’t properly account for concurrent actions. This is a simple illustration of a limit overrun race condition, where multiple actions occur simultaneously, and the limit is exceeded due to inadequate checks, potentially leading to issues or errors in a program or system.

Detecting and potentially exploiting limit overrun race conditions involves identifying scenarios where system limits or constraints are not properly enforced, leading to unintended consequences. Here’s a step-by-step guide, along with an easy example:

Step 1: Identify the Limit or Constraint

First, you need to find a limit or constraint in the system that, if exceeded, could cause problems. This could be a rate limit, a maximum quantity, or any other restriction that, if ignored, might lead to unexpected behavior.

Example: Consider a website that has a rate limit of 5 login attempts per minute for each user.

Step 2: Probe the System

Next, you need to test the system to see if it properly enforces the identified limit or constraint. Try to push the system to its limits while monitoring the system’s behavior.

Example: Attempt to log in with the same user account more than 5 times within a minute.

Step 3: Observe the System’s Response

Pay close attention to how the system responds when you exceed the limit. Does it deny access, slow down, or display any error messages? These responses can provide valuable information about whether a limit overrun race condition exists.

Example: If the system allows more than 5 login attempts within a minute for the same user, it might display an error message saying “Too many login attempts” or slow down response times.

Step 4: Analyze the Timing

To potentially exploit the limit overrun, you’ll need to understand the timing and rate at which the system checks and enforces the limit. This is critical for determining how quickly you can make subsequent attempts without triggering the limit.

Example: If the system checks the login attempts every 10 seconds, you have a window of opportunity to perform multiple login attempts within that timeframe.

Step 5: Exploit the Timing

Now that you understand the timing, you can exploit it by making additional attempts within the window of opportunity without triggering the limit. This might involve automating the process or coordinating with multiple users.

Example: If the system checks every 10 seconds, you can automate login attempts at the maximum rate of 5 per minute within the 10-second window without exceeding the limit.

Step 6: Document and Report

It’s crucial to act ethically and responsibly. If you discover a limit overrun race condition, document your findings and report them to the system’s administrators or developers. Exploiting such vulnerabilities without authorization is unethical and illegal.

Example: Notify the website’s administrators about the issue and provide details on how you identified it.

By following these steps, you can detect and potentially exploit limit overrun race conditions for ethical purposes, with the ultimate goal of helping to improve the security and robustness of the system.

Hidden Multi-Step Sequences of Race Conditions: Easy-to-Understand Examples

Race conditions can be even trickier when they involve multi-step sequences, where multiple actions must happen in a specific order to avoid problems. Let’s explore this concept with simple, real-life examples:

Example 1: Online Shopping Cart Checkout

Imagine you’re shopping online, and you have items in your shopping cart that you want to buy. The checkout process involves multiple steps: adding your shipping address, selecting a payment method, and confirming the order.

  1. Step 1: Address Entry: You start entering your shipping address.
  2. Step 2: Payment Selection: Simultaneously, you click to select your payment method.

Now, here’s where a hidden multi-step race condition can occur:

  1. Step 3a: Address Confirmation: The system checks your address, and it’s valid.
  2. Step 3b: Payment Confirmation: Simultaneously, the system verifies your payment method, and it’s valid.

Now, if there’s a race condition:

  1. Step 4a: Order Confirmation: The system confirms your order and deducts the payment.
  2. Step 4b: Payment Deduction Delayed: Simultaneously, the payment system deducts the payment after your order is confirmed.

In this case, a hidden multi-step race condition can result in the deduction of payment before order confirmation, which could lead to issues like double charging your account or sending you the wrong items because the payment was processed prematurely.

Example 2: Seat Reservation in a Cinema

Imagine you’re booking a seat in a cinema for a popular movie. The cinema’s website allows you to select a seat, enter your payment details, and then confirm the booking.

  1. Seat Selection: You select a seat you want to reserve.
  2. Payment Entry: Simultaneously, you enter your payment details.

Here’s where a hidden multi-step race condition can occur:

  1. Payment Authorization: The system authorizes your payment, which is successful.
  2. Seat Reservation: Simultaneously, the system tries to reserve the seat you selected.

If there’s a race condition:

  1. Seat Reservation Fails: The system attempts to reserve the seat after payment authorization, but someone else reserved it a fraction of a second earlier because the seat selection and payment entry happened simultaneously.

In this case, a hidden multi-step race condition can result in the website confirming payment for a seat that couldn’t be reserved, causing frustration and confusion for customers.

These examples illustrate how multi-step sequences in web applications can become vulnerable to race conditions when steps must occur in a specific order, and multiple users interact with the system simultaneously. Proper synchronization and validation are essential to prevent such hidden race conditions and ensure a smooth user experience.

To detect and potentially exploit hidden multi-step sequences that are susceptible to race conditions, you can follow a methodology that involves three key steps: Predict potential collisions, Probe for clues, and Prove the concept. Let’s break down each step:

1. Predict Potential Collisions:

Objective: Identify where race conditions might occur in multi-step sequences.

In this step, your goal is to predict scenarios in the application where multiple actions or steps could collide due to concurrency issues.

Example: In the cinema seat reservation example mentioned earlier, you might predict a potential collision at the point where the system checks seat availability and authorizes payment simultaneously. This is because multiple users may attempt to book the same seat at the same time.

2. Probe for Clues:

Objective: Gather evidence that race conditions may exist.

Once you’ve identified potential collision points, you need to gather evidence or clues that suggest race conditions might indeed be present.

Example: You could use automated testing tools or manual testing to simulate concurrent actions at the identified collision point. If you notice inconsistent results, like double bookings or unexpected errors, these are clues that race conditions might be at play.

3. Prove the Concept:

Objective: Confirm the existence of race conditions and understand their impact.

In this final step, you aim to prove that race conditions exist and gain a deeper understanding of their consequences.

Example: Suppose you have evidence of potential race conditions in the cinema seat reservation system. To prove the concept, you could conduct controlled experiments where multiple users simultaneously try to book the same seat. If you consistently observe issues such as double bookings or incorrect seat assignments, you’ve confirmed the existence of race conditions.

Exploitation Note: While detecting and understanding race conditions is essential for security testing, it’s important to highlight that exploiting race conditions in a real-world scenario without proper authorization is unethical and illegal. The objective should be to report and help fix these vulnerabilities rather than exploit them for personal gain.

By following this methodology of predicting potential collisions, probing for clues, and proving the concept, you can systematically identify, understand, and help mitigate race conditions in multi-step sequences within web applications, contributing to improved security and user experience.

Now I will give an overview of major race conditions

1. Multi-Endpoint Race Conditions:

  • Definition: Multi-endpoint race conditions occur when different parts (endpoints) of a system compete for shared resources or actions.
  • Example: In a ride-sharing app, one endpoint may handle booking a ride, while another handles checking for available drivers. If both endpoints don’t coordinate properly, a race condition could arise. For instance, two users might simultaneously book the same driver, creating confusion.

2. Aligning Multi-Endpoint Race Windows:

  • Definition: This involves coordinating the timing of different endpoints to avoid race conditions.
  • Example: In a messaging app, when you send a message and the other person deletes the conversation simultaneously, proper alignment ensures the message isn’t mistakenly sent or displayed as sent when it’s not.

3. Connection Warming:

  • Definition: Connection warming is a technique to establish and maintain connections to a server in advance to reduce latency.
  • Example: Online multiplayer games warm-up connections to game servers before the match starts. This reduces lag since players don’t have to wait for new connections to be established during gameplay.

4. Abusing Rate or Resource Limits:

  • Definition: It involves exploiting system limits (e.g., API rate limits) to disrupt or overload a service.
  • Example: If an API allows 100 requests per minute, an attacker might send 101 requests within a second to exhaust the limit, causing a denial of service.

5. Single-Endpoint Race Conditions:

  • Definition: In this case, a single component or endpoint competes with itself for shared resources.
  • Example: In an online ticket reservation system, if one process doesn’t correctly lock a seat it’s trying to reserve, it might mistakenly allow multiple users to book the same seat simultaneously.

6. Session-Based Locking Mechanisms:

  • Definition: Using session-based locking, only one user can access a resource at a time during their session.
  • Example: In an online document editing tool, if User A is editing a document, User B must wait until User A finishes, ensuring no simultaneous edits occur.

7. Partial Construction Race Conditions:

  • Definition: These occur when an object or resource is partially created or updated due to a race condition.
  • Example: In a banking app, if a race condition interrupts a transfer operation, it could lead to the money being deducted from one account but not credited to the other.

8. Time-Sensitive Attacks:

  • Definition: Attack techniques that depend on precise timing to exploit vulnerabilities.
  • Example: In cryptographic attacks, hackers might attempt to measure tiny variations in response times to reveal encryption keys.

To prevent race conditions:

  1. Use synchronization mechanisms.
  2. Implement proper locking.
  3. Employ atomic operations.
  4. Utilize transactions.
  5. Leverage immutable data structures.
  6. Employ queue-based systems.
  7. Apply rate limiting.
  8. Conduct thorough testing.
  9. Train developers on best practices.

In conclusion, race conditions are subtle yet potentially harmful vulnerabilities in concurrent software. Preventing them requires careful synchronization, locking, and adherence to best practices. Prioritizing race condition prevention is crucial to ensuring software reliability and data integrity.




Recent EEE graduate passionate about cybersecurity. Top 3% in Try Hack Me. I write about cyber security and technology.