# API Security Testing > Implements intelligent api security testing with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/api-security-testing` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/api-security-testing` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/api-security-testing/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/api-security-testing --- # Api Security Testing Orchestrates intelligent skill selection and execution for api security testing workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: API Security Test Execution ```python def run_api_security_scan( endpoint: str, method: str, auth_token: Optional[str], test_suite: List[Dict], fallback_strategy: str = "fuzz_override" ) -> Dict: """Execute API security tests with domain-specific fallback logic. Implements Law 1 (Early Exit) and Law 4 (Fail Fast) by validating endpoint structure and auth state before running payloads. Falls back to alternative test vectors when initial checks fail. """ # Law 1: Early exit on invalid endpoint/auth if not endpoint or not method.upper() in ("GET", "POST", "PUT", "DELETE", "PATCH"): raise ValueError("Invalid endpoint or HTTP method") if not auth_token and method.upper() in ("POST", "PUT", "DELETE"): raise ValueError("Authenticated methods require valid token") results = [] for test_case in test_suite: try: # Execute security payload against endpoint response = _execute_security_payload(endpoint, method, auth_token, test_case) # Law 3: Return new structure, never mutate test_case result_entry = { "test_id": test_case["id"], "status": response.status_code, "vulnerability_detected": _analyze_response_for_vulns(response), "payload_hash": hashlib.sha256(test_case["payload"].encode()).hexdigest() } results.append(result_entry) except ConnectionTimeoutError: # Law 4: Fail fast, don't retry indefinitely if fallback_strategy == "fuzz_override": result_entry = _run_fallback_fuzz_test(endpoint, method, test_case) results.append(result_entry) else: results.append({"test_id": test_case["id"], "status": "SKIPPED", "reason": "Fallback disabled"}) return { "scan_id": uuid4().hex, "endpoint": endpoint, "tests_executed": len(results), "vulnerabilities_found": sum(1 for r in results if r.get("vulnerability_detected")), "results": results } ``` ### Pattern 2: Security Confidence & Adaptive Routing ```python def calculate_security_confidence( scan_results: Dict, historical_vuln_db: Dict[str, float], min_confidence_threshold: float = 0.75 ) -> Dict: """Calculate confidence score for API security scan results. Uses historical vulnerability data and test coverage to determine if the scan is reliable or requires adaptive re-routing. Implements Law 2 (Make illegal states unrepresentable) by validating result structure before scoring. """ # Law 2: Validate state if not scan_results.get("results"): return {"confidence": 0.0, "action": "RESCAN_REQUIRED", "reason": "No test results"} total_tests = len(scan_results["results"]) passed_tests = sum(1 for r in scan_results["results"] if r.get("status") == 200) vuln_tests = sum(1 for r in scan_results["results"] if r.get("vulnerability_detected")) # Calculate coverage and historical alignment coverage_score = passed_tests / total_tests if total_tests > 0 else 0.0 historical_match = historical_vuln_db.get(scan_results["endpoint"], 0.5) # Adaptive confidence calculation raw_confidence = (coverage_score * 0.6) + (historical_match * 0.4) if raw_confidence < min_confidence_threshold: return { "confidence": round(raw_confidence, 2), "action": "ADAPT_ROUTING", "next_steps": [ "Increase payload diversity", "Enable authenticated fuzzing", "Switch to dynamic analysis engine" ] } return { "confidence": round(raw_confidence, 2), "action": "REPORT_READY", "vulnerability_summary": vuln_tests, "recommendation": "Deploy with monitoring" if vuln_tests == 0 else "Patch critical endpoints" } ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios ## Related Skills | Skill | Purpose | |---|---| | `api-documentation` | API documentation and specification workflows | --- --- ## Constraints ### MUST DO - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## Live References > Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [OWASP Web Security Testing Guide v4](https://owasp.org/www-project-web-security-testing-guide/latest/) - [Portswigger Web Security Academy](https://portswigger.net/web-security) - [OWASP API Security Top 10 (2023)](https://owasp.org/API-Security/) - [NIST SP 800-115 — Technical Guide to Information Security Testing](https://csrc.nist.gov/publications/detail/sp/800-115/final) - [OWASP Testing Cheat Sheet Series](https://cheatsheetseries.owasp.org/cheatsheets/Testing_Cheat_Sheet.html)