API Testing & Security

Build AI-powered API testing systems — contract tests, integration tests, security scans, performance testing, and the automated quality checks that catch issues before production.

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🔄 Quick Recall: In the previous lesson, you built API versioning strategies that let your API evolve without breaking consumers. Now you’ll build the testing and security systems that catch issues before they reach production — because shipping a broken or insecure API is far more costly than preventing the issue.

API testing requires a different mindset than application testing. Your API is a contract with consumers — any unexpected change in behavior, response format, or security posture can break their integrations. AI generates comprehensive test suites that cover the edge cases humans miss and security scans that find vulnerabilities before attackers do.

Contract Testing

AI prompt for contract test generation:

Generate a contract test suite for my API. OpenAPI spec: [PASTE OR DESCRIBE]. Framework: [jest/pytest/Go testing/etc.]. For each endpoint in the spec, generate tests that verify: (1) response status codes match the spec for valid requests, (2) response body structure matches the schema (all required fields present, correct types), (3) field formats match constraints (date formats, enum values, string lengths), (4) error responses for invalid inputs match the error schema, (5) authentication requirements are enforced (401 without token, 403 without permissions). Each test should: send a real HTTP request to a test server, validate the response against the spec, and provide a clear error message when the contract is violated. Include setup/teardown for test data.

Test pyramid for APIs:

Test Layer	What It Verifies	Speed	Coverage	AI Generates
Contract tests	Response matches OpenAPI spec	Fast	Schema compliance	Tests from spec
Integration tests	Full request-to-response flow	Medium	Business logic	Happy + edge cases
Edge case tests	Boundary inputs, special characters	Medium	Input validation	Unusual but valid inputs
Security tests	Auth, injection, data exposure	Slow	Vulnerability coverage	Attack patterns
Performance tests	Response time, throughput	Slow	Load handling	Load scenarios

Integration Test Generation

AI prompt for integration tests:

Generate integration tests for my API endpoint: [METHOD] [URL]. Request schema: [FIELDS WITH TYPES AND VALIDATION]. Response schema: [FIELDS WITH TYPES]. Business rules: [DESCRIBE — e.g., “orders require at least one item, total is auto-calculated, status defaults to ‘pending’”]. Generate tests for: (1) Happy path — valid request with all required fields, verify correct response, (2) Missing required fields — one test per required field, verify 400 with correct error detail, (3) Invalid field values — wrong types, out-of-range values, invalid formats, (4) Edge cases — empty strings, very long strings, special characters, unicode, null values, maximum array sizes, (5) Business rule violations — specific to the endpoint’s logic, (6) Idempotency — does repeating the same request produce the same result (for PUT/DELETE)?

✅ Quick Check: AI generates 40 integration tests for your ‘create user’ endpoint. 35 pass, 5 fail. The failing tests all involve email addresses with unusual but valid formats: “user+tag@gmail.com”, “user@subdomain.domain.com”, “user@123.123.123.123”. What does this tell you? (Answer: Your email validation regex is too strict — it rejects valid email formats. This is a common bug that manual tests rarely catch because developers test with “normal” email addresses. AI generates tests with the full range of valid inputs per RFC 5322, finding validation rules that are too restrictive. Fix: use a proven email validation library instead of a custom regex.)

Security Testing

AI prompt for API security scan:

Perform a security review of my API based on this OpenAPI spec: [PASTE OR DESCRIBE]. Check for the OWASP API Security Top 10: (1) Broken Object Level Authorization (BOLA) — can user A access user B’s resources by changing the ID in the URL? (2) Broken Authentication — are all sensitive endpoints auth-protected? Are tokens validated properly? (3) Excessive Data Exposure — do responses include fields the consumer doesn’t need (internal IDs, email addresses, timestamps)? (4) Lack of Resources & Rate Limiting — which endpoints have no rate limits? (5) Broken Function Level Authorization — can a regular user access admin endpoints? (6) Mass Assignment — can consumers set fields they shouldn’t (role, is_admin, internal_status)? (7) Security Misconfiguration — are error responses leaking stack traces or internal details? For each finding: severity (critical/high/medium/low), specific endpoint affected, proof of concept, and recommended fix.

OWASP API Top 10 quick reference:

Vulnerability	What to Check	AI Detection
BOLA (Broken Object Auth)	Can user A access user B’s /users/:id?	Checks for missing ownership validation
Broken Auth	Unprotected endpoints, weak tokens	Scans for missing auth requirements
Excessive Data	Internal fields in responses	Compares response fields to what’s needed
No Rate Limiting	Unbounded request frequency	Checks all endpoints for rate limit config
Mass Assignment	Can consumers set admin fields?	Compares writable fields to request schemas
Injection	SQL, NoSQL, command injection	Checks input validation on all parameters

Performance Testing

AI prompt for load test scenarios:

Design load testing scenarios for my API. Endpoints: [LIST YOUR MOST-USED ENDPOINTS]. Expected traffic: [REQUESTS PER SECOND AT PEAK]. Generate: (1) baseline test — expected traffic level for 10 minutes, measure response times and error rates, (2) stress test — 2×, 5×, 10× expected traffic, find the breaking point, (3) spike test — sudden 10× traffic increase for 30 seconds (simulating viral event or traffic burst), (4) endurance test — expected traffic for 2 hours (detect memory leaks and connection pool exhaustion), (5) specific endpoint tests — complex queries, large response payloads, endpoints with database writes. For each scenario: expected response time (p50, p95, p99), acceptable error rate, and what to do if thresholds are exceeded. Output as a k6 or Artillery configuration file.

CI/CD Quality Gates

AI prompt for API quality pipeline:

Design a CI/CD quality gate pipeline for my API. Create checks that run on every pull request: (1) OpenAPI spec validation — is the spec valid YAML/JSON? Does it follow our style guide? (2) Breaking change detection — compare the PR’s spec against main branch, flag any breaking changes, (3) Contract tests — does the implementation match the spec? (4) Integration tests — do all endpoints work correctly? (5) Security scan — any new vulnerabilities introduced? (6) Documentation check — does every endpoint have descriptions, examples, and error responses? (7) Performance check — are any endpoints slower than the baseline? Create the pipeline as a GitHub Actions workflow with clear pass/fail criteria for each gate.

Key Takeaways

Contract tests (response matches spec) catch the most dangerous API bugs: schema changes, format changes, and missing fields that break consumer integrations silently — AI generates these directly from your OpenAPI spec
Integration tests with AI-generated edge cases (unusual emails, unicode, boundary values, special characters) find validation bugs that happy-path manual tests miss — the suite-number-in-address bug would never be caught by a developer testing with “123 Main St”
API security scanning should cover the OWASP API Top 10 systematically, not just the vulnerability that was just reported — a single finding usually indicates systemic gaps across other endpoints
Performance testing should include not just load tests (can it handle peak traffic?) but endurance tests (does it develop memory leaks over 2 hours?) and spike tests (what happens when traffic 10× in 30 seconds?)
CI/CD quality gates that check spec validity, breaking changes, contract compliance, and security on every pull request make API quality a guaranteed property of your development process, not a manual review step

Up Next

In the final lesson, you’ll build your personalized implementation plan — applying these AI-powered API practices to your specific project, starting with the highest-impact improvement.

Knowledge Check

1. Your API has 100% unit test coverage. Every function works correctly in isolation. You deploy to staging and a consumer reports that the 'create order' endpoint returns a 500 error when the shipping address includes a suite number. What testing gap does this reveal?

Unit tests should have caught this — the coverage wasn't truly 100% Unit tests verify functions in isolation — they don't test the full request-to-response flow. The gap is integration testing: sending actual HTTP requests with realistic data through the full stack (routing → validation → business logic → database → response). AI generates integration test suites that include: (1) standard happy-path requests, (2) edge-case inputs (addresses with suites, special characters in names, unicode, empty strings, very long strings), (3) boundary conditions (maximum allowed items, minimum values, exactly-at-limit values), (4) combinatorial cases (different combinations of optional fields). The suite number bug would have been caught by an AI-generated edge-case test for 'address with additional line' You need better QA — automated tests can't catch everything

2. You have no contract tests. Your API spec says the 'list users' response includes a 'created_at' field (ISO 8601 string). A developer accidentally changes the serializer and now it returns a Unix timestamp. No tests fail. The change ships. Consumers break. How do you prevent this?

Code review should catch this — add more reviewers Contract tests that verify the API responses match the OpenAPI spec. A contract test for this endpoint: (1) sends GET /users to the actual API, (2) validates the response body against the OpenAPI schema — field names, types, required fields, format constraints, (3) specifically checks that created_at is a string matching ISO 8601 format, not a number. If the serializer changes the format, the contract test fails before deployment. AI generates contract tests from your OpenAPI spec automatically: one test per endpoint, validating every field type, required field, enum value, and format constraint. Run them in CI on every pull request Add a unit test for the date serializer function

3. Your API accepts a user-provided 'redirect_url' parameter. A security researcher reports that it's vulnerable to open redirect attacks. What should AI help you check across your entire API?

Just fix the redirect_url parameter — it's an isolated issue A systematic security review across all endpoints: (1) Input validation — are ALL user inputs validated (not just this one)? Check for: URL parameters that could be open redirects, string fields that could contain SQL injection, fields rendered in responses that could enable XSS, file upload endpoints without type validation, (2) Authentication gaps — are all endpoints that should require auth actually checking it? (3) Authorization gaps — can user A access user B's resources? (4) Rate limiting — are all endpoints rate-limited? Are destructive operations (delete, password reset) extra-limited? (5) Data exposure — are responses leaking internal IDs, email addresses, or other PII beyond what's needed? AI scans your OpenAPI spec and codebase for each vulnerability category and generates a prioritized finding report Add URL validation to the redirect parameter and move on

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Related Skills

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