Phase 41.5 — SecurityOrchestrator: Unified Adversarial Defense Pipeline & Threat Intelligence

[web3guru888]

Phase 41.5 — SecurityOrchestrator

Overview

The SecurityOrchestrator is the capstone of Phase 41, unifying all adversarial robustness components into a comprehensive security intelligence platform. It orchestrates attack simulation (41.1), robustness verification (41.2), adversarial training (41.3), and input sanitization (41.4) into an automated defense pipeline with threat detection, incident response, and continuous robustness monitoring.

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                      SecurityOrchestrator (41.5)                     │
│                                                                      │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │                    Threat Intelligence Engine                  │   │
│  │  Attack pattern analysis • Threat scoring • Alert generation  │   │
│  └──────────────────────────┬───────────────────────────────────┘   │
│                              │                                       │
│  ┌──────────┬────────────────┼────────────────┬──────────────────┐  │
│  │          │                │                │                  │  │
│  │  ┌───────▼──────┐ ┌──────▼───────┐ ┌──────▼──────┐ ┌────────▼┐ │
│  │  │ Attack       │ │ Robustness   │ │ Adversarial │ │  Input  │ │
│  │  │ Simulator    │ │ Verifier     │ │ Trainer     │ │Sanitizer│ │
│  │  │ (41.1)       │ │ (41.2)       │ │ (41.3)      │ │ (41.4)  │ │
│  │  └──────┬───────┘ └──────┬───────┘ └──────┬──────┘ └────┬───┘ │
│  │         │                │                │             │      │
│  └─────────┼────────────────┼────────────────┼─────────────┼──────┘  │
│            │                │                │             │         │
│  ┌─────────▼────────────────▼────────────────▼─────────────▼──────┐  │
│  │                   Defense Coordination Layer                    │  │
│  │                                                                │  │
│  │  ┌────────────────┐ ┌─────────────────┐ ┌──────────────────┐  │  │
│  │  │ Red Team       │ │ Blue Team       │ │ Purple Team      │  │  │
│  │  │ Automation     │ │ Defense Chain   │ │ Continuous       │  │  │
│  │  │                │ │                 │ │ Assessment       │  │  │
│  │  │ Scheduled      │ │ Input → Detect  │ │                  │  │  │
│  │  │ attack runs    │ │ → Sanitize →    │ │ Attack ↔ Defend  │  │  │
│  │  │ Model probing  │ │ Classify →      │ │ feedback loop    │  │  │
│  │  │ Vuln scanning  │ │ Monitor         │ │ Drift detection  │  │  │
│  │  └────────────────┘ └─────────────────┘ └──────────────────┘  │  │
│  └────────────────────────────────────────────────────────────────┘  │
│                                                                      │
│  ┌──────────────────────────────────────────────────────────────┐   │
│  │                   Security Dashboard & Reporting              │   │
│  │  Robustness scores • Attack logs • Certification status       │   │
│  │  Incident timeline • Model risk assessment • Compliance       │   │
│  └──────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────┘

TypeScript Interface

import { Model, Tensor, DataLoader } from '@asi-build/core';
import { AdversarialAttackSimulator, AttackConfig, RobustnessReport } from './attack-simulator';
import { RobustnessVerifier, CertificationReport } from './robustness-verifier';
import { AdversarialTrainer, AdversarialTrainingConfig, TrainingMetrics } from './adversarial-trainer';
import { InputSanitizer, SanitizationConfig, DetectionReport } from './input-sanitizer';

type ThreatLevel = 'critical' | 'high' | 'medium' | 'low' | 'info';

interface SecurityPolicy {
  name: string;
  minRobustAccuracy: number;          // minimum required robust accuracy
  minCertifiedRadius: number;          // minimum certified L2 radius
  maxFalsePositiveRate: number;        // max detection FPR
  requiredAttackSuites: string[];      // attacks that must be evaluated
  retrainingTrigger: number;           // robust accuracy drop to trigger retraining
  alertThresholds: Map<ThreatLevel, number>;
}

interface ThreatEvent {
  id: string;
  timestamp: Date;
  threatLevel: ThreatLevel;
  attackType: string;
  affectedModel: string;
  description: string;
  perturbationNorm: number;
  confidence: number;
  mitigationApplied: string;
  resolved: boolean;
}

interface SecurityAssessment {
  modelId: string;
  timestamp: Date;
  overallScore: number;                // 0-100 security score
  
  // Attack assessment
  empiricalRobustness: RobustnessReport[];
  
  // Certification
  certificationReport: CertificationReport;
  
  // Detection capability
  detectionReport: DetectionReport;
  
  // Threat events
  recentThreats: ThreatEvent[];
  
  // Recommendations
  recommendations: string[];
  
  // Compliance
  policyCompliance: Map<string, boolean>;
}

interface RetrainingRecommendation {
  trigger: string;                     // what triggered the recommendation
  currentRobustAccuracy: number;
  targetRobustAccuracy: number;
  suggestedMethod: string;
  suggestedConfig: AdversarialTrainingConfig;
  estimatedTrainingTime: number;
  priority: ThreatLevel;
}

interface SecurityOrchestrator {
  // Full security assessment
  assessModel(model: Model, testData: DataLoader, policy: SecurityPolicy): Promise<SecurityAssessment>;
  
  // Red team automation
  runRedTeam(model: Model, testData: DataLoader, attacks: AttackConfig[]): Promise<RobustnessReport[]>;
  scheduleRedTeam(model: Model, cronSchedule: string, config: AttackConfig[]): string;
  
  // Blue team defense chain
  deployDefenseChain(model: Model, sanitizerConfig: SanitizationConfig): DefenseChain;
  
  // Purple team (attack-defense feedback loop)
  runPurpleTeam(model: Model, trainData: DataLoader, testData: DataLoader, iterations: number): AsyncIterable<SecurityAssessment>;
  
  // Continuous monitoring
  monitorInference(model: Model, input: Tensor): Promise<{prediction: Tensor, threats: ThreatEvent[]}>;
  
  // Adaptive retraining
  recommendRetraining(assessment: SecurityAssessment, policy: SecurityPolicy): RetrainingRecommendation | null;
  executeRetraining(model: Model, trainData: DataLoader, recommendation: RetrainingRecommendation): AsyncIterable<TrainingMetrics>;
  
  // Threat intelligence
  getThreats(filter?: {level?: ThreatLevel, since?: Date}): Promise<ThreatEvent[]>;
  analyzeThreatPatterns(events: ThreatEvent[]): Promise<{patterns: string[], trend: string}>;
  
  // Reporting
  generateReport(assessment: SecurityAssessment): Promise<string>;
  
  // Policy management
  setPolicy(policy: SecurityPolicy): void;
  validateCompliance(assessment: SecurityAssessment): Map<string, boolean>;
}

Configuration

security_orchestrator:
  policy:
    name: "production_standard"
    min_robust_accuracy: 0.40
    min_certified_radius: 0.25
    max_false_positive_rate: 0.05
    required_attack_suites: ["autoattack", "pgd", "cw"]
    retraining_trigger: 0.05       # 5% robust accuracy drop
    alert_thresholds:
      critical: 0.20               # robust accuracy < 20%
      high: 0.30
      medium: 0.40
      low: 0.50
  
  red_team:
    schedule: "0 0 * * 0"          # weekly
    attacks:
      - method: "autoattack"
        norm: "Linf"
        epsilon: 0.031373
      - method: "pgd"
        norm: "L2"
        epsilon: 0.5
      - method: "cw"
        norm: "L2"
        confidence: 0
    sample_size: 1000
  
  blue_team:
    defense_chain:
      - "input_sanitizer"
      - "feature_squeezing_detect"
      - "model_inference"
      - "output_verification"
    fallback_model: "ensemble"
  
  purple_team:
    iterations: 5
    attack_budget_increase: 1.2     # 20% harder each round
    retraining_epochs: 50
    
  monitoring:
    enabled: true
    detection_methods: ["feature_squeezing", "ensemble_disagreement"]
    alert_on_threat: true
    log_all_detections: true
    burst_threshold: 0.10           # 10% adversarial rate
    burst_window_seconds: 300
  
  reporting:
    format: "markdown"
    include_visualizations: true
    retention_days: 90

Orchestration Pipeline

class SecurityOrchestrator:
    """Unified adversarial robustness orchestration pipeline."""
    
    def __init__(self, attacker, verifier, trainer, sanitizer, policy):
        self.attacker = attacker       # Phase 41.1
        self.verifier = verifier       # Phase 41.2
        self.trainer = trainer         # Phase 41.3
        self.sanitizer = sanitizer     # Phase 41.4
        self.policy = policy
        self.threat_log = []
    
    async def assess_model(self, model, test_loader) -> SecurityAssessment:
        """Full security assessment of a model."""
        # 1. Empirical robustness via red team attacks
        robustness_reports = await self.run_red_team(model, test_loader)
        
        # 2. Certified robustness via formal verification
        cert_report = await self.verifier.batch_certify(
            model, test_loader, self.policy.certification_config
        )
        
        # 3. Detection capability evaluation
        adv_data = self._generate_adversarial_set(model, test_loader)
        detection_report = await self.sanitizer.evaluate_detection(
            test_loader, adv_data, model
        )
        
        # 4. Compute overall security score
        score = self._compute_security_score(
            robustness_reports, cert_report, detection_report
        )
        
        # 5. Generate recommendations
        recommendations = self._generate_recommendations(score, robustness_reports)
        
        return SecurityAssessment(
            model_id=model.id, score=score,
            empirical_robustness=robustness_reports,
            certification_report=cert_report,
            detection_report=detection_report,
            recommendations=recommendations
        )
    
    async def run_purple_team(self, model, train_loader, test_loader, iterations=5):
        """Attack-defense feedback loop for continuous hardening."""
        for i in range(iterations):
            # Attack phase
            assessment = await self.assess_model(model, test_loader)
            yield assessment
            
            # Check if retraining needed
            recommendation = self.recommend_retraining(assessment)
            if recommendation:
                # Defense phase: adversarial retraining
                async for metrics in self.trainer.train(
                    model, train_loader, recommendation.config
                ):
                    pass  # training progresses
                
                # Re-assess after retraining
                assessment = await self.assess_model(model, test_loader)
                yield assessment
    
    def _compute_security_score(self, rob_reports, cert_report, det_report):
        """Weighted security score (0-100)."""
        empirical_score = min(r.robust_accuracy for r in rob_reports) * 100
        certified_score = cert_report.certified_accuracy * 100
        detection_score = det_report.auroc * 100
        
        return (0.4 * empirical_score + 
                0.3 * certified_score + 
                0.3 * detection_score)

Testing Strategy

describe('SecurityOrchestrator', () => {
  describe('Full Assessment', () => {
    it('should produce comprehensive security assessment', async () => {
      const assessment = await orchestrator.assessModel(model, testLoader, policy);
      expect(assessment.overallScore).toBeGreaterThanOrEqual(0);
      expect(assessment.overallScore).toBeLessThanOrEqual(100);
      expect(assessment.empiricalRobustness.length).toBeGreaterThan(0);
      expect(assessment.certificationReport).toBeDefined();
      expect(assessment.detectionReport).toBeDefined();
    });
  });
  
  describe('Red Team', () => {
    it('should run all required attack suites from policy', async () => {
      const reports = await orchestrator.runRedTeam(model, testLoader, policy.requiredAttackSuites);
      expect(reports.length).toBe(policy.requiredAttackSuites.length);
    });
  });
  
  describe('Purple Team', () => {
    it('should improve robustness over iterations', async () => {
      const assessments: SecurityAssessment[] = [];
      for await (const a of orchestrator.runPurpleTeam(model, trainLoader, testLoader, 3)) {
        assessments.push(a);
      }
      const first = assessments[0].overallScore;
      const last = assessments[assessments.length - 1].overallScore;
      expect(last).toBeGreaterThanOrEqual(first);
    });
  });
  
  describe('Monitoring', () => {
    it('should detect adversarial inputs during inference', async () => {
      const result = await orchestrator.monitorInference(model, advInput);
      expect(result.threats.length).toBeGreaterThan(0);
    });
    
    it('should not flag clean inputs', async () => {
      const result = await orchestrator.monitorInference(model, cleanInput);
      expect(result.threats.length).toBe(0);
    });
  });
  
  describe('Policy Compliance', () => {
    it('should identify policy violations', async () => {
      const assessment = await orchestrator.assessModel(weakModel, testLoader, strictPolicy);
      const compliance = orchestrator.validateCompliance(assessment);
      expect(compliance.get('min_robust_accuracy')).toBe(false);
    });
  });
  
  describe('Retraining', () => {
    it('should recommend retraining when robustness drops', async () => {
      const assessment = { overallScore: 25, empiricalRobustness: [{ robustAccuracy: 0.15 }] };
      const rec = orchestrator.recommendRetraining(assessment, policy);
      expect(rec).not.toBeNull();
      expect(rec.priority).toBe('critical');
    });
  });
});

Acceptance Criteria

• Full security assessment completes in <30 minutes for standard benchmarks
• Red team automation runs all policy-required attack suites
• Purple team loop measurably improves robustness over 3+ iterations
• Real-time monitoring detects adversarial inputs with <50ms latency
• Policy compliance validation correctly identifies violations
• Retraining recommendations include appropriate method and configuration
• Security reports generated in markdown with robustness curves
• Threat intelligence identifies attack pattern trends
• All sub-components (41.1–41.4) correctly integrated and orchestrated
• Dashboard exposes security scores, threat timeline, and compliance status

References

• Goodfellow et al. (2015) — FGSM
• Madry et al. (2018) — PGD Adversarial Training
• Carlini & Wagner (2017) — C&W Attack
• Cohen et al. (2019) — Randomized Smoothing
• Zhang et al. (2019) — TRADES
• Xu et al. (2018) — Feature Squeezing
• Croce & Hein (2020) — AutoAttack
• Tramèr et al. (2020) — Adaptive Attacks
• Biggio & Roli (2018) — Wild Patterns Survey

---

Capstone of Phase 41 — Adversarial Robustness & Security Intelligence. Integrates #825 (41.1), #826 (41.2), #827 (41.3), #828 (41.4).

[web3guru888]

🚀 Phase 42 Launched — Distributed Systems & Fault-Tolerant AI Infrastructure

Building on Phase 41's adversarial robustness, Phase 42 implements the distributed systems layer:

Sub-Phase	Component	Issue
42.1	ConsensusEngine — Raft/Paxos/PBFT consensus	#841
42.2	ShardManager — Data/model sharding, ZeRO partitioning	#842
42.3	FaultDetector — φ-accrual failure detection, gossip	#843
42.4	CheckpointManager — Chandy-Lamport snapshots, WAL	#844
42.5	InfrastructureOrchestrator — Borg-style scheduling, auto-scaling	#845

Academic foundation: Lamport (1998) Paxos, Ongaro & Ousterhout (2014) Raft, Castro & Liskov (1999) PBFT, Chandy & Lamport (1985) distributed snapshots, Verma et al. (2015) Borg, Rajbhandari et al. (2020) ZeRO, Narayanan et al. (2021) Megatron-LM

Planning: #840 | Wiki: 262 pages | Discussions: 580+