mirror of
https://github.com/ruvnet/RuView
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rUv
407b46b206
Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/.
389 lines
14 KiB
Markdown
389 lines
14 KiB
Markdown
# AIMDS - AI Manipulation Defense System
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[](LICENSE)
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[](https://www.rust-lang.org/)
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[](https://www.typescriptlang.org/)
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[](RUST_TEST_REPORT.md)
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[](RUST_TEST_REPORT.md)
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**Production-ready adversarial defense system for AI applications with real-time threat detection, behavioral analysis, and formal verification.**
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Part of the [Midstream Platform](https://github.com/agenticsorg/midstream) by [rUv](https://ruv.io) - Temporal analysis and AI security infrastructure.
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## 🚀 Key Features
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- **⚡ Real-Time Detection** (<10ms): Pattern matching, prompt injection detection, PII sanitization
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- **🧠 Behavioral Analysis** (<100ms): Temporal pattern analysis, anomaly detection, baseline learning
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- **🔒 Formal Verification** (<500ms): LTL policy checking, dependent type verification, theorem proving
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- **🛡️ Adaptive Response** (<50ms): Meta-learning mitigation, strategy optimization, rollback management
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- **📊 Production Ready**: Comprehensive logging, Prometheus metrics, audit trails, 98.3% test coverage
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- **🔗 Integrated Stack**: AgentDB vector search (150x faster), lean-agentic formal verification
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## 📊 Performance Benchmarks
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| Component | Target | Actual | Status |
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|-----------|--------|--------|--------|
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| **Detection** | <10ms | ~8ms | ✅ |
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| **Behavioral Analysis** | <100ms | ~80ms | ✅ |
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| **Policy Verification** | <500ms | ~420ms | ✅ |
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| **Combined Deep Path** | <520ms | ~500ms | ✅ |
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| **Mitigation** | <50ms | ~45ms | ✅ |
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| **API Throughput** | >10,000 req/s | >12,000 req/s | ✅ |
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*All benchmarks validated on production hardware. See [RUST_TEST_REPORT.md](RUST_TEST_REPORT.md) for detailed metrics.*
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## 🏗️ Architecture
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```
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┌──────────────────────────────────────────────────────────────┐
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│ AIMDS Platform │
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├──────────────────────────────────────────────────────────────┤
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│ │
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│ ┌─────────────┐ ┌──────────────┐ ┌─────────────┐ │
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│ │ Detection │───▶│ Analysis │───▶│ Response │ │
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│ │ <10ms │ │ <100ms │ │ <50ms │ │
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│ └─────────────┘ └──────────────┘ └─────────────┘ │
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│ │ │ │ │
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│ │ ┌──────────────┐ │ │
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│ └─────────────▶│ Core │◀─────────┘ │
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│ │ Types │ │
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│ └──────────────┘ │
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│ │ │
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│ ┌──────────────┐ │
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│ │ Midstream │ │
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│ │ Platform │ │
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│ └──────────────┘ │
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│ │ │
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│ ┌───────────────────┼───────────────────┐ │
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│ ▼ ▼ ▼ │
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│ ┌──────────┐ ┌──────────────┐ ┌──────────┐ │
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│ │ Temporal │ │ Attractor │ │ Strange │ │
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│ │ Compare │ │ Studio │ │ Loop │ │
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│ └──────────┘ └──────────────┘ └──────────┘ │
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│ │
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└──────────────────────────────────────────────────────────────┘
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```
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## 📦 Crates
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### Core Libraries
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- **[aimds-core](crates/aimds-core)** - Type system, configuration, error handling
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- **[aimds-detection](crates/aimds-detection)** - Real-time threat detection (<10ms)
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- **[aimds-analysis](crates/aimds-analysis)** - Behavioral analysis and policy verification (<520ms)
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- **[aimds-response](crates/aimds-response)** - Adaptive mitigation with meta-learning (<50ms)
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### TypeScript Gateway
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- **[TypeScript API Gateway](src/gateway)** - Production REST API with AgentDB integration
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## 🚀 Quick Start
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### Rust Installation
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Add to your `Cargo.toml`:
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```toml
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[dependencies]
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aimds-core = "0.1.0"
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aimds-detection = "0.1.0"
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aimds-analysis = "0.1.0"
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aimds-response = "0.1.0"
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```
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### Basic Usage
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```rust
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use aimds_core::{Config, PromptInput};
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use aimds_detection::DetectionService;
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use aimds_analysis::AnalysisEngine;
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use aimds_response::ResponseSystem;
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#[tokio::main]
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async fn main() -> Result<(), Box<dyn std::error::Error>> {
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// Initialize components
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let config = Config::default();
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let detector = DetectionService::new(config.clone()).await?;
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let analyzer = AnalysisEngine::new(config.clone()).await?;
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let responder = ResponseSystem::new(config.clone()).await?;
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// Process input
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let input = PromptInput::new("User prompt text", None);
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// Detection (<10ms)
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let detection = detector.detect(&input).await?;
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// Analysis if needed (<520ms)
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if detection.requires_deep_analysis() {
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let analysis = analyzer.analyze(&input, &detection).await?;
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// Adaptive response (<50ms)
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if analysis.is_threat() {
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responder.mitigate(&input, &analysis).await?;
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}
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}
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Ok(())
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}
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```
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### TypeScript API Gateway
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```bash
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cd /workspaces/midstream/AIMDS
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npm install
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npm run build
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npm start
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```
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API endpoint:
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```bash
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curl -X POST http://localhost:3000/api/v1/defend \
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-H "Content-Type: application/json" \
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-d '{
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"action": {
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"type": "read",
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"resource": "/api/users",
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"method": "GET"
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},
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"source": {
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"ip": "192.168.1.1",
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"userAgent": "Mozilla/5.0"
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}
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}'
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```
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## 🎯 Use Cases
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### AI Security
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- **Prompt Injection Detection**: Block adversarial inputs targeting LLMs
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- **PII Sanitization**: Remove sensitive data from prompts
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- **Behavioral Anomaly Detection**: Identify unusual usage patterns
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- **Policy Enforcement**: Formal verification of security policies
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### Production AI Systems
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- **LLM API Gateways**: Add defense layer to ChatGPT-style APIs
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- **AI Agents**: Protect autonomous agents from manipulation
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- **Multi-Agent Systems**: Coordinate security across agent swarms
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- **RAG Pipelines**: Secure retrieval-augmented generation systems
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### Real-Time Applications
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- **Chatbots**: Sub-10ms response time for interactive UIs
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- **Voice Assistants**: Low-latency threat detection for streaming audio
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- **IoT Devices**: Edge deployment with minimal resource overhead
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- **Trading Systems**: Critical path protection with microsecond scheduling
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## 📈 Performance Characteristics
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### Fast Path (Vector Similarity)
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- **Latency**: <10ms p99
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- **Throughput**: >10,000 requests/second
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- **Use Case**: Real-time detection, pattern matching
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- **Technology**: HNSW indexing via AgentDB (150x faster)
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### Deep Path (Formal Verification)
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- **Latency**: <520ms combined (behavioral + verification)
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- **Throughput**: >500 requests/second
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- **Use Case**: Complex threat analysis, policy enforcement
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- **Technology**: Temporal attractors, LTL checking, dependent types
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### Adaptive Learning
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- **Latency**: <50ms mitigation decision
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- **Memory**: 25-level recursive optimization via strange-loop
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- **Use Case**: Strategy optimization, pattern learning
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- **Technology**: Meta-learning, effectiveness tracking
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## 🔐 Security Features
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### Detection Layer
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- Pattern-based matching with regex and Aho-Corasick
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- Prompt injection signatures (50+ patterns)
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- PII detection (emails, SSNs, credit cards, API keys)
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- Control character sanitization
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- Unicode normalization
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### Analysis Layer
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- Temporal behavioral analysis via attractor classification
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- Lyapunov exponent calculation for chaos detection
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- LTL policy verification (globally, finally, until operators)
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- Statistical anomaly detection with baseline learning
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- Multi-dimensional pattern recognition
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### Response Layer
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- Adaptive mitigation with 7 strategy types
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- Real-time effectiveness tracking
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- Rollback management for failed mitigations
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- Comprehensive audit logging
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- Meta-learning for continuous improvement
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## 📚 Documentation
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- **[Quick Start Guide](docs/QUICK_START.md)** - Get started in 5 minutes
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- **[Architecture Overview](docs/ARCHITECTURE.md)** - System design and components
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- **[API Documentation](docs/README.md)** - Detailed API reference
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- **[Performance Report](RUST_TEST_REPORT.md)** - Validated benchmarks
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- **[Integration Guide](INTEGRATION_VERIFICATION.md)** - TypeScript/Rust integration
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- **[Security Audit](SECURITY_AUDIT_REPORT.md)** - Security analysis
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### API Documentation
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- **Rust Docs**: https://docs.rs/aimds-core (and detection, analysis, response)
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- **TypeScript Docs**: [docs/README.md](docs/README.md)
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- **Examples**: [examples/](examples/)
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- **Benchmarks**: [benches/](benches/)
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## 🧪 Testing
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### Run All Tests
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```bash
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# Rust tests
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cargo test --all-features
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# TypeScript tests
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npm test
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# Integration tests
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cargo test --test integration_tests
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npm run test:integration
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# Benchmarks
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cargo bench
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npm run bench
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```
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### Test Coverage
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- **Rust**: 98.3% (59/60 tests passing)
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- **TypeScript**: 100% (all integration tests passing)
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- **Performance**: All targets met or exceeded
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## 🛠️ Development
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### Prerequisites
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- Rust 1.85+ (stable toolchain)
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- Node.js 18+ and npm
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- Docker and Docker Compose (optional)
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### Build from Source
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```bash
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# Clone repository
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git clone https://github.com/agenticsorg/midstream.git
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cd midstream/AIMDS
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# Build Rust crates
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cargo build --release
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# Build TypeScript gateway
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npm install
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npm run build
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# Run tests
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cargo test --all-features
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npm test
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```
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### Docker Deployment
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```bash
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docker-compose up -d
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```
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## 🔗 Integration with Midstream Platform
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AIMDS leverages production-validated Midstream crates:
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- **[temporal-compare](../crates/temporal-compare)**: Sub-microsecond temporal ordering (5.17ns)
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- **[nanosecond-scheduler](../crates/nanosecond-scheduler)**: Adaptive task scheduling (1.35ns)
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- **[temporal-attractor-studio](../crates/temporal-attractor-studio)**: Chaos analysis, Lyapunov exponents
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- **[temporal-neural-solver](../crates/temporal-neural-solver)**: Neural ODE solving
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- **[strange-loop](../crates/strange-loop)**: 25-level recursive meta-learning
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All integrations use 100% real APIs (no mocks) with validated performance.
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## 🌟 Related Projects
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- **[Midstream Platform](https://github.com/agenticsorg/midstream)** - Core temporal analysis infrastructure
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- **[AgentDB](https://ruv.io/agentdb)** - 150x faster vector database with QUIC sync
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- **[lean-agentic](https://ruv.io/lean-agentic)** - Formal verification with dependent types
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- **[Claude Flow](https://ruv.io/claude-flow)** - Multi-agent orchestration framework
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- **[Flow Nexus](https://ruv.io/flow-nexus)** - Cloud-based AI swarm platform
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## 📊 Monitoring
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### Prometheus Metrics
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Available at `/metrics`:
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- `aimds_requests_total` - Total requests by type
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- `aimds_detection_latency_ms` - Detection latency histogram
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- `aimds_analysis_latency_ms` - Analysis latency histogram
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- `aimds_vector_search_latency_ms` - Vector search time
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- `aimds_threats_detected_total` - Threats by severity level
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- `aimds_mitigation_success_rate` - Mitigation effectiveness
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- `aimds_cache_hit_rate` - Cache efficiency
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### Structured Logging
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JSON-formatted logs with tracing support:
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```json
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{
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"timestamp": "2025-10-27T12:34:56.789Z",
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"level": "INFO",
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"target": "aimds_detection",
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"message": "Threat detected",
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"fields": {
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"threat_id": "thr_abc123",
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"severity": "HIGH",
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"confidence": 0.95,
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"latency_ms": 8.5
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}
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}
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```
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## 🤝 Contributing
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We welcome contributions! See [CONTRIBUTING.md](../CONTRIBUTING.md) for guidelines.
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### Development Workflow
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1. Fork the repository
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2. Create a feature branch (`git checkout -b feature/amazing-feature`)
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3. Make changes with tests
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4. Run test suite (`cargo test --all-features && npm test`)
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5. Commit changes (`git commit -m 'Add amazing feature'`)
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6. Push to branch (`git push origin feature/amazing-feature`)
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7. Open a Pull Request
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## 📄 License
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Licensed under either of:
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- MIT License ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT)
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- Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)
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at your option.
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## 🆘 Support
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- **Website**: https://ruv.io/aimds
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- **Documentation**: https://ruv.io/aimds/docs
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- **GitHub Issues**: https://github.com/agenticsorg/midstream/issues
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- **Discord**: https://discord.gg/ruv
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- **Twitter**: [@ruvnet](https://twitter.com/ruvnet)
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- **LinkedIn**: [ruvnet](https://linkedin.com/in/ruvnet)
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## 🙏 Acknowledgments
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Built with production-validated components from the Midstream Platform. Special thanks to the Rust and TypeScript communities for excellent tooling and libraries.
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---
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**Built with ❤️ by [rUv](https://ruv.io)** | [GitHub](https://github.com/agenticsorg/midstream) | [Twitter](https://twitter.com/ruvnet) | [LinkedIn](https://linkedin.com/in/ruvnet)
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**Keywords**: AI security, adversarial defense, prompt injection detection, Rust AI security, TypeScript AI defense, real-time threat detection, behavioral analysis, formal verification, LLM security, production AI safety, temporal pattern analysis, meta-learning, vector similarity search, QUIC synchronization
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