API Security Is Still Tested From the Outside - It’s Time to Protect Endpoints From the Inside

API security is still tested from the outside, after systems are deployed and behavior is already exposed. Most security tools evaluate what an attacker can do to a running system, not what the system was intended to allow. As a result, many API incidents stem from untested assumptions in code rather than broken defenses. Real protection starts by validating security intent where endpoints are defined, not after failures appear in production.

The Tea App incident, where a Firebase bucket remained publicly accessible, is just one example. No exploit was required. No clever bypass was discovered. The endpoint was open because nothing in the development process asserted that it must not be.

This pattern reflects a systemic failure across engineering, security, and process. At its core, it’s a failure of how we think about API security during development.

Most organizations test API security after the system exists. Once services are deployed and traffic flows, teams rely on penetration tests, dynamic and static scanners, or bug bounty programs to identify risk.

These practices are necessary, but they share the same limitation: they evaluate observable behavior, not developer intent. They can tell you what an attacker can do to a deployed system, but they cannot tell you what the system was supposed to allow in the first place.

By the time an issue is found, the code has already shipped, long after the pull request where the risk was introduced, and PR coverage provides a more practical signal of quality to help expose that risk earlier. The context of the change is lost, ownership is unclear, and fixes are reactive. What appears to be a security failure is often a timing failure.

Security is being validated too far downstream from where the most important decisions are made.

Outside-in API security focuses on observing exposed behavior after deployment, testing running systems to see what can be accessed or exploited. Inside-out API security starts earlier, verifying security intent directly in code, where routes, access rules, and validation logic are defined. The distinction matters because behavior only shows what a system allows today, while intent determines what it should never allow at all.

Every API is explicitly defined in the codebase. Routes, handlers, middleware, guards, and validation rules describe what the system exposes and under what conditions.

That is where access decisions are encoded. That is where assumptions are introduced. And yet, most security testing treats the codebase as opaque, waiting until those decisions surface as runtime behavior.

This disconnect made sense when systems were relatively static and releases were infrequent. It breaks down in modern development environments, where APIs evolve continuously through pull requests.

If an endpoint is introduced or modified in code, that is the moment when its security properties should be verified.

Outside-in security testing answers an important but limited question: what can I do to this system right now?

Inside-out security testing asks something more fundamental: what did we intend this system to allow, and is that intent actually enforced?

Consider a simple endpoint definition:

Is this endpoint public? Should it require authentication? Is it restricted to administrators? Does it rely on middleware applied elsewhere?

Without an explicit test asserting the expected security behavior, the answer is not guaranteed. It exists only as an assumption, in someone’s head, in documentation, or in a code review comment that will eventually be forgotten.

In many API incidents, nothing is technically broken. No exception is thrown. No vulnerability is triggered. The system simply does what it was written to do, even if that was never the intended outcome.

This is a difficult realization for many teams, because it challenges how security incidents are usually explained.

When an issue occurs, the instinct is to look for a bug or a missing check. Often, however, nothing is wrong with the implementation itself. The system behaves exactly as it was designed to behave.

The real problem is simpler: there was never a test that explicitly stated, “This must never be allowed.”

Authentication rules, authorization logic, input validation, and data exposure limits are security contracts. If they are not asserted in tests, they are not enforced, they are assumptions. And assumptions tend to fail silently, until they surface in production.

Modern APIs evolve continuously. Endpoints are added, refactored, and reused through a constant stream of pull requests. Authentication logic shifts, middleware is reused in new contexts, and security assumptions quietly drift as systems grow.

Even without AI, this pace already stretches the limits of manual review and manual testing. Reviewers reason about intent, while ad-hoc tests validate behavior, but neither produces a durable, executable guarantee of what must never be allowed.

AI-generated code compounds the problem, especially when teams adopt AI-generated code at enterprise scale. New endpoints can appear in seconds, often without deep contextual understanding of the surrounding system. The code may look correct and follow established patterns, yet small differences, a missing guard, an incorrect default, a misplaced middleware, can significantly change an endpoint’s security posture.

In this environment, relying on humans to remember which security cases to test, and when, simply doesn’t scale.

Protecting endpoints during development changes how teams think about API security. Instead of discovering issues after deployment, security becomes something that is enforced continuously as the code evolves.

The first shift is from periodic audits to continuous protection of the existing codebase. Endpoints that lack explicit security tests represent unverified assumptions, even if they have been in production for years.

The second shift is toward incremental verification. Every pull request changes the API surface in small but meaningful ways, adding an endpoint, modifying access logic, or refactoring middleware. Security tests must adapt to those changes by introducing or updating tests as part of the development process.

Together, these shifts turn security from an external checkpoint into an internal guarantee.

‍

‍

For the first time, it is practical to encode security intent as executable tests and verify it continuously as systems evolve, aligning with approaches like the Early Quality Score (EQS) that focus on confidence rather than surface metrics. What once depended on human memory, best effort, and periodic audits can now be enforced directly in the development process, where APIs are defined and changed.

As software development accelerates, especially with AI-generated code, this shift is no longer optional. Speed without early verification does not scale, and security that relies on post-hoc detection will always lag behind change.

The teams that succeed will be the ones that stop assuming security will hold and start proving it - early, continuously, and in code.

‍

In the next era of software, security won’t be something you discover. It will be something you can prove.