42Crunch Plugin: A Game-Changer for API Security in UAE

What the audit engine actually looks at

42Crunch does not scan compiled code or watch live requests. It parses the OpenAPI or Swagger definition file that describes an API's paths, parameters, and responses, and checks that definition against a fixed rule set. A typical audit flags security schemes that are declared but never referenced on an operation, string and array fields with no maximum length or count, integer fields with no bounds, response schemas that return more properties than the endpoint needs, and operations with no defined error responses at all. The output is a score out of 100 alongside a list of findings, each tied to a specific line in the contract and, where the rule maps cleanly, to an OWASP API Security Top 10 category.

This is contract linting, and it is worth being precise about what that means. A well-written OpenAPI file can still describe an API whose authorisation checks are broken in the code that implements it, because nothing in the YAML says the check actually runs before the database query fires. The audit tells you the contract is well formed and reasonably conservative. It does not tell you the implementation matches the contract.

Why it moved into GitHub Copilot

The plugin started as a VS Code extension that flagged issues inline as a developer typed an OpenAPI file. Putting the same engine behind a GitHub Copilot chat participant changes where the check happens. When Copilot generates or edits an API definition, the developer can ask the extension to audit that draft immediately, in the same window, before the file is committed. For teams where a growing share of new endpoint scaffolding is AI-generated, that matters: a model completing a YAML file has no reason to add an authorisation scheme unless the existing pattern in the file already has one, and it will happily copy a permissive schema from three endpoints earlier in the same file. Auditing at generation time catches that gap before it becomes a code review comment, or before it ships.

What it catches, and what it structurally cannot

The OWASP API Security Top 10 splits roughly into two groups for this purpose. Contract audit is genuinely strong at catching security misconfiguration, improper inventory management, unrestricted resource consumption where limits are missing from the schema, and the absence of declared authentication or authorisation schemes. These are static properties of the document.

It is structurally weak at catching broken object level authorization and broken function level authorization, which are consistently among the most common findings in real API assessments. Both are runtime behaviours: does the API actually check that the authenticated user owns the object referenced by the ID in the URL, and does it actually enforce role checks on the function being called. A contract can declare a bearer token scheme correctly, score well, and still let any authenticated user pull any other user's record by changing an ID in the path, because the audit has no way to execute the endpoint and observe what the authorisation middleware does. Catching that requires exercising the live API with valid and invalid tokens across different account contexts, which is dynamic testing, not contract review. That is the gap a manual VAPT engagement is built to close, and it is a large part of why API assessments still need a person with an intercepting proxy rather than only a spec linter.

Fitting it into a pipeline without breaking one

Most teams that adopt this kind of tool set a minimum score as a CI gate: a pull request that drops the contract score below a threshold fails the build. The threshold is the whole exercise. Set it too low and it catches nothing new. Set it too high on day one against an existing API estate and it blocks every unrelated change until someone burns a sprint fixing legacy findings that have nothing to do with the current pull request. The workable pattern is to baseline the existing score, gate only on regressions from that baseline, and raise the bar gradually as the backlog of findings gets worked down, rather than demanding a clean slate immediately.

42Crunch also sells a runtime protection component that generates firewall rules from the same OpenAPI contract, enforcing the shapes and bounds the audit checked for at design time. Treat that as complementary to a proper API gateway or WAF policy, not a substitute for one; it enforces what the contract says, and inherits any gap the contract itself has.

Where this sits for organisations in the UAE

Open banking and open finance initiatives in the region have pushed more financial institutions to expose customer facing APIs that did not previously need to be internet reachable, which is exactly the surface this class of tooling was built for. What regulators and assessors ask for in practice is an accurate inventory of what APIs exist, what data each one touches, and evidence that they have been tested, not evidence that a specific product was purchased. A contract audit is a reasonable way to build and maintain that inventory as part of the development workflow, because the OpenAPI file already has to exist for documentation and client generation anyway. It is a weaker answer if it is the only testing an organisation can point to when an assessor asks how authorisation is verified on a specific endpoint. See how this fits alongside secret scanning and dependency checks in a broader DevSecOps pipeline.

AI-assisted development raises the same question from a different angle. The compliance risk in AI-generated code is not unique to APIs: a generated authorisation check needs the same review as one a person typed, and accepting a pattern because it compiled is how the gap gets into production in the first place.

A short rule for using it

Run the contract audit on every API definition before it merges, and treat a passing score as proof the schema is sane, not proof the API is secure. Keep a running VAPT programme built to OSCP-level testing standards for anything that touches customer data or moves money, with authorisation testing specifically in scope at the object and function level. If an assessor or a regulator asks how API security is handled, the honest answer has two parts: automated contract checks catch the obvious gaps early, and a tester who can manipulate tokens and IDs by hand catches the ones that only show up at runtime.

Common questions

Is API security the same as application security?

They overlap but are not the same scope. Application security covers the full software estate, including business logic, session handling, and the client side. API security is the subset that concerns the interfaces other systems call directly, usually without a browser or a user in the loop, which changes which controls matter most: schema validation, rate limiting, and authorisation on every object reference tend to dominate over things like cross-site scripting.

How does a tool like this handle false positives?

Contract audit tools generally let a reviewer mark a specific finding as accepted or suppressed for a given endpoint, with a reason attached, so the same finding does not keep failing against the same intentional design choice on every scan. That suppression list should be visible in code review, because a long list of silently accepted findings is itself a signal that the gate has stopped doing its job.

Does this only work inside GitHub Copilot?

The underlying audit engine works on the OpenAPI or Swagger file directly, independent of how that file was written, so it also ships as a standalone VS Code extension, a CLI, and a CI/CD action. The Copilot integration is one entry point among several, useful specifically because it puts the check at the moment of generation rather than requiring a separate step.