Why Input Validation for APIs Matters in the AI Age

API security was designed with humans in mind. Time-honored API security techniques like rate limiting use standard human behavior as their guideline. This leaves a bit of a blind spot when it comes to AI, as it doesn’t necessarily behave like a human. Things are even more complicated now that AI can perform actions directly, with no human intervention required. API security systems need ways to verify who’s attempting to access data.

Input validation is one of the strongest tools for ensuring API security, as AI can generate millions of variations on inputs with no effort. It’s not just good for APIs, either. Input validation also helps ensure that API responses are in a format that’s useful for an AI. With that in mind, we’re going to take a deeper look into input validation for APIs in the age of AI.

What Is Input Validation?

Input validation is the process of verifying that the data received by a system is accurate, complete, and conforms to expected standards. It ensures that only properly structured, safe, and meaningful data can enter a system. It also safeguards against errors, bugs, and malicious inputs.

Input validation often checks for things like data type, length, format (e.g., email, date), required fields, and acceptable ranges. As such, input validation is an important protective layer between external clients and internal resources. Without input validation, an API could be vulnerable to injection attacks, model poisoning, and system crashes.

Why Input Validation Matters: 4 Case Studies

Let’s pause for a moment and consider some of the real-world ramifications of improper input validation. Numerous high-profile data breaches have been caused by input validation not being implemented properly. Let’s explore four recent examples.

Volkswagen OTP API Breach

Consider the recent Volkswagen one-time password (OTP) API breach, for example. The system accepted any four-digit numeric input and lacked both rate limiting and input validation. As a result, attackers were able to brute-force OTPs simply by cycling through over 10,000 possible combinations until access was granted. Without input validation to detect repetitive patterns, throttle access, or limit attempts, the system effectively treated all numeric input as equally valid. This resulted in unauthorized access to vehicle profiles and exposure of internal credentials.

Ivanti Injection Vulnerability

Improper input validation also caused serious issues with Ivanti’s Endpoint Manager, which suffered from an expression language (EL) injection vulnerability. In this data breach, users were able to supply input directly to Hibernate Validator message templates without proper sanitization. Bad actors could input messages, like ${"".getClass().forName('java.lang.Runtime').exec('command')}, which would then run on the server. To make matters worse, the unauthorized endpoint was completely unsecured. Input validation would have prevented malicious code from being able to access the server.

Radware WAF Bypass

Lack of input validation allowed cybercriminals to bypass Radware’s Web Application Firewall (WAF) by sending malformed or encoded inputs that could bypass security protections. As a result, malicious input that would normally be blocked was allowed to reach the backend systems. This highlights a common input validation issue: failing to canonicalize or strictly parse incoming data, allowing inconsistencies between security filters and application logic to be exploited.

Cisco Privilege Escalation

Finally, Cisco faced a series of privilege escalation vulnerabilities that stemmed from poor input parameter validation. In some instances, users with limited administrative privileges were able to craft configuration commands containing illegal or out-of-band characters, which allowed them to escalate to root-level access. Cisco’s web-based management APIs also failed to enforce strict input checks and authorization verification, enabling crafted API calls to escalate privileges without proper oversight.

Best Practices For Input Validation Enforce Strict Schema

Enforcing strict schema on all incoming data is one of the best ways to implement strong input validation. Using schema validation frameworks like JSON Schema, Joi (for JavaScript), or Pydantic (for Python) allows users to formally declare the expected structure, types, formats, and constraints of each input field, which ensures that the API only processes data that conforms precisely to your specifications. This reduces the risk of malformed or unexpected input triggering unexpected behavior or vulnerabilities.

Also read: 4 Examples of JSON Schema In Production Use Standardized Responses

It’s also important to respond to invalid inputs securely and consistently. Instead of sending verbose error messages and running the risk of revealing details about internal logic or accidentally exposing sensitive data, you should use error responses that inform the client that input was rejected without exposing sensitive system information.

Implement Strict Formats and Constraints

Enforcing strict formats and constraints for common input types is another best practice for enhancing API security with input validation. Input fields like email addresses, phone numbers, dates, and identifiers should be validated using regular expressions or strict parsers, not informally interpreted or corrected by the backend. Allowing inputs that are close to the desired format can introduce ambiguity or allow malicious data to slip through.

Secure Sensitive Endpoints

Endpoints allowing access to sensitive data should be further secured with additional security solutions. Rate limiting, IP throttling, and captcha for high-risk endpoints like login forms or OTP requests will help prevent brute force attacks like the one that caused the Volkswagen data breach, as well as automated attacks. Creating an allowlist for entities allowed to access that endpoint instead of a denylist is another way to make sure sensitive endpoints are as secure as possible.

Final Thoughts on Input Validation for APIs in the AI Era

Input validation for APIs is only going to become more critical as AI continues to evolve. Standard API security solutions like rate limiting and user behavior analysis are based on standard human behavior, which could be next to useless against AI. There’s virtually no limit to the scale and precision of the attacks AI is capable of.

Input validation is one of the most dependable defenses against AI-driven attacks. The Volkswagen, Ivanti, Radware, and Cisco data breaches illustrate what can go wrong when input isn’t validated properly. Luckily, schema enforcement, standardized error responses, and strict format requirements will keep many of these cybersecurity risks at bay. Securing sensitive endpoints with techniques like throttling, CAPTCHAs, and safelisting will help even more. Implementing input validation will help you and your organization to move forward into the AI age with confidence.