APIs have become the backbone of modern digital ecosystems, powering everything from mobile apps to e-commerce platforms. However, as APIs grow in importance, they also become prime targets for malicious actors. Increasingly, bots are being weaponized to exploit vulnerabilities, overwhelm systems, and siphon sensitive data—all without triggering alarms until it’s too late.
The rise in bot-driven API abuse has far-reaching implications, from financial losses and operational disruptions to reputational damage. Understanding the tactics behind these attacks is critical for organizations seeking to stay ahead of evolving threats.
In this blog post, we’ll explore lessons learned from real-world incidents of API abuse, highlighting key attack methods, effective strategies for mitigating risk, and how Wallarm’s API Abuse Prevention solution can help. By learning from these examples, your organization can strengthen its defenses and protect its digital assets against bot-driven API threats.
How Has Bot-Driven API Abuse Evolved in Recent Years?
Account takeover and scraping are the two most common types of API abuse and have evolved significantly in the past few years. Threat actors are improving the efficacy of these techniques using machine learning (ML) algorithms so their attacks can bypass traditional pattern-based defenses.
Account Takeover
As the name suggests, account takeover attacks involve attackers attempting to gain unauthorized access to someone else’s account without their permission or knowledge. Once in the account, threat actors typically abuse their access to steal sensitive information, conduct fraudulent transactions, or spread spam or malware.
To execute these attacks, cybercriminals often use bots to perform credential stuffing attacks, which test large volumes of username-password pairs, usually obtained from data breaches, against an API’s login or authentication endpoints.
In recent years, account takeover attacks have become increasingly adaptive and more challenging to detect. For example, Wallarm often observes attacks that start quite aggressively but when blocked, alter their behavior by reducing requests per second (RPS) and changing IP addresses in an attempt to fly under the radar. To make matters worse, ML algorithms allow bots to learn from past attempts, adjust their strategies in real-time, and bypass traditional defenses.
Scraping
In the API world, scraping refers to the systematic extraction of large amounts of data from APIs through automated means such as AI-enabled bots. This practice has evolved from traditional web scraping, which involved mimicking human behavior to collect information from websites.
API scraping has become increasingly prevalent due to several key factors:
- Ease of interaction: APIs are designed for programmatic access, eliminating the need to emulate human browsing behavior.
- Efficiency: Attackers can request large volumes of data at once, rather than navigating through web pages sequentially.
- Speed: The direct nature of API interactions significantly accelerates the data extraction process.
- Structured data: APIs typically return data in structured formats like JSON or XML, making it easier to parse and process.
The shift towards API scraping has important implications for businesses. The ease and speed of API scraping make it a more attractive target for malicious actors. Attackers can potentially steal vast amounts of data in a short time, posing significant risks to organizations.
Which are the Often-Overlooked Aspects of API Security?
In light of these evolving attack techniques, paying close attention to certain aspects of API security is especially important. They include:
- Broken Object Level Authorization (BOLA): APIs that do not adequately enforce user permissions for object access, allowing unauthorized data retrieval.
- Lack of Protection Against Automated Threats: APIs without bot detection mechanisms are vulnerable to automated attacks such as scraping, credential stuffing, and vulnerability scanners.
- Business Logic Vulnerabilities: Attackers exploit flaws in business logic to manipulate API behavior in unintended ways.
- Improper Asset Management: Unofficial or undocumented APIs created without the security team’s knowledge and deprecated APIs that are no longer maintained but still accessible.
- Lack of Proper Rate Limiting: APIs without strict rate limits are vulnerable to abuse through brute-force attacks and resource exhaustion.
- Improper Error Handling and Information Leakage: Overly detailed error messages can reveal sensitive information like server configurations, API keys, or user data.
- Insufficient Logging and Monitoring: Suspicious activities go unnoticed without proper logging, delaying incident response.
If you have any of these API security issues, you’re especially vulnerable to API abuse in the modern threat landscape.
What Role Does AI Play in Bot-Driven API Abuse Prevention?
Although AI and ML technologies have supercharged bot-driven API abuse’s scale, sophistication, and success rates, they are also helpful for API abuse prevention. They offer the following capabilities that help identify and prevent bot-driven API abuse attacks:
- Behavioral Analysis: Machine learning models analyze vast amounts of data to detect and block unusual user activities that may indicate attacks or API abuse.
- Real-Time Threat Detection: ML algorithms help to analyze traffic in real-time to identify malicious activities. For instance, sudden spikes in failed login attempts may indicate a credential-stuffing attack.
- Intelligent Rate Limiting: Machine learning models adjust rate limits dynamically based on user behavior and threat intelligence. They can differentiate between legitimate high-volume users and potential attackers.
As part of a broader API security solution, these capabilities can help protect against API abuse.
How Can Organizations Fend Off Bot-Driven API Abuse?
Protecting against bot-driven API abuse is no mean feat. It requires a team of security experts that can organize secure development and API protection processes that include:
- Strong Authentication and Authorization
- Secure Communication Channels
- Input Validation and Output Encoding
- Rate Limiting and Throttling
- Monitoring and Logging
- Regular Security Testing and Audits
- Advanced Threat Protection
- Secure Development Lifecycle (SDLC)
- Limiting Data Exposure
Implementing these processes can be laborious, expensive, and often beyond the capabilities of overstretched in-house security teams. Fortunately, there’s a better way to protect against bot-driven API abuse.
Wallarm’s Integrated API Security solution offers the majority of the above features, providing visibility, reconfigurability, and management capabilities to protect against even the most sophisticated bot-driven API abuse attacks.
How Does Wallarm Prevent API Abuse in the Real World?
To put our solution into context, let’s look at a scraping incident Wallarm prevented recently:
A client recently faced a scraping attack that took advantage of a weak numerical ID in one of their APIs. This made it easy for attackers to guess and access data using bots to go through the IDs in order. Ideally, the client could have fixed this by replacing the weak ID with a stronger one, like a random string (UUID), but technical limitations made that impossible.
Instead, they used Wallarm’s API Abuse Prevention solution, which stopped the scraping bots by spotting suspicious patterns in the traffic. This included detecting repeated attempts to guess IDs and bots crawling through the API. Wallarm’s solution protected the API without significant changes, showing how flexible defenses can solve deeply ingrained vulnerabilities. Still not convinced? Take a product tour today for a comprehensive look at our API Abuse Prevention solution.