How Schema Enforcement Helps Prevent API Attacks

Key Insights

• API attacks often exploit outdated, misconfigured, or unmanaged APIs.

• Schema enforcement acts as a security contract, validating API requests and responses.

• Strong schema validation helps block malicious, malformed, and unauthorized API traffic.

• API schemas define data types, required fields, relationships, and security constraints.

The Growing Threat of API Attacks

One API use case is connecting online retailers to their payment portals of choice. Stripe is one of those online payment providers – and a highly popular one at that. In 2025, it was discovered that threat actors were able to exploit old APIs in Stripe’s infrastructure, and use them to validate stolen card details.

Delving into the full attack chain can give a dissection of today’s multi-part – and often complex – API attacks. The initial stage sees the threat actors exploit vulnerabilities in a target’s WooCommerce and WordPress site. This vulnerability allows them to inject a malicious loader script.

From there, this loader script shows every site visitor a fake payment page that hides the genuine Stripe payment button underneath the attackers’ own transparent version. When a user clicks on this, they’re directed to an attacker-controlled payment portal. Entering their payment information as usual, the attackers’ payment portal calls a deprecated Stripe API, which validates the end-user’s card details. Because this old API doesn’t check the sources or rates of these requests, the attackers are free to distribute this across large numbers of sites. Once verified, the stolen card details are shipped to the attacker’s own database.

In contravention of API security best practices, these outdated Stripe APIs were still able to request card detail verification from Stripe’s own servers, resulting in a highly efficient campaign of credential theft.

Understanding API Schema

Schemas provide the structural backbone for consistent, reliable, and predictable communication between software systems. Just as a database schema defines how stored data should be organized, an API schema formalizes how data should be exchanged. With schemas in place, it’s possible to explicitly reject or allow APIs according to how closely they adhere to them. Fundamentally, they act as mini contracts within API schema enforcement, ensuring both sides agree on the format, data types, and validation rules at play.

Data types

API payload protection first needs to define the type of information that an API should be handling. Since APIs are so versatile, there’s a large swathe of data types they interact with, including:

• Strings: These are sequences of characters that represent text – think names, descriptions, and comments. Some strings can also be flagged with their own special formats like dates, times, and byte data.
• Integers and numbers: An integer refers to whole numbers that don’t have a fractional component, useful for counting the number of users. Numbers, on the other hand, can contain decimals, useful for representing numeric data.
• Boolean data: More complex API calls may require specific values or ranges thereof. Boolean data refers to true or false values and is useful for binary state representations.
• Objects: These are key-value pairs that represent structured data – an object may include strings that are owned by the same user, like an email address and username. Packaging these into objects allows for an API to call and edit individual properties.
• Arrays: These allow a program to create and store lists of multiple items – for instance, the transaction histories of multiple users. Array formatting allows an API to quickly retrieve data in a specific user’s transaction from a specific date.
• Enums: A fixed set of predefined possible values; an enum value must be one from a limited list of named constants. It’s commonly used for status updates such as pending, approved, and rejected.
• Null: The final data type is one that indicates no data being included at all. Null is a stand-in for data that’s unknown, unset, or intentionally cleared.

An API schema will define precisely which data type each property should have – if it doesn’t match its declared type, it should result in a validation failure. For example, a field declared as an integer must not receive a string.

Required and Optional Fields

It’s not enough to just include what data types should be in an API schema: it also needs to define which properties should be included when being exchanged through the API. For example, when an API is handling object data, it may be mandatory to include the object’s ID and name fields.

If a required field is missing or omitted, the API request or response is considered invalid and typically rejected: this is a core ability of API compliance enforcement.

Relationships Between Elements

API schemas can define relationships between elements by explicitly specifying how different data entities are connected.

• One-to-One Relationship: Represents a direct, exclusive link between two entities – like a customer profile that’s linked to exactly one address.
• One-to-Many: Represents a single entity linked to multiple related records, such as a customer with multiple orders.
• Many-to-Many: Involves multiple instances of one entity linked to multiple instances of another. Typically modeled via linking tables and objects, an example would be students enrolled in multiple courses; each course also has multiple students. To highlight the relationship, each student’s ID could include an array of references for each course.

In that last example, it’s easy to see how defining these relationships usually requires a reference field in each object. Common schemas use a small $ref or href pointer, which explicitly defines which attributes are linked to that object.

Further Validation Rules

Alongside the basic data handling rules above, it’s highly likely that an API schema will need to add some further rules to define API requests within a specific context. For example, properties can have additional restrictions like minimum/maximum values for numbers, or length constraints for strings and arrays. These extra rules are often explicitly drafted for tight API security – length constraints prevent injection and buffer overflow attacks, while version controls prevent the misuse of outdated APIs (as was seen in the Stripe API attack).

Rather than writing the entirety of your own schema, today’s API landscape has matured into an abundance of industry best practices.

The Plethora of API Schema

JSON Schema is a declarative vocabulary for describing the structure, data types, and constraints of JSON instances. Instead of relying solely on free-form JSON, the schema can state what fields exist, what type each must have, and what rules apply. That schema then becomes a contract that any JSON instance can be validated against.

Different APIs require different schemas, however: the OpenAPI Specification – formerly Swagger – provides a widely adopted standard for RESTful APIs. It’s partly so popular because it can adapt to the large variety of data types involved in Large Language Models – including complex, nested data structures. The $ref structure previously mentioned is OpenAI’s reference language. Some APIs are built on XML – the popular schema for which is the XML Schema Definition (XSD). With so many API schema out there, it can quickly become a headache to secure all APIs across an organization’s entire tech stack.

Gain and Enforce Full Schema Visibility with Check Point

Check Point is Check Point’s industry-leading WAF. Architectured as an API and traffic gateway, it employs a machine-learning engine that identifies unique API endpoints through HTTP and URI path analysis.

With their associated endpoints identified, Check Point then determines the connections being made via API: requests, query parameters, and content type all build a real-world profile of the APIs being used across the entirety of the organization. This data is used to infer precisely which type of API is making or fulfilling each request.

The end result of this live traffic analysis is a positive API security model that enforces each API’s ideal schema; only requests that match the approved schema are allowed. Check out the solution for yourself with a free trial today.