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Detect API Abuse

Picture of Apurva Prakash
Apurva Prakash
Marketing Manager @ AppSentinels
  • • ⏱︎8mins Read

Prioritized automation of API ecosystem

Just about every application uses an application programming interface (API). While APIs add a lot of value to an organization, they come with some significant problems from a security standpoint. In fact, Gartner predicts that API abuses will be the most common threat vector from 2022, bypassing all other threat vectors.

So, what problems exactly do APIs face? And what can security defenders do about it?

What is an API Abuse?

API abuse refers to the mishandling of APIs for malicious purposes. With the requisite skills, cybercriminals can reverse engineer applications to modify their flow, which can result in hackers getting sanctioned access to the application.

Cybercriminals can use APIs to access undesirable segments of applications, unauthorized access to data belonging to other users, execute account takeovers, scrape business-critical data, perform distributed denial of service (DDoS) attacks, etc.

Understanding API attacks is key to preventing, detecting, and neutralizing them, and this article intends to help with that.

Types of API Abuse

Unwanted Application Business logic

It is possible to modify an application’s intrinsic nature using breached APIs. Breached API calls will reassemble a normal API call in every respect but will cause the application’s business logic to be tweaked to carry out unintended/unauthorized actions. Examples can be changing the PIN of debit cards or transferring money across accounts without user authorization.

The Coinbase attack in Feb’22 is a great example of such an attack. You can read more about it here: https://blog.coinbase.com/retrospective-recent-coinbase-bug-bounty-award-9f127e04f060

Taking over Accounts

Botnets are deployed to initiate account takeovers. To test stolen users and passwords, a botnet invokes APIs to check the combinations. While API management systems reject invalid login attempts, they aren’t very effective at combating the volume of bots. These bots, emerging from different IPs, repeatedly check for credential combinations with the intention of brute-forcing their way to a valid login.

Specifically, skilled hackers ensure that bots trigger API requests at realistic intervals, resembling human requests, and have a better chance of bypassing conventional security protocols.

Content Scraping

Hackers can deploy APIs to scrape content from an application. It is common for competitors or other cyber criminals to do so to gain a competitive advantage, especially during major sales periods. For example, by scrapping business-critical prices of a famous item, competitors can price their products to damage business prospects.

Distributed Denial of Service (DDoS) Attacks

Hackers can target compute-intensive APIs to intentionally overload application servers. This act, coupled with traffic from numerous IPs and devices, can occupy system resources and prolong server response times for legitimate users.

How to Detect API Abuse

Consider a platform that provides value across the entire API lifecycle, from development, testing, deployment, and retirement

API visibility in real-time: If you can’t see, you can’t protect it! API visibility is an essential pillar of detecting API abuse. Consider a security platform that provides real-time continuous discovery of all APIs in your tech stack. It should provide details on parameters, such as whether a parameter is mandatory, optional, or PII/sensitive. Additionally, the platform should discover API attributes and not just API endpoints. The visibility should also include baselining of API usage to detect whether APIs are encountering anomalous behavior.

Risk Assessment: The platform should evaluate the risk associated with each API relative to its exposure, likelihood of an attack, and consequent impact. This helps security and engineering teams prioritize response and improves efficiency in addressing security breaches.

Deep Learning of API behavior: Look for a platform to perform deep learning of application behavior to understand the application baseline and context. As API breaches are complex business logic exploits, without building a strong understanding of the application workflows and context, the platform won’t be able to detect API abuse.

Shift-Left API Testing: The platform should shift left to identify weaknesses in the application and insert them into the organization’s CI/CD cycle to find potential vulnerabilities earlier in the cycle. As APIs are stateless but the workflows are stateful, the testing platform should be able to mimic all happy path scenarios to provide extensive coverage. AppSentinels does exactly this via the industry’s first Intelligent Stateful API DAST.

Comprehensive view of attacks: Can your API security solution connect security-related events and map them to the source of the attack (users or groups)? AppSentinels accomplishes this through the application, user, device, and traffic fingerprinting.

By connecting activities from the same users across multiple IPs, AppSentinels seeks to offer SecOps teams perfect clarity on an attack, its current stage, and the methods used for the invasion. The correlation also allows us to discern between attacks and legitimate user behavior, which is necessary to avoid false positives.

Frequently Asked Questions

What is API abuse, and how does it differ from a traditional API security vulnerability exploitation?+–

API abuse involves the misuse of APIs for malicious purposes by using them in ways technically allowed by their design but contrary to intended use. Unlike vulnerability exploitation (which involves breaking security controls), API abuse exploits the legitimate functionality itself. Cybercriminals can reverse engineer applications to modify their flow, gaining access to unintended segments. Examples include using a valid authentication API for credential stuffing, using a valid data retrieval API for scraping, or using valid account functions to execute account takeovers. The challenge is that abuse looks identical to legitimate usage from a technical standpoint and detecting it requires behavioral context, not just request validity.

How do attackers use APIs specifically for account takeover (ATO) attacks, and what signals reveal these attempts?+–

Account takeover via API typically involves credential stuffing and testing stolen credential lists against authentication APIs at high speed — or social engineering follow-through using information harvested from data APIs. ATO detection signals include: abnormally high authentication failure rates from specific IP clusters or geographic regions, successful authentications immediately followed by high-value account actions (password changes, linked email modifications), multiple accounts accessed from the same IP or device fingerprint in rapid succession, and authentication pattern deviations from a user’s established geographic or timing baseline. These behavioral signals require cross-session correlation that simple per-request analysis cannot provide.

What makes automated API abuse detection necessary when human-based monitoring has been the traditional approach?+–

Human-based monitoring cannot handle the volume, speed, or subtlety of modern API abuse. A single credential stuffing campaign may generate millions of authentication requests within hours, distributed across thousands of IP addresses in patterns that require correlation across enormous data volumes to detect. Human analysts reviewing logs would take days or weeks to identify patterns that automated systems can surface in seconds. Abuse also often emerges gradually and attack volumes below alerting thresholds that only become clearly anomalous when viewed across extended time windows. Automated behavioral analytics continuously correlates these signals at the speed and scale required to detect and respond before significant damage accumulates.

What is scraping abuse, and why can’t simple rate limiting prevent sophisticated scraping attacks?+–

Scraping abuse uses API endpoints to systematically extract large volumes of data like competitor pricing, product catalogs, user directories, or market intelligence and at scale and speed far beyond what human users would do. Simple rate limiting fails against sophisticated scraping because attackers distribute requests across thousands of residential proxy IPs, each generating request rates below the per-IP threshold. They also rotate user agents, vary request timing to mimic human patterns, and respect robots.txt to avoid obvious bot signatures. Effective scraping detection requires identifying the collective pattern across distributed request origins — recognizing systematic data extraction by behavioral signature rather than source identity.

How does API-targeted DDoS differ from traditional network-layer DDoS, and why is it harder to defend against?+–

Network-layer DDoS floods bandwidth with packets; it’s mitigated by network-level filtering and CDN scrubbing. API-targeted DDoS sends legitimate-looking HTTP requests to expensive API endpoints like complex database queries, resource-intensive computation, or cache-bypassing requests that exhaust application server or database resources at low request volumes. Because the traffic is technically valid HTTP at normal volumes, network-level DDoS defenses are ineffective. Defense requires identifying which API calls are computationally expensive and implementing specific rate limits or caching strategies for those endpoints, while detecting patterns of deliberately targeting resource-intensive paths that indicate application-layer DoS intent rather than ordinary traffic spikes.

What role does behavioral analytics play in distinguishing legitimate high-volume API users from abusers?+–

Behavioral analytics establishes baselines for what legitimate high-volume usage looks like a trading platform might have legitimate users making thousands of price check API calls, while an attacker conducting reconnaissance would show different call sequence patterns, object enumeration behavior, or data extraction patterns. The key is context: high volume alone isn’t abuse if it matches established user patterns. Behavioral analytics differentiates by analyzing the nature of calls (random enumeration versus deterministic user-driven queries), the diversity of objects accessed (legitimate users access their own data; scrapers access everything), and temporal distribution (human behavior has natural breaks; automation runs continuously).

What is “prioritized automation of the API ecosystem” as mentioned in the blog, and why is it the defensive response to API abuse?+–

Prioritized automation of the API security ecosystem means automating the detection, analysis, and response functions that must operate at machine speed to counter automated attacks. Because API abuse is executed programmatically like credential stuffing scripts, scraping bots, enumeration loops and human response timescales are fundamentally mismatched with attack speeds. Automated detection surfaces suspicious patterns within seconds; automated throttling or blocking responds before significant data is extracted or accounts are compromised; automated alerting ensures human analysts focus on validated, high-priority incidents rather than drowning in raw event streams.

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