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VXDF Technical Blog15 min read

The VXDF Engine: A Multi-Stage Architecture for Evidence-Based Security

Deep dive into the technical architecture that transforms theoretical vulnerability detection into provable exploit validation

VXDF Engine Demo
VXDF Engine in Action

Watch the VXDF engine transform raw security scan outputs into validated, exploitable findings with concrete evidence

The Alert Fatigue Crisis

21,000+
Hours wasted annually on false positives
85%
SAST findings are false positives
3-6 months
Average time to fix confirmed vulns

VXDF (Validated Exploitable Data Flow) represents a paradigm shift from theoretical vulnerability detection to evidence-based security analysis. This inaugural post dissects the technical architecture of the VXDF engine—a multi-stage analysis pipeline that systematically validates exploitability before alerting security teams.

Unlike traditional scanners that output raw findings, the VXDF engine operates as an automated security investigator: it ingests signals from multiple sources, correlates them through static analysis, dynamically validates potential exploits in sandboxed environments, and produces verified findings with concrete proof-of-concept demonstrations.

VXDF Engine Architecture Overview

┌─────────────────────────────────────────────────────────────────┐
│                    VXDF ENGINE PIPELINE                        │
├─────────────────────────────────────────────────────────────────┤
│ Stage 1: Universal Fact Ingestion & Modeling                   │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ SARIF       │ │ CycloneDX    │ │ API Disc.   │ │ IaC Scans   │ │
│ │ (SAST)      │ │ (SCA/SBOM)   │ │ (Custom)    │ │ (Checkov)   │ │
│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │
│        │               │               │               │        │
│        └───────────────┼───────────────┼───────────────┘        │
│                        ▼               ▼                        │
│              ┌─────────────────────────────────────────┐        │
│              │         BaseFact Objects                │        │
│              │ • VulnerabilityFact • EndpointFact     │        │
│              │ • PackageFact • ConfigFact             │        │
│              └─────────────────┬───────────────────────┘        │
├─────────────────────────────────────────────────────────────────┤
│ Stage 2: Static Correlation & Hypothesis Generation             │
│              ┌─────────────────▼───────────────────────┐        │
│              │        Correlation Graph Builder         │        │
│              │                                         │        │
│              │ Layer 1: Heuristic Correlation (0.1-0.4)│        │
│              │ Layer 2: Import Graph Analysis (0.5-0.6)│        │
│              │ Layer 3: Taint Analysis       (0.8-0.9)│        │
│              └─────────────────┬───────────────────────┘        │
│                               ▼                                │
│              ┌─────────────────────────────────────────┐        │
│              │    Hypothesis Graph (Facts + Edges)     │        │
│              │ Nodes: Facts | Edges: Relationships     │        │
│              └─────────────────┬───────────────────────┘        │
├─────────────────────────────────────────────────────────────────┤
│ Stage 3: Dynamic Validation & Hypothesis Testing               │
│              ┌─────────────────▼───────────────────────┐        │
│              │      Validation Orchestrator            │        │
│              │                                         │        │
│              │ • Payload Generation (context-aware)    │        │
│              │ • Sandboxed Request Execution           │        │
│              │ • Multi-Oracle Evidence Collection:      │        │
│              │   - Content Oracle (response analysis)   │        │
│              │   - Timing Oracle (blind injection)     │        │
│              │   - OAST Oracle (out-of-band callbacks) │        │
│              └─────────────────┬───────────────────────┘        │
├─────────────────────────────────────────────────────────────────┤
│ Stage 4: Reporting & Enrichment                                │
│              ┌─────────────────▼───────────────────────┐        │
│              │    Validated Exploitable Findings       │        │
│              │                                         │        │
│              │ • OWASP VXDF JSON Output               │        │
│              │ • Evidence Artifacts (req/resp logs)    │        │
│              │ • CWE/CVSS Enrichment                  │        │
│              │ • Remediation Guidance                  │        │
│              └─────────────────────────────────────────┘        │
└─────────────────────────────────────────────────────────────────┘

Stage 1: Universal Fact Ingestion & Modeling

The foundation of evidence-based security analysis is structured data normalization. Stage 1 transforms heterogeneous security tool outputs into a unified fact model, preserving provenance and enabling cross-tool correlation.

Input Sources & Parsing

Static Analysis (SARIF)

  • • Semgrep, CodeQL, Snyk Code
  • • Bandit (Python), ESLint (JS)
  • • Custom SAST rule outputs

Composition Analysis

  • • CycloneDX SBOM (Trivy, Grype)
  • • SPDX JSON (Snyk Open Source)
  • • Package vulnerability databases

API Discovery

  • • Flask route extraction
  • • Express.js endpoint mapping
  • • OpenAPI specification parsing

Infrastructure Scans

  • • Terraform (tfsec, Checkov)
  • • Kubernetes YAML (kube-score)
  • • CloudFormation templates

BaseFact Data Model

// Core BaseFact interface
interface BaseFact {
  id: string;                    // Unique identifier
  type: FactType;               // VULNERABILITY | ENDPOINT | PACKAGE | CONFIG
  location: SourceLocation;     // File path, line numbers
  provenance: ProvenanceInfo;   // Source tool, version, timestamp
  confidence: number;           // Initial confidence [0.0, 1.0]
  metadata: Record<string, any>; // Tool-specific data
}

// Example: SQL Injection from Semgrep SARIF
const vulnFact: VulnerabilityFact = {
  id: "vuln_001",
  type: "VULNERABILITY",
  location: {
    filePath: "src/models/user.py",
    startLine: 45,
    endLine: 47,
    codeSnippet: "cursor.execute(f\"SELECT * FROM users WHERE id = {user_id}\")"
  },
  provenance: {
    tool: "semgrep",
    version: "1.45.0",
    ruleId: "python.lang.security.audit.sqli.python-sqli",
    timestamp: "2024-01-15T10:30:00Z"
  },
  confidence: 0.8,
  vulnerabilityData: {
    cweId: "CWE-89",
    severity: "HIGH",
    category: "SQL_INJECTION",
    sinkFunction: "cursor.execute",
    taintedParameter: "user_id"
  }
};

Key Innovation: Every fact retains complete audit trail. We never lose track of which tool produced what finding, enabling confidence calibration and false positive feedback loops.

Stage 2: Static Correlation & Hypothesis Generation

Stage 2 transforms isolated facts into a Correlation Graph—a knowledge graph where nodes represent security facts and edges represent potential attack paths. This stage employs a three-layer analysis approach with increasing fidelity.

Layer 1: Heuristic Correlation (Confidence: 0.1-0.4)

// Heuristic correlation example
function correlateByProximity(facts: BaseFact[]): CorrelationEdge[] {
  const edges: CorrelationEdge[] = [];
  
  for (const vulnFact of facts.filter(f => f.type === 'VULNERABILITY')) {
    for (const endpointFact of facts.filter(f => f.type === 'ENDPOINT')) {
      const pathSimilarity = calculatePathSimilarity(
        vulnFact.location.filePath,
        endpointFact.location.filePath
      );
      
      if (pathSimilarity > 0.7) { // Same directory or similar naming
        edges.push({
          source: endpointFact.id,
          target: vulnFact.id,
          relationship: 'heuristic_proximity',
          confidence: 0.2 + (pathSimilarity * 0.2),
          evidence: { pathSimilarity, heuristic: 'file_proximity' }
        });
      }
    }
  }
  
  return edges;
}

Layer 2: Import Graph Analysis (Confidence: 0.5-0.6)

Analyze module dependencies to establish structural relationships between endpoints and vulnerable code.

// Import graph traversal for Python/JavaScript
class ImportGraphAnalyzer {
  buildImportGraph(codebase: string): ImportGraph {
    // Parse import statements across the codebase
    // Build directed graph of module dependencies
  }
  
  findImportPath(sourceFile: string, targetFile: string): ImportPath | null {
    // BFS/DFS to find if sourceFile can reach targetFile
    // Return path: [file1 -> file2 -> ... -> targetFile]
  }
  
  correlateViaImports(endpointFact: EndpointFact, vulnFact: VulnerabilityFact): CorrelationEdge | null {
    const importPath = this.findImportPath(
      endpointFact.location.filePath,
      vulnFact.location.filePath
    );
    
    if (importPath) {
      return {
        source: endpointFact.id,
        target: vulnFact.id,
        relationship: 'has_import_path_to',
        confidence: 0.6,
        evidence: { 
          importPath: importPath.pathSteps,
          transitiveDepth: importPath.length 
        }
      };
    }
    
    return null;
  }
}

Layer 3: High-Fidelity Taint Analysis (Confidence: 0.8-0.9)

The most sophisticated layer: orchestrate existing static analyzers to perform targeted data flow analysis between specific source-sink pairs.

// Dynamic Semgrep rule generation for taint analysis
class TaintAnalysisOrchestrator {
  async validateDataFlow(sourceNode: EndpointFact, sinkNode: VulnerabilityFact): Promise<TaintResult> {
    // Generate custom Semgrep rule for this specific source-sink pair
    const customRule = this.generateTaintRule(sourceNode, sinkNode);
    
    // Execute Semgrep with the generated rule
    const semgrepResult = await this.executeSemgrep(customRule);
    
    if (semgrepResult.findings.length > 0) {
      return {
        hasDataFlow: true,
        confidence: 0.9,
        staticTrace: semgrepResult.findings[0].dataFlowTrace,
        evidence: {
          semgrepRule: customRule,
          traceSteps: semgrepResult.findings[0].dataFlowTrace
        }
      };
    }
    
    return { hasDataFlow: false, confidence: 0.0 };
  }
  
  private generateTaintRule(endpoint: EndpointFact, vuln: VulnerabilityFact): SemgrepRule {
    return {
      id: `custom-taint-${endpoint.id}-to-${vuln.id}`,
      pattern: `
        rules:
          - id: custom-data-flow-check
            mode: taint
            pattern-sources:
              - patterns:
                - pattern: ${endpoint.parameterPattern}  # e.g., request.args.get("user_id")
            pattern-sinks:  
              - patterns:
                - pattern: ${vuln.sinkPattern}          # e.g., cursor.execute(...)
            message: "Data flows from endpoint parameter to vulnerable sink"
      `
    };
  }
}

Correlation Graph Output

At the end of Stage 2, we have a rich graph structure:

  • Nodes: All security facts (vulns, endpoints, packages, configs)
  • Edges: Hypothesized relationships with confidence scores
  • Evidence: Static analysis traces, import paths, heuristic metadata
  • Prioritization: High-confidence edges (>0.7) become validation candidates

Stage 3: Dynamic Validation & Hypothesis Testing

Stage 3 implements an automated penetration testing loop. For each high-confidence hypothesis from Stage 2, the Validation Orchestrator attempts to craft and execute real exploits in sandboxed environments.

Multi-Oracle Evidence Collection

The engine employs three complementary oracles to detect successful exploits, including blind and asynchronous vulnerabilities.

1. Content Oracle

class ContentOracle {
  analyzeResponse(response: HttpResponse, payload: Payload): EvidenceResult {
    const indicators = this.getVulnIndicators(payload.type);
    
    for (const indicator of indicators) {
      if (response.body.includes(indicator.signature)) {
        return {
          success: true,
          evidence: {
            type: 'content_match',
            signature: indicator.signature,
            location: response.body.indexOf(indicator.signature),
            context: this.extractContext(response.body, indicator.signature)
          }
        };
      }
    }
    
    return { success: false };
  }
}

2. Timing Oracle

class TimingOracle {
  async analyzeTimingSignal(endpoint: EndpointFact, payload: Payload): Promise<EvidenceResult> {
    // Establish baseline response time
    const baselineRequests = await Promise.all([
      this.sendRequest(endpoint, { normalPayload: 'test' }),
      this.sendRequest(endpoint, { normalPayload: 'test' }),
      this.sendRequest(endpoint, { normalPayload: 'test' })
    ]);
    
    const baselineTime = this.calculateMedianTime(baselineRequests);
    
    // Send timing-attack payload (e.g., pg_sleep, WAITFOR DELAY)
    const timingRequest = await this.sendRequest(endpoint, payload);
    const payloadTime = timingRequest.responseTime;
    
    const timeDifference = payloadTime - baselineTime;
    
    if (timeDifference > payload.expectedDelay * 0.8) { // 80% threshold
      return {
        success: true,
        evidence: {
          type: 'timing_differential',
          baselineTime,
          payloadTime,
          timeDifference,
          expectedDelay: payload.expectedDelay,
          confidence: Math.min(timeDifference / payload.expectedDelay, 1.0)
        }
      };
    }
    
    return { success: false };
  }
}

3. Out-of-Band Oracle (OAST)

class OASTOracle {
  private oastDomain = 'vxdf-oast.com';
  private callbackServer: CallbackListener;
  
  async generateOASTPayload(vulnType: string): Promise<OASTPayload> {
    const uniqueId = this.generateUniqueId();
    const subdomain = `${uniqueId}.${this.oastDomain}`;
    
    // Register expectation for callback
    this.callbackServer.expectCallback(uniqueId, {
      timeout: 30000, // 30 second timeout
      expectedProtocols: ['DNS', 'HTTP', 'HTTPS']
    });
    
    switch (vulnType) {
      case 'SSRF':
        return {
          payload: `http://${subdomain}/ssrf-test`,
          expectedCallback: { type: 'HTTP', subdomain }
        };
      case 'XXE':
        return {
          payload: `<!ENTITY xxe SYSTEM "http://${subdomain}/xxe-test">&xxe;`,
          expectedCallback: { type: 'HTTP', subdomain }
        };
    }
  }
}

Evidence Fusion: The orchestrator combines results from all oracles to make the final determination. Even if one oracle fails, others might provide confirmation. For maximum confidence, multiple oracles should agree.

Stage 4: Reporting & Enrichment

Stage 4 transforms validated findings into actionable intelligence. The engine produces machine-readable OWASP VXDF JSON reports enriched with CWE mappings, CVSS scores, and concrete remediation guidance.

OWASP VXDF JSON Output Format

{
  "vxdf_version": "1.0",
  "scan_metadata": {
    "scan_id": "scan_20240115_103000",
    "timestamp": "2024-01-15T10:30:00Z",
    "target": {
      "repository": "https://github.com/example/vulnerable-app",
      "commit_sha": "a1b2c3d4e5f6",
      "branch": "main"
    },
    "engine_version": "0.9.2"
  },
  "validated_findings": [
    {
      "finding_id": "vxdf_001",
      "title": "SQL Injection in User Profile Endpoint",
      "severity": "CRITICAL",
      "cvss_score": 9.8,
      "cwe_id": "CWE-89",
      "vulnerability_details": {
        "category": "SQL_INJECTION",
        "location": {
          "file_path": "src/models/user.py",
          "line_range": [45, 47],
          "function": "get_user_profile",
          "code_snippet": "cursor.execute(f\"SELECT * FROM users WHERE id = {user_id}\")"
        },
        "entry_point": {
          "endpoint": "/api/users/{user_id}",
          "method": "GET",
          "parameter": "user_id",
          "parameter_type": "path_parameter"
        }
      },
      "exploit_evidence": {
        "validation_method": "dynamic_exploitation",
        "proof_of_concept": {
          "http_request": {
            "method": "GET",
            "url": "/api/users/1' OR '1'='1",
            "headers": { "Authorization": "Bearer test_token" }
          },
          "http_response": {
            "status_code": 200,
            "response_time_ms": 1250,
            "body_snippet": "{\"users\": [{\"id\": 1, \"username\": \"admin\"}...]"
          }
        },
        "oracle_confirmations": [
          {
            "oracle_type": "content",
            "result": "success",
            "evidence": "Response contained multiple user records"
          },
          {
            "oracle_type": "timing",
            "result": "success", 
            "evidence": "pg_sleep(5) caused 5.2s delay vs 0.1s baseline"
          }
        ],
        "confidence_score": 0.98
      }
    }
  ]
}

Why This Architecture is Revolutionary

Evidence-Driven Analysis

Unlike probabilistic ML approaches, every VXDF finding is backed by concrete exploitation evidence. No black-box scoring—every confidence level has transparent justification through static analysis traces and dynamic validation results.

Best-in-Class Tool Orchestration

Rather than reinventing static analysis, VXDF leverages Semgrep, CodeQL, and other mature tools as specialized components. The innovation lies in intelligent orchestration and correlation across tool boundaries.

Multi-Factor Exploit Validation

Professional penetration testers use content analysis, timing attacks, and out-of-band techniques. VXDF automates these methodologies, detecting even blind and asynchronous vulnerabilities that simple DAST tools miss.

Auditable Confidence Scoring

Confidence scores aren't arbitrary—they reflect cumulative evidence strength. A 0.9 confidence means "static taint analysis confirmed + successful dynamic exploit." Security engineers can inspect the reasoning behind every score.

Performance & Scalability Characteristics

Stage 1-2 (Static)

  • • Linear complexity O(n) in codebase size
  • • Parallelizable across files/modules
  • • ~10-30 seconds for typical web app

Stage 3 (Dynamic)

  • • O(k) in high-confidence hypotheses
  • • Sandboxed execution required
  • • ~2-5 minutes per hypothesis

Overall Pipeline

  • • 95%+ false positive reduction
  • • ~10-15 min end-to-end for most apps
  • • Scales horizontally with container orchestration

Join the Evidence-Based Security Revolution

The VXDF engine represents a fundamental shift from theoretical vulnerability detection to provable exploit validation. This approach has the potential to eliminate alert fatigue, accelerate remediation cycles, and restore developer confidence in security tooling.

Open Source Contribution Opportunities

Parser Development

  • • New SAST tool integrations (SonarQube, Veracode)
  • • Language-specific API discovery engines
  • • Container security scan parsers (Trivy, Clair)

Validation Modules

  • • Framework-specific payload generators
  • • Advanced oracle implementations
  • • Exploit technique libraries

Core Engine

  • • Graph correlation algorithms
  • • Performance optimizations
  • • Distributed processing architecture

Integration & UX

  • • CI/CD pipeline integrations
  • • Dashboard and visualization tools
  • • IDE plugins and developer workflows

Ready to move beyond false positives? Join us in building the future of evidence-based application security.

Next posts in this series: Deep-dive implementation guides, performance benchmarks, and real-world case studies from production deployments.

Join the Conversation

Have thoughts on this architecture? Questions about implementation details? We'd love to hear from the security engineering community.

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Published on June 9, 2025