Back-end servers power modern applications, requiring significant expertise to build. However, securing these servers against threats like hackers and phishers is crucial.
Malicious actors exploit vulnerable entry points in back-end servers to cause damage, steal sensitive information, and degrade application performance through various API attacks, including SQL and NoSQL injections, Distributed Denial-of-Service (DDoS) attacks, malware, and other exploitative methods.
This article will explore rate limiting, a critical technique that safeguards back-end APIs against threats like DDoS, brute-force attacks, and other malicious activities. But first, let’s understand what rate limiting actually means.
What is Rate Limiting?
Rate limiting is a mechanism designed to control the number of requests a client can make to a back-end server within a specified time period, effectively preventing excessive or repeated requests.
But why is it crucial to implement rate limiting during API development? We’ll explore the reasons in the next section.
Importance of Rate Limiting
Here are some key reasons why rate limiting is essential in back-end application development:
DDoS Attacks
Firstly, rate limiting acts as a crucial defense against DDoS attacks, which involve overwhelming server endpoints with massive volumes of requests, often through automated bots, leading to server inefficiency and disruptions in service.
DDoS attacks can be categorised into volumetric, protocol-based, or application layer attacks. A notable instance of such an attack occurred on GitHub in 2018.
Web Scraping
Rate limiting also helps safeguard web applications and servers from unauthorised web scrapers and crawlers. These automated tools continuously send requests to collect website data, potentially exposing sensitive information. Implementing a robust rate limiter prevents such unauthorised access and data scraping.
Brute Force Attack
This involves attempting to gain access to a server’s resources by trying all possible configurations, a process that can be done manually but is typically automated with bots due to its resource-intensive nature. Rate limiting effectively mitigates these attacks by blocking requests that exceed a set number within a specified time frame.
Resource Optimization
Server requests incur costs for API owners related to running and maintenance. Implementing a rate limiter helps manage the volume of requests, thereby conserving costs and improving efficiency. Next, we will discuss some of the algorithms used to build rate limiters.
Adoption and Usage of Rate-Limiting by Popular Sites
Rate limiting is widely adopted across tech products, from large-scale to small-scale applications. For instance, Twitter (now X) incorporates a rate limit feature in its APIs, which offer access to various functionalities, including the sign-up extension.
To ensure the efficient operation of these interfaces, Twitter enforces a rate limit of 50 tweet post requests per user every 24 hours.
Other Real life Use-cases of API Rate Limiting
The use of application programming interfaces (APIs) extends beyond popular sites like Twitter. Here are some other real-world applications of rate limiting:
Reducing the Incidence of Spamming
Research shows that over 160 billion spam emails are sent daily, prompting messaging and email platforms to implement rate limiting to reduce the spread of unsolicited messages and spam content. This approach helps maintain platform integrity and promotes responsible usage within a specified time range.
Tackling Fraudulent Activities
Rate limiting is widely used in web applications to detect unusual activities that may indicate fraudulent behaviour by some users. This measure helps prevent and mitigate ongoing fraudulent transactions, protecting the application server from potential abuse.
Disabling Malicious User Authentication
Malicious individuals often attempt to compromise web servers through methods like brute force, DDoS attacks, and other techniques to gain unauthorised access to user accounts.
However, many sites have implemented robust rate limiting systems that restrict the number of login attempts within a set time frame, enhancing overall web security.
How Does Rate Limiting Work?
Rate-limiting tools in applications are built on various algorithm structures that dictate their functionality. These algorithms help control the number of requests a server can handle at any given time, ultimately enhancing the server’s performance and efficiency.
Examples of Rate Limiting Algorithms
Here are some of the most commonly used rate-limiting algorithms:
Fixed Window Algorithm
This algorithm works by setting a fixed time interval during which all clients are allowed a specific number of requests to the server. It regulates the request rate uniformly, regardless of how many clients are accessing the API simultaneously.
For example, if a five-minute request limit is set, no client can access the endpoint until the five-minute window expires. While simple, this model is not cost-effective due to its strict limitations on access.
Sliding Window Algorithm
This algorithm is similar to the fixed window algorithm but addresses its limitations by allowing individualised access for each client. It assigns independent time intervals to each client, regulating their requests within the specified time frame, making it more flexible and efficient.
For example, if a Client makes a request at 10:00, they can make up to 10 requests until their window expires at 10:03. Meanwhile, Client B, who starts at 10:02, is allowed 10 requests until their window expires at 10:05. This approach ensures each client has a separate time interval for requests.
Leaking Bucket Algorithm
This algorithm, inspired by the concept of a “leaking bucket,” ensures that the server processes a limited number of requests at any given time. Requests exceeding this limit are discarded and typically return an “error 429” response. This approach helps prevent server overload, maintaining efficiency and speed.
Token Bucket Algorithm
This model is similar to the leaking bucket algorithm, featuring a hypothetical bucket that manages tokens. Tokens are added to the bucket at regular intervals, and each incoming request removes a token.
Once the tokens are depleted, further requests are discarded, returning an “error 429.” This mechanism helps prevent server congestion and maintains optimal efficiency.
Rate Limiting Best Practices
Efficient web API development relies on adhering to best practices. To fully leverage rate limiting as an API security measure, consider implementing the following strategies:
- Firstly, select a compatible rate-limiting algorithm. Implementing a robust rate-limiting algorithm is crucial to achieving optimal results. Ensure that the chosen algorithm aligns well with your API endpoint requirements to maximise effectiveness.
- Set rate limits within reasonable ranges. Arbitrary rate limit parameters can harm user experience, defeating the purpose of rate limiting. Establishing balanced time limits helps enhance user satisfaction while effectively mitigating attacks.
- Implement efficient error handling and provide clear feedback to the client. The standard error code for rate limiting is 429, indicating too many requests. Properly managing these errors, especially those resulting from API misuse, is essential to guide users and maintain a smooth experience.
- Implement flexible rate-limiting mechanisms tailored to different parameters. Applying a fixed time interval uniformly across all endpoints is often ineffective, as some API endpoints are more data-sensitive than others. A flexible rate limiter that adjusts parameters based on endpoint relevance enhances server efficiency and strengthens security.
- Ensure the implementation of appropriate application logging, monitoring, and observability tools. Utilising API metrics, logs, and monitoring systems adds an extra layer of security for web APIs by tracking server activities. Monitoring alerts can notify developers of suspicious requests, enabling quick responses to potential threats.
- Ensure synchronisation between rate limiting and other API security measures. Properly aligning rate limiters with existing security protocols enhances overall API protection. It’s important to have in-depth knowledge and expertise to integrate these measures effectively, avoiding conflicts that could undermine security efforts.
- Ensure comprehensive API documentation. Clear and detailed documentation informs users, developers, and clients about the rate-limiting policies in place, promoting compliance and helping them understand how to interact with the API within its limits.
Conclusion
In conclusion, we have highlighted rate limiting as an important API security hack and some of its real life use cases.
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