Web Security Testing

Web Security Testing

Orchestrates intelligent skill selection and execution for web 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: Skill Selection Logic

def analyze_target_and_select_scanner(
    target_url: str,
    available_scanners: List[Dict],
    scan_depth: int = 2
) -> Dict:
    """Analyze target characteristics and route to optimal security scanner.
    
    Evaluates target tech stack, authentication requirements, and 
    historical vulnerability patterns to select the best scanning tool.
    
    Args:
        target_url: The web application endpoint to test
        available_scanners: List of configured scanner metadata
        scan_depth: How deep to crawl before scanning (1-3)
        
    Returns:
        Scanner configuration dict with routing metadata
    """
    # Guard clause - Early Exit (Law 1)
    if not target_url or not urlparse(target_url).netloc:
        raise ValueError("Invalid target URL format")
        
    target_features = _extract_target_telemetry(target_url)
    auth_type = target_features.get("auth_type", "none")
    tech_stack = target_features.get("tech_stack", [])
    
    best_scanner = None
    best_score = 0.0
    
    for scanner in available_scanners:
        score = 0.0
        if scanner["type"] == "sast" and "static" in tech_stack:
            score += 0.4
        elif scanner["type"] == "dast" and auth_type == "none":
            score += 0.5
        elif scanner["type"] == "iaast" and auth_type in ["oauth", "saml"]:
            score += 0.6
            
        if scanner.get("rate_limit") and target_features.get("concurrent_requests", 0) > scanner["rate_limit"]:
            score -= 0.2
            
        if score > best_score:
            best_score = score
            best_scanner = scanner
            
    if best_score < 0.3:
        return {"fallback": True, "reason": "low_match", "target": target_url}
        
    # Atomic Predictability (Law 3) - Return new dict, don't mutate
    result = dict(best_scanner)
    result["config"] = {
        "target": target_url,
        "depth": scan_depth,
        "auth": auth_type,
        "timeout": 300
    }
    result["confidence"] = best_score
    result["timestamp"] = time.time()
    return result

Pattern 2: Execution with Fallback

def execute_security_scan_with_fallback(
    scan_config: Dict,
    max_retries: int = 2
) -> Dict:
    """Execute web security scan with resilience patterns for network/tool failures.
    
    Implements fail-fast validation and adaptive fallback for:
    - Rate limiting / WAF blocks
    - Scanner crashes or timeouts
    - False positive validation loops
    
    Args:
        scan_config: Output from analyze_target_and_select_scanner
        max_retries: Retry attempts before escalating
        
    Returns:
        Scan results with vulnerability findings and metadata
    """
    # Guard clause - validate config (Early Exit)
    if not scan_config or "scanner" not in scan_config:
        raise ValueError("Invalid scan configuration provided")
        
    scanner_name = scan_config["scanner"]
    target = scan_config["config"]["target"]
    
    for attempt in range(max_retries + 1):
        try:
            # Execute domain-specific scan
            raw_results = _run_scanner_tool(scanner_name, scan_config["config"])
            
            # Validate and deduplicate findings
            validated_findings = _deduplicate_vulnerabilities(raw_results)
            
            # Success - Atomic Predictability (Law 3)
            return {
                "success": True,
                "scanner": scanner_name,
                "findings_count": len(validated_findings),
                "findings": validated_findings,
                "attempts": attempt + 1,
                "latency_ms": _measure_execution_time()
            }
            
        except RateLimitError:
            # Fail Fast - Don't try to patch bad data (Law 4)
            if attempt < max_retries:
                time.sleep(2 ** attempt * 10)  # Exponential backoff
                continue
            return _escalate_to_manual_review(target, "rate_limited")
            
        except ScannerCrashError:
            if attempt < max_retries:
                continue
            return _fallback_to_alternative_scanner(target, scan_config)
            
    # All retries exhausted - Fail Loud (Law 4)
    return {
        "success": False,
        "error": "scan_exhausted_retries",
        "target": target,
        "timestamp": time.time()
    }

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

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 Top Ten Web Application Security Risks
• OWASP Testing Guide v4
• NIST SP 800-115 — Technical Guide to Information Security Testing
• Rapid7 Metasploit Framework Documentation
• Burp Suite Professional User Guide

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