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

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

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

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
• Portswigger Web Security Academy
• OWASP API Security Top 10 (2023)
• NIST SP 800-115 — Technical Guide to Information Security Testing
• OWASP Testing Cheat Sheet Series