Three-Layer Defense

1. Temporal Analysis (Midstream)

   • Behavioral pattern detection via temporal-compare
   • Strange-loop detection for manipulation attempts
   • Nanosecond precision with scheduler
   • QUIC-based distributed coordination

2. Vector Intelligence (AgentDB)

   • 150x faster semantic search with HNSW
   • 4-32x memory reduction via quantization
   • Persistent pattern learning
   • Hybrid search (vector + metadata)

3. Formal Verification (lean-agentic)

   • Mathematical proof of safety properties
   • Policy compliance verification
   • Theorem proving for critical decisions
   • Symbolic reasoning integration

Data Flow

User Input → Temporal Analysis → Vector Search → Formal Verification → Decision
     ↓              ↓                  ↓                ↓                ↓
  Normalize    Detect Patterns    Find Similar    Prove Safety    Allow/Block

Rust Implementation

use temporal_compare::{TemporalCompare, ComparisonResult};
use strange_loop::{StrangeLoop, LoopDetector};
use nanosecond_scheduler::{Scheduler, Task};

pub struct AIMDSAnalyzer {
    temporal: TemporalCompare,
    loop_detector: StrangeLoop,
    scheduler: Scheduler,
}

impl AIMDSAnalyzer {
    pub fn new() -> Self {
        Self {
            temporal: TemporalCompare::default(),
            loop_detector: StrangeLoop::new(),
            scheduler: Scheduler::with_precision_ns(100), // 100ns precision
        }
    }

    pub async fn analyze_behavior(&self, events: Vec<Event>) -> AnalysisResult {
        // Schedule temporal analysis
        let task = Task::new(move || {
            // Compare event sequences
            let comparison = self.temporal.compare(&events);

            // Detect manipulation loops
            let loops = self.loop_detector.detect(&events);

            AnalysisResult {
                temporal_anomaly: comparison.deviation > 0.3,
                loop_detected: !loops.is_empty(),
                confidence: comparison.confidence,
            }
        });

        self.scheduler.schedule(task).await
    }
}

TypeScript Bridge

import { spawn } from 'child_process';
import { promisify } from 'util';

export class MidstreamBridge {
  async analyzePattern(events: Event[]): Promise<AnalysisResult> {
    // Call Rust binary via CLI
    const result = await this.execRust('aimds-analyzer', [
      '--events', JSON.stringify(events),
      '--precision', '100ns'
    ]);

    return JSON.parse(result);
  }

  private async execRust(cmd: string, args: string[]): Promise<string> {
    return new Promise((resolve, reject) => {
      const proc = spawn(cmd, args);
      let output = '';

      proc.stdout.on('data', data => output += data);
      proc.on('close', code => {
        if (code === 0) resolve(output);
        else reject(new Error(`Exit code ${code}`));
      });
    });
  }
}

Setup Vector Database

import { AgentDB, VectorSearchOptions } from 'agentdb';
import { z } from 'zod';

const PatternSchema = z.object({
  pattern_id: z.string(),
  category: z.enum(['jailbreak', 'prompt-injection', 'data-leak', 'bias']),
  severity: z.number().min(0).max(1),
  description: z.string(),
  embedding: z.array(z.number())
});

export class PatternDatabase {
  private db: AgentDB;

  constructor() {
    this.db = new AgentDB({
      path: './aimds-patterns',
      quantization: 'int8',
      enableHNSW: true,
      dimension: 1536 // OpenAI embedding size
    });
  }

  async indexPattern(pattern: z.infer<typeof PatternSchema>) {
    await this.db.insert({
      id: pattern.pattern_id,
      vector: pattern.embedding,
      metadata: {
        category: pattern.category,
        severity: pattern.severity,
        description: pattern.description
      }
    });
  }

  async findSimilarPatterns(
    query: number[],
    threshold: number = 0.8
  ): Promise<MatchedPattern[]> {
    const results = await this.db.vectorSearch({
      query,
      k: 20,
      metric: 'cosine',
      filter: (meta) => meta.severity >= threshold
    });

    return results.matches.map(m => ({
      pattern_id: m.id,
      similarity: m.score,
      category: m.metadata.category,
      severity: m.metadata.severity
    }));
  }

  async hybridSearch(query: string, embedding: number[]) {
    // Combine vector similarity + metadata filtering
    return await this.db.query({
      vector: embedding,
      filter: {
        $or: [
          { category: 'jailbreak' },
          { severity: { $gte: 0.7 } }
        ]
      },
      limit: 10
    });
  }
}

Optimized Pattern Learning

export class PatternLearner {
  private db: PatternDatabase;

  async learnFromIncident(incident: SecurityIncident) {
    // Extract features with HNSW indexing (150x faster)
    const embedding = await this.embed(incident.text);

    // Store with quantization (4x memory savings)
    await this.db.indexPattern({
      pattern_id: incident.id,
      category: incident.type,
      severity: incident.impact,
      description: incident.description,
      embedding
    });

    // Update HNSW index
    await this.db.rebuildIndex();
  }

  private async embed(text: string): Promise<number[]> {
    // Use your embedding model (OpenAI, local, etc.)
    // Returns 1536-dim vector for text
    return embedText(text);
  }
}

Define Safety Policies

import { LeanAgenticClient, Theorem } from 'lean-agentic';

export class SafetyVerifier {
  private client: LeanAgenticClient;

  constructor() {
    this.client = new LeanAgenticClient({
      endpoint: process.env.LEAN_ENDPOINT || 'http://localhost:3000',
      verbose: true
    });
  }

  async verifyInput(input: string, context: Context): Promise<VerificationResult> {
    // Define safety theorem
    const theorem: Theorem = {
      name: 'input_safety',
      statement: `
        theorem input_safety (input: Input) (ctx: Context) :
          (no_injection input) ∧
          (policy_compliant input ctx) ∧
          (no_data_leak input) →
          Safe input
      `,
      context: {
        input,
        policies: context.policies,
        history: context.history
      }
    };

    // Attempt proof
    const proof = await this.client.prove(theorem);

    return {
      safe: proof.success,
      confidence: proof.confidence,
      violations: proof.counterexamples || [],
      proof_trace: proof.trace
    };
  }

  async verifyPolicy(policy: Policy): Promise<boolean> {
    // Prove policy consistency
    const theorem = {
      name: 'policy_consistency',
      statement: `
        theorem policy_consistency (p: Policy) :
          (∀ input, decide p input = true ∨ decide p input = false) ∧
          (∀ input, safe_decision p input)
      `
    };

    const proof = await this.client.prove(theorem);
    return proof.success;
  }
}

Integration with Vector Search

export class AIMDSCore {
  private temporal: MidstreamBridge;
  private patterns: PatternDatabase;
  private verifier: SafetyVerifier;

  async evaluateInput(input: string): Promise<Defense> {
    // 1. Temporal analysis
    const temporal = await this.temporal.analyzePattern([
      { type: 'input', content: input, timestamp: Date.now() }
    ]);

    if (temporal.loop_detected) {
      return { action: 'block', reason: 'Manipulation loop detected' };
    }

    // 2. Vector pattern matching
    const embedding = await embedText(input);
    const matches = await this.patterns.findSimilarPatterns(embedding, 0.75);

    if (matches.some(m => m.severity > 0.8)) {
      return { action: 'block', reason: 'High-severity pattern match' };
    }

    // 3. Formal verification
    const verified = await this.verifier.verifyInput(input, {
      policies: this.loadPolicies(),
      history: this.getHistory()
    });

    if (!verified.safe) {
      return {
        action: 'block',
        reason: 'Policy violation',
        violations: verified.violations
      };
    }

    // All checks passed
    return { action: 'allow', confidence: verified.confidence };
  }
}

Setup QUIC Server

use quic_multistream::{QuicServer, StreamHandler};
use tokio::sync::mpsc;

pub struct AIMDSCoordinator {
    server: QuicServer,
    pattern_sync: mpsc::Sender<Pattern>,
}

impl AIMDSCoordinator {
    pub async fn start(&self) -> Result<()> {
        let server = QuicServer::bind("0.0.0.0:4433").await?;

        server.on_stream(|stream| async move {
            // Handle pattern synchronization
            match stream.stream_type() {
                "pattern_update" => {
                    let pattern: Pattern = stream.read_json().await?;
                    self.pattern_sync.send(pattern).await?;
                }
                "verification_request" => {
                    let req: VerifyRequest = stream.read_json().await?;
                    let result = self.verify(req).await?;
                    stream.write_json(&result).await?;
                }
                _ => {}
            }
            Ok(())
        });

        server.serve().await
    }
}

Client-Side Coordination

import { QuicClient } from 'quic-multistream';

export class AIMDSClient {
  private client: QuicClient;

  async connect(coordinatorUrl: string) {
    this.client = await QuicClient.connect(coordinatorUrl);
  }

  async syncPattern(pattern: Pattern) {
    const stream = await this.client.openStream('pattern_update');
    await stream.writeJSON(pattern);
    await stream.close();
  }

  async requestVerification(input: string): Promise<VerificationResult> {
    const stream = await this.client.openStream('verification_request');
    await stream.writeJSON({ input, timestamp: Date.now() });

    const result = await stream.readJSON();
    await stream.close();

    return result;
  }
}

Problem: Slow vector search

Solution:

// Rebuild HNSW index
await db.rebuildIndex();

// Increase HNSW parameters
const db = new AgentDB({
  enableHNSW: true,
  efConstruction: 400, // Higher = better quality
  M: 32                // Higher = better recall
});

Problem: Theorem proving takes too long

Solution:

// Increase timeout
const verifier = new SafetyVerifier({
  timeout_ms: 10000 // 10 seconds
});

// Simplify theorem statement
// Break complex proofs into smaller lemmas

Problem: Cannot establish QUIC connection

Solution:

# Check certificate validity
openssl s_client -connect localhost:4433

# Regenerate self-signed certificate
cargo run --bin generate-cert

# Check firewall rules
sudo ufw allow 4433/udp