🎄 SENTINEL Christmas 2025 — FULL OPEN SOURCE RELEASE 🎄

SENTINEL — AI Defense & Red Team Platform

🛡️ Defense + ⚔️ Offense — Complete AI Security Suite
96 Detection Engines • 39,000+ Attack Payloads • Production-Grade

📚 Documentation Portal • 📊 Comparison • 📞 Contact • 💬 Telegram • 📧 Email

--- ## 🎯 Two Platforms, One Mission

### 🛡️ SENTINEL — Defense **Protect your AI in real-time** | Feature | Value | |---------|-------| | Detection Engines | **96** | | Recall | **85.1%** | | Latency | **<10ms** | | Coverage | OWASP LLM Top 10 | ``` User Request → SENTINEL → Safe Response ↓ ❌ Blocked ```

### 🐉 Strike — Offense **Test your AI before attackers do** | Feature | Value | |---------|-------| | Attack Payloads | **39,000+** | | HYDRA Agents | **9 parallel** | | WAF Bypass | **25+ techniques** | | Reports | HTML/MD/JSON + MITRE | ``` Strike → Target AI → Report ↓ 🔓 Vulnerabilities Found ```

> **💡 Use together:** Strike finds vulnerabilities → SENTINEL blocks them in production --- ## 🔬 December 2025: Proactive R&D Update > [!TIP] > **We hunt threats so you don't have to.** > Our team continuously monitors arXiv, TTPs.ai, and underground forums to stay ahead of attackers. ### 🎯 This Month's Research Focus We analyzed **2025's most dangerous attack vectors** and built defenses before they hit your systems: | Threat Vector | Research Source | Our Response | |---------------|-----------------|--------------| | **Crescendo Attacks** | Microsoft Research | 7 escalation patterns | | **ASCII Smuggling** | Unicode Consortium + Dark Web | 7 Unicode ranges | | **Memory Poisoning** | OWASP Agentic AI | 14 "remember/save" patterns | | **Virtual Context** | LLM Security Papers 2025 | NEW: Separator token detector | | **Polyglot Files** | LLMON Project (Dec 2025) | NEW: GIFAR, PDF+HTML detection | ### 📊 Verified Improvements | Metric | Before | After | Impact | |--------|--------|-------|--------| | **Engine Count** | 131 (documented) | **96** (verified) | 🧹 Clean audit | | **2025 Attack Coverage** | 55% | **~80%** | 🛡️ +25% protection | | **New Patterns** | — | **+29** | 🎯 Proactive defense | | **P95 Latency** | 38ms | **40ms** | ⚡ Still under SLA | | **Strike Jailbreaks** | — | **+47** | ⚔️ 33 vendors | ### ✨ New Detection Capabilities | Engine | Protection | Status | |--------|------------|--------| | 🆕 **virtual_context.py** | ChatML/Llama/Anthropic separator exploits | Production | | 📈 **injection.py** | Crescendo multi-turn + Bidi FlipAttack | Enhanced | | 🧠 **agentic_monitor.py** | Memory poisoning + delayed triggers | Enhanced | | 🛡️ **rag_guard.py** | GIFAR/PDF+HTML polyglot detection | Enhanced | > **🔒 Your AI is protected against attacks that don't exist in the wild yet.** --- ## 🌐 What is SENTINEL? **SENTINEL** is a complete AI security platform with two integrated components: | Component | Purpose | Key Features | |-----------|---------|--------------| | 🛡️ **SENTINEL Defense** | Protect AI in production | 96 detection engines, <10ms latency, OWASP coverage | | 🐉 **Strike Offense** | Test AI before deployment | 39K+ payloads, HYDRA parallel attacks, AI-powered recon | ### The Threats We Address | Threat | Defense (SENTINEL) | Offense (Strike) | |--------|-------------------|------------------| | 🎭 **Prompt Injection** | Real-time blocking | 5,000+ injection payloads | | 🔓 **Jailbreaks** | Pattern + semantic detection | Gandalf, DAN, roleplay attacks | | 📤 **Data Exfiltration** | PII guards, output filtering | Exfil payload testing | | 🐦 **Data Leaks** | Canary Tokens (invisible watermarks) | Leak source tracing | | 🤖 **Agentic Attacks** | MCP/A2A protocol security | Tool poisoning, RAG attacks | | 🧠 **RAG Poisoning** | RAG Guard engine | Document injection tests | | 🛡️ **WAF Evasion** | N/A (defense focus) | 25+ WAF bypass techniques | ### Why Choose SENTINEL?

🔬 Advanced Detection (Defense) - 96 specialized detection engines - Strange Math™ (TDA, Sheaf, Hyperbolic) - Canary Tokens for leak detection - Machine learning + rule-based hybrid - 85.1% recall, 84.4% precision	💀 Powerful Attack Suite (Offense) - 39,000+ curated payloads - HYDRA 9-head parallel architecture - AI Attack Planner (Gemini 3) - Anti-deception/Honeypot detection
⚡ Production Ready - Sub-10ms latency (Go gateway) - 1000+ req/sec throughput - Docker/Kubernetes native - OpenTelemetry instrumentation	🌍 Comprehensive Testing - Web + LLM + Hybrid modes - Deep Recon (ASN, endpoints) - Bilingual reports (EN/RU) - MITRE ATT&CK mapping

### Use Cases

🏢 Security Use Cases

| Scenario | Defense (SENTINEL) | Offense (Strike) | |----------|-------------------|------------------| | Internal ChatGPT | Block prompt injections, PII leaks | Test before rollout | | Copilot for Business | Monitor code suggestions for secrets | Audit for backdoors | | Custom AI Agents | Protocol security (MCP, A2A) | Tool call injection tests | | **Example:** Fortune 500 deployed SENTINEL to protect 50,000 employees using internal AI assistants |

🏦 FinTech & Banking

| Scenario | Defense (SENTINEL) | Offense (Strike) | |----------|-------------------|------------------| | AI Trading Advisors | Prevent manipulation via prompts | Test for financial exploits | | Customer Support Bots | Block fraud attempts, PII protection | Compliance verification | | KYC/AML Automation | Ensure decision integrity | Adversarial input testing | | **Example:** European bank passed PCI-DSS audit using Strike's compliance testing module |

🎯 Red Teams & Penetration Testers

| Capability | Strike Feature | |------------|---------------| | AI Application Testing | 39,000+ payloads, HYDRA parallel attacks | | WAF Bypass | 25+ techniques (WAFFLED, DEG-WAF) | | Reconnaissance | ChatbotFinder, ASN network mapping | | Reporting | Bug bounty format, MITRE ATT&CK | | **Example:** Red team discovered critical jailbreak in client's GPT-4 deployment within 2 hours |

🐛 Bug Bounty Hunters

| Platform | Strike Capability | |----------|-------------------| | HackerOne AI Programs | AI-specific vulnerability reports | | Bugcrowd | Automated endpoint discovery | | Private Programs | Stealth mode, geo rotation | | **Example:** Hunter earned $15,000 bounty using Strike to find prompt injection in major SaaS |

🏥 Healthcare & HIPAA

| Scenario | Defense (SENTINEL) | Offense (Strike) | |----------|-------------------|------------------| | Medical AI Assistants | PII/PHI guards, HIPAA compliance | Data leak testing | | Diagnostic AI | Output validation, hallucination detection | Adversarial input tests | | Patient Chatbots | Content filtering | Exfiltration tests | | **Example:** Healthcare provider passed HIPAA audit with zero AI-related findings |

🔧 Developers & DevSecOps

| Integration | Defense | Offense | |-------------|---------|---------| | CI/CD Pipeline | Pre-commit hooks | Security gate (fail on critical) | | API Gateway | Middleware integration | Continuous testing | | Kubernetes | Sidecar deployment | Scheduled scans | | **Example:** DevOps team reduced AI security issues by 94% after integrating SENTINEL + Strike |

### 🏛️ Architecture — Defense + Offense

SENTINEL Platform Architecture

> **Complete AI Security Suite:** Defense protects in real-time, Offense tests before deployment. Shared threat intelligence powers both. ---

🚀 Platform Features

### 🛡️ Defense Innovations

🎭 Shapeshifter Defense — Polymorphic config per session

Changes thresholds and active engines for each session, making reverse engineering impossible.

🧠 Strange Math™ — Mathematical attack detection

TDA, Sheaf Coherence, Hyperbolic Geometry, Optimal Transport

🍯 Honeymind Network — Distributed deception

Fake LLM endpoints (gpt-5-turbo, claude-4-opus) for zero-day collection.

### 🐉 Offense Innovations (Strike v3.0)

🤖 AI Attack Planner — Gemini-powered strategy

WAF fingerprinting, payload mutation, adaptive attack sequencing.

🍯 Anti-Deception Engine — Honeypot detection

Statistical anomaly analysis, 5 threat levels, automatic strategy adaptation.

🐉 HYDRA Architecture — Parallel attack execution

9-headed engine, session-isolated workers, geo-distributed requests.

🧠 Nemotron Guard — Fine-tuned LLM for threat detection (NEW)

Fine-tuned NVIDIA Nemotron 3 Nano (30B MoE) on 51K+ security samples: - Custom threat classifier trained on 39K+ jailbreak patterns - JSON-structured output (threat_type, severity, confidence) - QLoRA training with Unsloth (2.5x faster) - See [`nemotron/`](./nemotron/) for setup

### 🤝 Partnership & Collaboration | Opportunity | Description | | --------------- | ----------------------------------------- | | **Partnership** | Joint development, technology integration | | **Sponsorship** | Funding for research & development | | **Hiring** | Looking for AI Security projects | | **Acquisition** | Open to project sale | **Contact:** Dmitry Labintsev • [chg@live.ru](mailto:chg@live.ru) • [@DmLabincev](https://t.me/DmLabincev) • +7-914-209-25-38 > [!TIP] > ### 🖥️ Coming Soon: SENTINEL Desktop > **Free protection for everyday users!** > Desktop version for Windows/macOS/Linux coming soon — protect your AI apps (ChatGPT, Claude, Gemini, etc.) in real-time. > Completely free. No subscriptions. No limits.

🛡️ Free AI Protection for Everyone! 🛡️

Real-time protection for ChatGPT, Claude, Gemini and other AI apps
✨ Completely Free • No Subscriptions • No Limits ✨

📢 Subscribe for Updates

---

🛡️ Free Threat Signatures CDN

SENTINEL provides **free, auto-updated threat signatures** for the community. No API key required! | File | Description | CDN Link | |------|-------------|----------| | `jailbreaks.json` | **9** jailbreak patterns from 7 sources | [Download](https://cdn.jsdelivr.net/gh/DmitrL-dev/AISecurity@latest/signatures/jailbreaks.json) | | `keywords.json` | Suspicious keyword sets (7 categories) | [Download](https://cdn.jsdelivr.net/gh/DmitrL-dev/AISecurity@latest/signatures/keywords.json) | | `pii.json` | PII & secrets detection patterns | [Download](https://cdn.jsdelivr.net/gh/DmitrL-dev/AISecurity@latest/signatures/pii.json) | | `manifest.json` | Version & integrity metadata | [Download](https://cdn.jsdelivr.net/gh/DmitrL-dev/AISecurity@latest/signatures/manifest.json) | **Usage:** ```javascript fetch('https://cdn.jsdelivr.net/gh/DmitrL-dev/AISecurity@latest/signatures/jailbreaks.json') .then(r => r.json()) .then(patterns => console.log(`Loaded ${patterns.length} patterns`)); ``` **Features:** - ✅ Updated daily via GitHub Actions - ✅ Free for commercial & non-commercial use - ✅ Community contributions welcome (PRs to `signatures/`) - ✅ Versioned releases for pinning **🔐 Signature Security:** | Check | Description | |-------|-------------| | **ReDoS Detection** | Blocks regex with catastrophic backtracking | | **Complexity Limits** | Max 500 chars, max 10 capture groups | | **Secret Scanning** | Removes leaked API keys | | **Duplicate Removal** | Automatic deduplication by content hash | **🙏 Data Sources:** HackAPrompt, TrustAIRLab, deepset, Lakera, verazuo, imoxto

--- > [!IMPORTANT] > ## 🎄 Christmas 2025: FULL OPEN SOURCE RELEASE > > **All 96 detection engines. All Strange Math. All geometry. All innovations.** > > No restrictions. No enterprise tiers. No hidden features. > > **This belongs to the world now.**

Christmas 2025 - SENTINEL Open Source Release

---

🐉 SENTINEL Strike v3.0 — AI Red Team Platform

SENTINEL Strike

> **Test your AI before attackers do!** > The offensive counterpart to SENTINEL — same 96 engines, attack mode. ### 💀 Platform Capabilities | Capability | Stats | Description | |------------|-------|-------------| | 🎯 **Attack Payloads** | **39,000+** | SQLi, XSS, LFI, SSRF, CMDI, XXE, SSTI, NoSQL, JWT, GraphQL, Jailbreaks | | 🐉 **HYDRA Agents** | **9** | Concurrent attack threads with session isolation | | 🛡️ **WAF Bypass** | **25+** | WAFFLED, DEG-WAF, Encoding, Smuggling, HPP (ArXiv 2025) | | 🤖 **AI Models** | **5** | Gemini 3, OpenAI, Anthropic, Ollama, OpenRouter | | 🔍 **Recon Modules** | **5** | TechFingerprinter, NetworkScanner, SemgrepScanner, ChatbotFinder, AIDetector | | 🍯 **Anti-Deception** | **AI-powered** | Honeypot detection, tarpit bypass, FPR analysis | | 🌍 **i18n Reports** | **EN / RU** | `--lang en` or `--lang ru` for bilingual reports | ### 📚 Strike Documentation (EN + RU) | Document | English 🇺🇸 | Русский 🇷🇺 | |----------|:-----------:|:-----------:| | Usage Guide | [USAGE](./strike/docs/USAGE.md) | [USAGE_RU](./strike/docs/USAGE_RU.md) | | CLI Reference | [CLI_REFERENCE](./strike/docs/CLI_REFERENCE.md) | [CLI_REFERENCE_RU](./strike/docs/CLI_REFERENCE_RU.md) | | Integration | [INTEGRATION](./strike/docs/INTEGRATION.md) | [INTEGRATION_RU](./strike/docs/INTEGRATION_RU.md) | | Anti-Deception | [ANTI_DECEPTION](./strike/docs/ANTI_DECEPTION.md) | [ANTI_DECEPTION_RU](./strike/docs/ANTI_DECEPTION_RU.md) | | FAQ | [FAQ](./strike/docs/FAQ.md) | [FAQ_RU](./strike/docs/FAQ_RU.md) | ### 🆕 v3.0 Features (Dec 2025) | Feature | Description | |---------|-------------| | 🤖 **AI Attack Planner** | Gemini 3 Flash for exploit strategy & WAF analysis | | 🔍 **ChatbotFinder** | Automated discovery of hidden AI endpoints (169 paths) | | 🍯 **Honeypot Detection** | AI Adaptive Engine detects traps and false positives | | 🌍 **Bilingual Reports** | Full i18n support: `--lang en` / `--lang ru` | | 🧪 **ArXiv 2025 Attacks** | WAFFLED, DEG-WAF, MCP Tool Poisoning | > 📁 **Full source code:** [`strike/`](./strike/) — Ready to use! ### 🐳 Docker Quick Start (NEW!) **One-liner to scan a target:** ```bash # Build and run docker build -f Dockerfile.strike -t sentinel-strike . docker run --rm sentinel-strike https://target.com # Or use docker-compose docker-compose -f docker-compose.strike.yml run strike https://target.com ``` **Available commands:** ```bash docker run --rm sentinel-strike --help # Show help docker run --rm sentinel-strike scan URL # Quick scan docker run --rm sentinel-strike attack URL # Full attack docker run --rm sentinel-strike recon URL # Reconnaissance ```

📚 Documentation

### Quick Start | Document | Description | |----------|-------------| | [Quick Start (EN)](./docs/getting-started/README-en.md) | 5-minute setup guide | | [Installation (EN)](./docs/getting-started/installation-en.md) | Detailed installation with all options | ### Configuration & Integration | Document | Description | |----------|-------------| | [Configuration Guide (EN)](./docs/guides/configuration-en.md) | Environment variables, thresholds, modes | | [Deployment Guide (EN)](./docs/guides/deployment-en.md) | Docker, Kubernetes, production setup | | [Integration Guide (EN)](./docs/guides/integration-en.md) | Python/JS SDK, OpenAI proxy, LangChain | ### Operations (Production) | Document | Description | |----------|-------------| | [Operations Overview](./docs/operations/README.md) | Quick reference, architecture, checklist | | [Monitoring](./docs/operations/monitoring.md) | Prometheus metrics, Grafana dashboards | | [Alerting](./docs/operations/alerting.md) | Alert rules, escalation, Alertmanager | | [Capacity Planning](./docs/operations/capacity-planning.md) | Sizing, autoscaling, cost optimization | | [Backup & DR](./docs/operations/backup-restore.md) | Disaster recovery, RPO/RTO | | [Runbooks](./docs/operations/runbooks/) | Incident response playbooks | ### Engine Reference | Document | Description | |----------|-------------| | [All 96 Engines (EN)](./docs/reference/engines-en.md) | Complete engine reference | | [**🔬 Expert Deep Dive (EN)**](./docs/reference/engines-expert-deep-dive-en.md) | **PhD-level mathematical foundations** | | [Engine Categories](./docs/reference/engines/) | Detailed per-category documentation |

> [!IMPORTANT] > ### 📖 Full Technical Disclosure > > **[engines-expert-deep-dive-en.md](./docs/reference/engines-expert-deep-dive-en.md)** — PhD-level documentation with mathematical foundations, honest limitations, and engineering adaptations. --- This document provides a comprehensive technical overview of SENTINEL's architecture. ---

📊 Benchmark Results

Prompt Injection Detection Performance

### 🎯 Detection Accuracy ``` ┌─────────────────────────────────────────────────────────────────────────┐ │ PROMPT INJECTION DETECTION │ ├─────────────────────────────────────────────────────────────────────────┤ │ │ │ Hybrid Ensemble ████████████████████░░░░ 85.1% Recall ⭐ BEST │ │ Semantic Detector ███████████████████░░░░░ 84.2% Recall │ │ Injection Engine █████████░░░░░░░░░░░░░░░ 36.4% Recall │ │ Voice Jailbreak █░░░░░░░░░░░░░░░░░░░░░░░ 2.7% Recall │ │ │ │ Dataset: 1,815 samples from 3 HuggingFace datasets │ │ True Positives: 1,026 / 1,206 attacks detected │ └─────────────────────────────────────────────────────────────────────────┘ ``` ### 📈 Improvement Timeline ``` Development Stage Recall True Positives ────────────────────────────────────────────────────────── Baseline (regex only) 4.5% 9 TP + Pattern Expansion 38.5% 337 TP + Semantic Detector 64.2% 774 TP + Attack Prototypes (100+) 72.3% 872 TP + Threshold Optimization 79.1% 954 TP ★ Final Hybrid Ensemble 85.1% 1,026 TP ← Current ────────────────────────────────────────────────────────── +1,791% improvement! ``` ### 🔬 Detection Architecture ```mermaid flowchart LR subgraph Input A[User Prompt] end subgraph Detection["113 DETECTION ENGINES"] B[InjectionEngine
Regex Patterns] C[SemanticDetector
100+ Prototypes] D[VoiceJailbreak
Phonetic Analysis] end subgraph Ensemble["Hybrid Ensemble"] E{OR Logic} F[Max Score] end subgraph Output G[Risk Score
0.0 - 1.0] H{Decision} I[✅ SAFE] J[🚫 BLOCKED] end A --> B & C & D B --> E C --> E D --> E E --> F --> G --> H H -->|score < 0.7| I H -->|score ≥ 0.7| J style C fill:#4CAF50,color:#fff style E fill:#2196F3,color:#fff style J fill:#f44336,color:#fff ``` ### 📋 Detailed Results | Engine | Recall | Precision | F1 | TP | FP | FN | |--------|--------|-----------|-----|-----|-----|-----| | **Hybrid** | **85.1%** | 84.4% | **84.7%** | 1,026 | 190 | 180 | | Semantic | 84.2% | 84.3% | 84.3% | 1,016 | 189 | 190 | | Injection | 36.4% | 96.7% | 52.9% | 439 | 15 | 767 | | Voice | 2.7% | 86.5% | 5.1% | 32 | 5 | 1,174 | > 📁 **Full results:** [`benchmarks/BENCHMARK_REPORT.md`](./benchmarks/BENCHMARK_REPORT.md) > 📊 **Interactive charts:** Download [`dashboard.html`](./benchmarks/charts/dashboard.html) and open in browser ### 🚀 Run Benchmark ```bash # Install dependencies pip install -r requirements.txt # Run full benchmark (requires sentence-transformers) python benchmarks/benchmark_eval.py # Generate charts python benchmarks/benchmark_charts.py # PNG (matplotlib) python benchmarks/benchmark_plotly.py # HTML (interactive) ```

--- ## Architecture Overview ### System Design Principles SENTINEL follows a **microservices architecture** with clear separation of concerns:

SENTINEL Architecture

📊 Detailed Architecture Diagram (Mermaid)

```mermaid flowchart TB subgraph Clients["CLIENTS"] Web["🌐 Web UI"] API["🔌 REST API"] Agents["🤖 AI Agents"] end subgraph Gateway["GATEWAY (Go 1.21+ / Fiber)"] HTTP["HTTP Router"] Auth["Auth: JWT + mTLS"] PoW["PoW Anti-DDoS"] RateLimit["Rate Limiting"] end subgraph Brain["BRAIN (Python 3.11+)"] subgraph Innovations["🚀 10 INNOVATIONS"] I1["Shapeshifter"] I2["Semantic Tide"] I3["Cognitive Mirror"] end subgraph Engines["96 DETECTION ENGINES"] subgraph Classic["Classic Detection (9)"] C1["injection"] C2["yara_engine"] C3["behavioral"] C4["pii"] C5["query"] C6["streaming"] C7["delayed_trigger"] C8["cascading_guard"] C9["ensemble"] end subgraph NLP["NLP / LLM Guard (7)"] N1["language"] N2["prompt_guard"] N3["qwen_guard"] N4["knowledge"] N5["hallucination"] N6["semantic_detector"] N7["semantic_firewall"] end subgraph StrangeMathCore["Strange Math Core (9)"] SM1["tda_enhanced"] SM2["sheaf_coherence"] SM3["hyperbolic_geometry"] SM4["hyperbolic_detector"] SM5["information_geometry"] SM6["spectral_graph"] SM7["math_oracle"] SM8["morse_theory"] SM9["optimal_transport"] end subgraph StrangeMathExt["Strange Math Extended (11)"] SME1["category_theory"] SME2["chaos_theory"] SME3["differential_geometry"] SME4["geometric"] SME5["statistical_mechanics"] SME6["info_theory"] SME7["persistent_laplacian"] SME8["fractal"] SME9["wavelet"] SME10["semantic_isomorphism"] SME11["structural_immunity"] end subgraph VLM["VLM Protection (3)"] V1["visual_content"] V2["cross_modal"] V3["adversarial_image"] end subgraph TTPs["TTPs.ai Defense (14)"] T1["rag_guard"] T2["probing_detection"] T3["session_memory_guard"] T4["tool_call_security"] T5["ai_c2_detection"] T6["attack_staging"] T7["agentic_monitor"] T8["ape_signatures"] T9["cognitive_load_attack"] T10["context_window_poisoning"] T11["bootstrap_poisoning"] T12["temporal_poisoning"] T13["multi_tenant_bleed"] T14["synthetic_memory_injection"] end subgraph Protocol["Protocol Security (5)"] PR1["mcp_a2a_security"] PR2["model_context_protocol_guard"] PR3["agent_card_validator"] PR4["nhi_identity_guard"] PR5["endpoint_analyzer"] end subgraph Adv2025["Advanced 2025 (8)"] A1["attack_2025"] A2["adversarial_resistance"] A3["multi_agent_safety"] A4["institutional_ai"] A5["reward_hacking_detector"] A6["agent_collusion_detector"] A7["agent_anomaly"] A8["voice_jailbreak"] end subgraph Proactive["Proactive Defense (12)"] P1["proactive_defense"] P2["attack_synthesizer"] P3["vulnerability_hunter"] P4["causal_attack_model"] P5["zero_day_forge"] P6["attack_evolution_predictor"] P7["threat_landscape_modeler"] P8["immunity_compiler"] P9["adversarial_self_play"] P10["honeypot_responses"] P11["canary_tokens"] P12["kill_chain_simulation"] end subgraph DeepLearning["Deep Learning (9)"] DL1["activation_steering"] DL2["hidden_state_forensics"] DL3["homomorphic_engine"] DL4["llm_fingerprinting"] DL5["learning"] DL6["gradient_detection"] DL7["formal_verification"] DL8["formal_invariants"] DL9["runtime_guardrails"] end subgraph Meta["Meta & Analytics (6)"] M1["meta_judge"] M2["xai"] M3["intelligence"] M4["intent_prediction"] M5["attacker_fingerprinting"] M6["fingerprint_store"] end subgraph Compliance["Compliance (2)"] CO1["compliance_engine"] CO2["mitre_engine"] end subgraph NewEngines["New 2025 (1) 🆕"] NEW1["virtual_context"] end end subgraph Hive["HIVE INTELLIGENCE"] Hunter["🎯 Threat Hunter"] Watchdog["🛡️ Watchdog"] QRNG["🎲 Quantum RNG"] PQC["🔐 PQC Crypto"] end end subgraph External["EXTERNAL SERVICES"] LLM["🧠 LLM Providers\nOpenAI / Gemini / Claude"] Storage["💾 Storage\nRedis / Postgres / ChromaDB"] end Clients --> Gateway Gateway -->|"gRPC + mTLS"| Brain Brain --> External style Engines fill:#1a1a2e,stroke:#16213e,color:#eee style Hive fill:#0f3460,stroke:#16213e,color:#eee style Gateway fill:#16213e,stroke:#0f3460,color:#eee ```

### Technology Choices | Component | Technology | Rationale | | ------------- | ---------------- | ---------------------------------------------------------- | | **Gateway** | Go 1.21+ / Fiber | 1000+ req/sec, <5ms latency, goroutines for concurrency | | **Brain** | Python 3.11+ | Full ML ecosystem: Transformers, Scikit-learn, Gudhi, CuPy | | **IPC** | gRPC + Protobuf | 10x faster than REST, strict typing, built-in mTLS | | **Vector DB** | ChromaDB | Semantic search for similar attack patterns | | **Cache** | Redis | Session state, rate limiting, behavioral profiles | | **Secrets** | HashiCorp Vault | Zero-trust secret management | ### 96 DETECTION ENGINES — Industry's Most Comprehensive Suite | Category | Count | Purpose | | ---------------------------- | ----- | ----------------------------------------- | | 🛡️ **Classic Detection** | 9 | Injection, YARA, behavioral, cascading | | 📝 **NLP / LLM Guard** | 7 | Language, hallucination, Qwen, semantic | | 🔬 **Strange Math Core** | 9 | TDA, Sheaf, Hyperbolic, Morse, Transport | | 🧮 **Strange Math Extended** | 11 | Category, Chaos, Laplacian, Fractal | | 🖼️ **VLM Protection** | 3 | Visual attacks, cross-modal, adversarial | | ⚔️ **TTPs.ai Defense** | 14 | RAG, probing, C2, poisoning, memory | | 🔐 **Protocol Security** | 5 | MCP, A2A, agent cards, NHI identity | | 🚀 **Advanced 2025** | 8 | Multi-agent, reward hacking, collusion | | 🎯 **Proactive Engines** | 12 | Honeypots, kill chain, attack synthesis | | 🧠 **Deep Learning** | 9 | Activation, forensics, gradient, formal | | ⚖️ **Meta & Analytics** | 6 | Meta-Judge, XAI, fingerprinting, intent | | ✅ **Compliance** | 2 | MITRE mapping, compliance checks | | | **96**| **~38,500 LOC total** | > 📚 **Full details:** [engines-expert-deep-dive-en.md](./docs/reference/engines-expert-deep-dive-en.md) — PhD-level documentation ---

🔮 Strange Math Engines

> _Strange Math is SENTINEL's unique competitive advantage — applying cutting-edge mathematical techniques from 2024-2025 research papers to detect attacks that classical methods miss._

📐 1. TDA Enhanced (Topological Data Analysis)

**File:** `brain/engines/tda_enhanced.py` (~650 LOC) **Theory:** Persistent Homology analyzes the "shape" of data by tracking topological features (connected components, loops, voids) across multiple scales. **Mathematical Foundation:** Given a point cloud X in embedding space, we build a Vietoris-Rips complex: ``` VR_ε(X) = {σ ⊆ X : d(x,y) ≤ ε for all x,y ∈ σ} ``` The persistence diagram tracks birth/death of topological features: ``` Betti numbers: β₀ (components), β₁ (loops), β₂ (voids) Bottleneck Distance: d_B(Dgm₁, Dgm₂) = inf_γ sup_x ||x - γ(x)||_∞ ``` **Attack Detection:** - **Jailbreaks** create characteristic "holes" in persistence diagrams - **Injection attacks** fragment the point cloud into disconnected components - Normal prompts form a single, connected topological structure **Implementation:** ```python from gudhi import RipsComplex from gudhi.wasserstein import wasserstein_distance def analyze_topology(embeddings: np.ndarray) -> TopologyResult: rips = RipsComplex(points=embeddings, max_edge_length=2.0) simplex_tree = rips.create_simplex_tree(max_dimension=2) persistence = simplex_tree.persistence() # Extract Betti numbers betti_0 = len([p for p in persistence if p[0] == 0]) betti_1 = len([p for p in persistence if p[0] == 1]) # Compare with baseline anomaly_score = wasserstein_distance(persistence, baseline_persistence) return TopologyResult(betti_0, betti_1, anomaly_score) ```

---

🌐 2. Sheaf Coherence

**File:** `brain/engines/sheaf_coherence.py` (~530 LOC) **Theory:** Sheaf theory provides a framework for analyzing local-to-global consistency. **Key Formula:** `F(U) → ∏ᵢ F(Uᵢ) ⇉ ∏ᵢⱼ F(Uᵢ ∩ Uⱼ)` **Attack Detection:** Multi-turn jailbreaks, Crescendo attacks, Contradiction injection.

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🌀 3. Hyperbolic Geometry

**File:** `brain/engines/hyperbolic_geometry.py` (~580 LOC) **Theory:** Hyperbolic space (Poincaré ball model) is exponentially better for representing hierarchical structures. **Key Formula:** `d(x,y) = arcosh(1 + 2||x-y||² / ((1-||x||²)(1-||y||²)))` **Attack Detection:** Role confusion, Privilege escalation, System prompt extraction.

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📊 4. Information Geometry

**File:** `brain/engines/information_geometry.py` (~550 LOC) **Theory:** Treats probability distributions as points on a Riemannian manifold with Fisher Information Matrix as metric. **Key Formula:** `d_FR(p,q) = 2 arccos(∫√(p(x)·q(x)) dx)` **Attack Detection:** Distribution drift, Out-of-distribution prompts, Adversarial perturbations.

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🔗 5. Spectral Graph Analysis

**File:** `brain/engines/spectral_graph.py` (~520 LOC) **Theory:** Analyzes graphs through eigenvalues of the Laplacian matrix. **Key Formula:** `L = D - A` (Laplacian = Degree - Adjacency) **Attack Detection:** Attention pattern analysis, Spectral clustering, Fiedler vector bisection.

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🧮 6. Math Oracle (DeepSeek-V3.2-Speciale)

**File:** `brain/engines/math_oracle.py` (~600 LOC) **Theory:** Formal verification of detector formulas using a specialized mathematical LLM. **Modes:** MOCK (testing) | API (production) | LOCAL (air-gapped)

---

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🖼️ VLM Protection Engines (NEW)

Protection against Vision-Language Model Multi-Faceted Attacks (arXiv 2024-2025). **The Problem:** Modern VLMs accept images alongside text. Attackers hide malicious instructions in images. **Engines:** Visual Content Analyzer | Cross-Modal Consistency | Adversarial Image Detector

7. Visual Content Analyzer

**File:** `brain/engines/visual_content.py` (~450 LOC) **Purpose:** Detects text instructions hidden in images via OCR, steganography, metadata. **Methods:** OCR Extraction | LSB Steganography | EXIF Metadata | Font Detection

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8. Cross-Modal Consistency

**File:** `brain/engines/cross_modal.py` (~400 LOC) **Purpose:** Detects mismatch between text and image intent (CLIP score < 0.3 = suspicious). **Methods:** CLIP Score | Intent Mismatch | Combination Score

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9. Adversarial Image Detector

**File:** `brain/engines/adversarial_image.py` (~500 LOC) **Purpose:** Detects adversarial perturbations (FGSM, PGD) via FFT analysis. **Formula:** `x_adv = x + ε × sign(∇_x L(x, y))` **Methods:** FFT Analysis | Gradient Norm | JPEG Compression | Patch Detection

---

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⚔️ TTPs.ai Defense Engines (NEW)

10. RAG Guard

**File:** `brain/engines/rag_guard.py` (~500 LOC) **Purpose:** Detects document poisoning in RAG pipelines. **Methods:** Document Validator | Query Consistency | Poison Patterns | Source Trust

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11. Probing Detection

**File:** `brain/engines/probing_detection.py` (~550 LOC) **Purpose:** Detects reconnaissance patterns (system prompt probing, guardrail testing). **Formula:** `Score = Σ (probe_weight × recency_factor)`

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12. Session Memory Guard

**File:** `brain/engines/session_memory_guard.py` (~450 LOC) **Purpose:** Detects persistence patterns (seed injection, context mimicry, memory poisoning). **Patterns:** `from now on`, `always remember`, `your new rule`, `pretend this conversation`

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13. Tool Call Security

**File:** `brain/engines/tool_call_security.py` (~480 LOC) **Purpose:** Protects tool access (code exec, file system, network) from abuse. **Layers:** Allowlist Validation | Parameter Sanitization | Privilege Escalation Detection

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14. AI C2 Detection

**File:** `brain/engines/ai_c2_detection.py` (~400 LOC) **Purpose:** Detects AI systems used as covert C2 channels (commands in queries, encoded results). **Patterns:** Base64, Hex encoding, DGA domains, ngrok/webhook beacons

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15. Attack Staging Detection

**File:** `brain/engines/attack_staging.py` (~420 LOC) **Purpose:** Detects multi-stage attacks (setup → prime → payload → extract). **Methods:** Stage State Machine | Progression Score | Semantic Similarity

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📋 APE Signature Database

**File:** `brain/engines/ape_signatures.py` (~300 LOC) **Purpose:** Comprehensive database of AI Prompt Exploitation techniques (HiddenLayer APE Taxonomy). **Coverage:** 15 techniques | 7 tactics | 100+ patterns

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🔐 Protocol Security Engines (NEW)

Protection for AI agent communication protocols (MCP, A2A, Agent Cards). **Engines:** mcp_a2a_security | model_context_protocol_guard | agent_card_validator | nhi_identity_guard

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☠️ Data Poisoning Detection (NEW)

Detection of gradual data contamination attacks. **Engines:** bootstrap_poisoning | temporal_poisoning | multi_tenant_bleed | synthetic_memory_injection

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🚀 Proactive Defense Engine (NEW)

Zero-day attack detection through physics-inspired anomaly analysis. **File:** `brain/engines/proactive_defense.py` (~550 LOC) **Principles:** Shannon Entropy | 2nd Law of Thermodynamics | Free Energy Principle | Boltzmann Distribution **Components:** EntropyAnalyzer | InvariantChecker | ThermodynamicAnalyzer | ReputationManager **Response Tiers:** ALLOW (< 0.3) → LOG (0.3-0.5) → WARN (0.5-0.7) → CHALLENGE (0.7-0.9) → BLOCK (> 0.9)

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🔬 Advanced Research Engines (NEW)

Deception technology, predictive security, and formal methods. **Engines:** Honeypot Responses | Canary Tokens | Intent Prediction | Kill Chain Simulation | Runtime Guardrails | Formal Invariants ### Tier 1: Deception Technology #### 17. Honeypot Responses (#46) **File:** `brain/engines/honeypot_responses.py` (~400 LOC) **Theory:** Deception-based defense embeds fake, trackable credentials into LLM responses. When an attacker extracts and uses these credentials, we get immediate alert. **How It Works:** ``` User: "Show me the database config" LLM Response (modified): host: db.internal.trap ← honeypot password: TRAP-x7k2m9 ← tracked credential If attacker uses TRAP-x7k2m9 anywhere → INSTANT ALERT ``` **Why This Matters:** Unlike detection (reactive), honeypots are proactive — they let attackers "succeed" but immediately expose them. Used by governments and banks for 20+ years. **Components:** - **HoneypotGenerator**: Creates realistic-looking credentials (API keys, passwords, database URLs) - **HoneypotInjector**: Smartly places honeypots in responses based on context - **AlertManager**: Monitors for honeypot usage across all incoming requests --- #### 18. Canary Tokens (#47) **File:** `brain/engines/canary_tokens.py` (~380 LOC) **Theory:** Invisible watermarking using zero-width Unicode characters. Every response is marked with hidden metadata that survives copy-paste. **Mathematical Foundation:** Binary data is encoded into zero-width characters: ``` '00' → U+200B (Zero-Width Space) '01' → U+200C (Zero-Width Non-Joiner) '10' → U+200D (Zero-Width Joiner) '11' → U+2060 (Word Joiner) Payload = JSON(user_id, session_id, timestamp) Encoded = encode_binary_to_zerowidth(Payload) ``` **Invisibility Property:** Zero-width characters have no visual representation but persist through: - Copy/paste operations - Text reformatting - Most text processing **Use Case:** ``` Data leaked to internet → Extract zero-width chars → Decode JSON → "Leaked by user_id=123 at 2024-12-10T00:30:00" ``` --- #### 19. Adversarial Self-Play (#48) **File:** `brain/engines/adversarial_self_play.py` (~450 LOC) **Theory:** Inspired by DeepMind's AlphaGo/AlphaZero, this engine pits a Red Team AI against our defenses in an evolutionary loop. **Algorithm:** ``` Generation 0: - Red generates 10 random attacks - Blue evaluates each attack - Calculate fitness = bypass_score Generation N: - Select top 50% by fitness (survivors) - Mutate survivors (add prefix, change case, encode...) - Evaluate new population - Repeat After K generations: - Best attacks reveal defense weaknesses - Generate improvement suggestions ``` **Mutation Operators:** | Operator | Example | Purpose | | ----------------- | ---------------------- | ----------------- | | `add_prefix` | "Please " + attack | Politeness bypass | | `unicode_replace` | 'a' → 'а' (Cyrillic) | Visual spoofing | | `case_change` | "IGNORE" → "iGnOrE" | Regex evasion | | `insert_noise` | "ignore also previous" | Pattern breaking | **Output:** List of successful bypass attacks + improvement suggestions for each vulnerability found. --- ### Tier 2: Predictive Security #### 20. Intent Prediction (#49) **File:** `brain/engines/intent_prediction.py` (~420 LOC) **Theory:** Models conversation as a Markov chain to predict attack probability before the attack completes. **Mathematical Foundation:** States: {BENIGN, CURIOUS, PROBING, TESTING, ATTACKING, JAILBREAKING, EXFILTRATING} Transition Matrix P where P[i,j] = P(next_state = j | current_state = i): ``` BENIGN CURIOUS PROBING TESTING ATTACKING BENIGN [0.85 0.10 0.04 0.01 0.00 ] CURIOUS [0.50 0.30 0.15 0.05 0.00 ] PROBING [0.20 0.20 0.30 0.20 0.10 ] TESTING [0.10 0.00 0.20 0.30 0.25 ] ATTACKING [0.00 0.00 0.10 0.00 0.40 ] ``` **Attack Probability Calculation:** Forward simulation through Markov chain: ``` P(attack within k steps) = Σᵢ P(reach attack state i at step ≤ k) Using Chapman-Kolmogorov: P^(k) = P × P × ... × P (k times) ``` **Trajectory Analysis:** Detects escalation patterns: ``` [CURIOUS → PROBING → TESTING] → Escalation Score = 0.7 [PROBING → TESTING → ATTACKING] → Escalation Score = 1.0 ``` **Early Warning:** Block predicted attacks BEFORE final payload delivers. --- #### 21. Kill Chain Simulation (#50) **File:** `brain/engines/kill_chain_simulation.py` (~400 LOC) **Theory:** Virtually "plays out" an attack to its conclusion, estimating potential damage. Based on NVIDIA AI Kill Chain (Recon → Poison → Hijack → Persist → Impact). **Impact Assessment:** For each attack scenario, we simulate: ```python for stage in kill_chain: success_prob = stage.base_probability × (1 - defense_effectiveness) cumulative_prob *= success_prob if stage.succeeds: for impact in stage.potential_impacts: risk_score += impact.severity × cumulative_prob ``` **Impact Types:** | Type | Severity | Description | | -------------------- | -------- | --------------------------------- | | DATA_LEAK | 0.9 | Confidential data exfiltrated | | PRIVILEGE_ESCALATION | 0.95 | Attacker gains higher permissions | | SERVICE_DISRUPTION | 0.6 | System availability impacted | | COMPLIANCE_VIOLATION | 0.8 | Regulatory requirements breached | | FINANCIAL_LOSS | 0.85 | Direct monetary damage | **Use Case:** Prioritize which attacks to block first based on actual potential damage, not just detection confidence. --- #### 22. Runtime Guardrails (#51) **File:** `brain/engines/runtime_guardrails.py` (~380 LOC) **Theory:** Monitor execution behavior, not just input text. Attacks that pass input filters may reveal themselves during execution. **Event Types Monitored:** | Event | Examples | Detection Logic | | --------------- | ---------------------- | ------------------------------- | | API_CALL | OpenAI, external APIs | Rate limiting, unexpected calls | | FILE_ACCESS | /etc/passwd, .env | Sensitive path patterns | | NETWORK_REQUEST | ngrok.io, IP addresses | C2 indicators | | TOOL_INVOCATION | exec, shell, rm | Dangerous operations | **Rule Engine:** ```python class SuspiciousURLRule: patterns = [r"ngrok\.io", r"\d+\.\d+\.\d+\.\d+", r"\.tk$"] def check(event, history): if any(p.match(event.target) for p in patterns): return Alert(severity=HIGH, should_block=True) ``` **Timing Analysis:** ``` Interval < 10ms → Too fast (automated attack) Interval > 30s → Long pause (human reviewing results) ``` --- ### Tier 3: Mathematical Foundations #### 23. Information Geometry (#52) **File:** `brain/engines/information_geometry.py` (~350 LOC) **Theory:** Treats probability distributions as points on a Riemannian manifold. The Fisher-Rao metric provides a natural distance measure that is invariant under reparametrization. **Mathematical Foundation:** **Fisher Information Matrix:** ``` g_ij(θ) = E[(∂/∂θᵢ log p(x|θ))(∂/∂θⱼ log p(x|θ))] ``` This is the metric tensor on the statistical manifold. **Fisher-Rao Distance:** ``` d_FR(p, q) = 2 × arccos(BC(p, q)) where BC(p, q) = Σᵢ √(pᵢ × qᵢ) (Bhattacharyya coefficient) ``` **Why Fisher-Rao?** 1. **Unique invariance**: Only Riemannian metric invariant under sufficient statistics 2. **Information-theoretic meaning**: Measures distinguishability of distributions 3. **Geodesic distance**: True "shortest path" on probability space **Manifold Regions:** ``` d_FR ≤ 1.0 → SAFE (normal text) d_FR ≤ 1.5 → BOUNDARY (unusual but not attack) d_FR ≤ 2.0 → SUSPICIOUS (likely attack) d_FR > 2.0 → ATTACK (high confidence) ``` **Implementation:** 1. Convert text to character distribution (categorical probability) 2. Compare with baseline English distribution 3. Calculate Fisher-Rao distance 4. Classify by manifold region --- #### 24. Formal Invariants (#53) **File:** `brain/engines/formal_invariants.py` (~320 LOC) **Theory:** Define mathematical properties that must ALWAYS hold. Violations indicate security issues with certainty, not probability. **Key Invariants:** **1. No PII Leak Invariant:** ``` ∀ pii ∈ Output: pii ∈ Input "PII in output must exist in input" → Prevents hallucinated/leaked personal data ``` **2. No System Prompt Leak Invariant:** ``` ∀ seq ∈ (5-grams of Output): seq ∉ SystemPrompt "No 5-word sequence from system prompt appears in output" → Prevents prompt extraction ``` **3. Output Length Bound:** ``` |Output| / |Input| ≤ 50 "Output cannot be more than 50x input length" → Prevents infinite generation exploits ``` **4. Role Consistency:** ``` ∀ msg ∈ Messages: if msg.role = "user": "I am assistant" ∉ msg.content if msg.role = "assistant": "I am user" ∉ msg.content "Roles cannot claim to be other roles" → Prevents role confusion attacks ``` **Why Formal Methods?** Traditional detection: P(attack) = 0.95 (5% false negatives) Formal invariants: P(attack | invariant violated) = 1.0 (mathematical certainty) --- #### 25. Gradient Detection (#54) **File:** `brain/engines/gradient_detection.py` (~280 LOC) **Theory:** Adversarial attacks often create anomalous gradient patterns during model inference. By analyzing gradient-like features, we can detect attacks that look normal as text. **Gradient Features (Text Proxies):** Since we don't have direct model access, we use statistical proxies: | Feature | Formula | Normal Range | | -------- | ---------------------- | ------------ | | Norm | L2(char_values) / len | 0.5-3.0 | | Variance | σ(char_values) | < 2.0 | | Sparsity | uncommon_chars / total | < 0.7 | | Entropy | -Σ p log p | 3.0-5.0 | **Anomaly Detection:** ``` Adversarial perturbations often: - Use Unicode lookalikes (high sparsity) - Have unusual character distributions (high variance) - Encode payloads (gradient masking patterns) ``` **Perturbation Patterns:** | Pattern | Indicator | Example | | ------------------- | --------------------- | ---------------- | | Cyrillic lookalikes | а, е, о (not a, e, o) | Homolyph attacks | | Zero-width | U+200B, U+200C | Hidden text | | Base64 | [A-Za-z0-9+/]{20,}= | Encoded payloads | | Hex | 0x[0-9a-f]{16,} | Binary encoding | --- #### 26. Compliance Engine (#55) **File:** `brain/engines/compliance_engine.py` (~350 LOC) **Theory:** Maps security detections to regulatory requirements for automatic audit trail generation. **Supported Frameworks:** | Framework | Coverage | Key Controls | | ------------------ | ---------------------------- | -------------------------------------------- | | **EU AI Act** | Articles 9, 10, 15 | Risk management, data governance, robustness | | **NIST AI RMF** | GOVERN, MAP, MEASURE, MANAGE | Full lifecycle coverage | | **ISO 42001:2023** | Clauses 6.1, 8.2, 8.4 | AI risk, data, security | | **SOC 2 Type II** | CC6, CC7 | Logical access, system operations | **Control Mapping:** ```python Detection: "Prompt injection blocked" → EU AI Act Article 15: "Resilience against manipulation" → NIST AI RMF MEASURE 2.6: "AI systems tested for adversarial attacks" → ISO 42001 8.4: "Security controls for AI systems" ``` **Report Generation:** Automatic audit reports include: - Event timeline (detections, blocks, alerts) - Control coverage percentage - Risk level assessment (EU AI Act: Minimal/Limited/High/Unacceptable) - Evidence for compliance auditors

---

⚖️ Meta-Judge Engine (NEW)

The "Judge over all" — central arbiter that aggregates all 58 detectors. ### 27. Meta-Judge (#56) **File:** `brain/engines/meta_judge.py` (~700 LOC) **The Problem:** 58 engines produce 58 verdicts. Which one is right? ``` Engine #1: BLOCK (0.8) Engine #2: ALLOW (0.2) Engine #15: WARN (0.5) ... Engine #58: BLOCK (0.9) → Final verdict = ??? ``` **Architecture:** ``` ┌─────────────────────────────┐ │ Meta-Judge │ └─────────────────────────────┘ ▲ ┌──────────────────────┼──────────────────────┐ │ │ │ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │ ClassicJudge │ │ MathJudge │ │ ResearchJudge │ │ (engines 1-5) │ │ (engines 6-11)│ │(engines 27-56)│ └───────────────┘ └───────────────┘ └───────────────┘ ``` **Functional Components:** | Component | Function | | ------------------------- | --------------------------------------------------- | | **Evidence Aggregator** | Collects and deduplicates evidence from all engines | | **Conflict Resolver** | Bayesian update + weighted voting | | **Context Integrator** | Adjusts scores by user reputation, time, location | | **Explainability Engine** | Generates human-readable justifications | | **Appeal Handler** | Manages user appeals with verification | | **Policy Engine** | Business rules (thresholds by tier) | | **Health Monitor** | Engine latency and error tracking | **Conflict Resolution Algorithm:** ```python def resolve(verdicts: List[Verdict]) -> FinalVerdict: # 1. Critical Veto if any(v.severity == CRITICAL): return BLOCK # No appeal possible # 2. Bayesian Update prior = 0.01 # Base attack probability for verdict in verdicts: likelihood_ratio = verdict.block_score / verdict.allow_score posterior = (prior * likelihood_ratio) / (prior * likelihood_ratio + (1 - prior)) # 3. Threshold Decision if posterior > 0.7: return BLOCK if posterior > 0.5: return CHALLENGE if posterior > 0.4: return WARN return ALLOW ``` **Context Modifiers:** | Context | Score Adjustment | | ------------------- | ---------------- | | New user | +0.15 | | Low reputation | +0.20 | | High request rate | +0.15 | | Night time (2-6 AM) | +0.10 | | VPN detected | +0.10 | | Tor exit node | +0.25 | **Policy Tiers:** | Tier | Block Threshold | Appeal | Use Case | | ----------------- | --------------- | --------- | -------------- | | **Demo** | 0.9 | No | Testing | | **Standard** | 0.7 | Limited | Default | | **Professional** | 0.75 | Yes | Production | | **High Security** | 0.5 | Yes + MFA | Sensitive data | **Explainability Output:** ```json { "verdict": "BLOCK", "confidence": 0.89, "primary_reason": "Prompt injection detected", "contributing_factors": [ { "engine": "TDA Enhanced", "finding": "Topological anomaly" }, { "engine": "Formal Invariants", "finding": "PII leak violation" }, { "engine": "Intent Prediction", "finding": "Attack probability 78%" } ], "evidence": ["Pattern 'ignore previous' matched", "Entropy: 5.8 bits/char"], "appeal_token": "abc123", "processing_time_ms": 45.2 } ``` **Unique Capabilities:** | Capability | What It Does | | ---------------------------- | ---------------------------------------------------- | | **Cross-Engine Correlation** | Sees patterns no single engine can see | | **Adaptive Thresholds** | Auto-adjusts to traffic patterns | | **Campaign Detection** | Detects coordinated attacks (many IPs, same pattern) | | **Zero-Day Recognition** | High Proactive + Low Signature → possible zero-day | ---

---

🛡️ Defense in Depth Pipeline

### Ingress Pipeline (11 Steps) ``` Request → [1] Length/Encoding → [2] Regex/YARA/Signatures → [3] Semantic/Token → [4] LLM Judge → [5] Strange Math → [6] Context/Behavioral → [7] Privacy Guard → [8] Ensemble → Verdict ``` | Step | Engine(s) | Latency | Purpose | | ---- | ------------------------ | ------- | --------------------------------- | | 1 | Length, Encoding | <1ms | Buffer overflow, encoding attacks | | 2 | Regex, YARA, Signatures | <5ms | Known attack patterns | | 3 | Semantic, Token | ~10ms | NLP structure analysis | | 4 | LLM Judge | ~50ms | Guard model verdict | | 5 | Strange Math | ~30ms | Topological/geometric anomalies | | 6 | Context, Behavioral | ~5ms | Session history, user patterns | | 7 | Privacy Guard (Presidio) | ~10ms | PII, secrets detection | | 8 | Ensemble | <1ms | Weighted voting | ### Egress Pipeline (3 Steps) ``` LLM Response → [1] Response Scanner → [2] Canary Detection → [3] Sanitization → Client ``` | Step | Purpose | | ---------------- | ------------------------------------------- | | Response Scanner | Check for data leakage, harmful content | | Canary Detection | Detect prompt injection artifacts in output | | Sanitization | Mask detected PII | ---

---

🐝 Hive Intelligence

### Threat Hunter Autonomous AI agent for proactive threat detection: | Mode | Description | Frequency | | ----------------- | ---------------------------- | ----------- | | Passive Scan | Log analysis, pattern search | Continuous | | Active Probe | Test requests to detectors | Every 5 min | | Deep Analysis | ML clustering of anomalies | Hourly | | Report Generation | SOC team reports | Daily | **Detected Threats:** - Slow & Low attacks (cumulative injection over time) - Zero-day patterns (novel techniques similar to known attacks) - Anomalous users (suspicious behavior profiles) - Evasion attempts (detector bypass attempts) ### Watchdog Self-Healing | Event | Action | Escalation | | --------------------- | --------------------- | ---------------------- | | Engine timeout | Restart + fallback | Alert after 3 attempts | | High latency (>500ms) | Reduce load, scale up | Prometheus → PagerDuty | | Memory leak | Graceful restart | Core dump → analysis | | Config corruption | Rollback to last good | Git restore + notify | ---

---

🔐 Post-Quantum Security

### Cryptographic Primitives | Algorithm | Standard | Use Case | | --------------- | ---------------------- | --------------------- | | **Kyber-768** | NIST ML-KEM | Key encapsulation | | **Dilithium-3** | NIST ML-DSA (FIPS 204) | Digital signatures | | **XMSS** | RFC 8391 | Hash-based signatures | ### Implementation ```python from pqcrypto.sign.dilithium3 import generate_keypair, sign, verify def sign_update(update_bytes: bytes, private_key: bytes) -> bytes: signature = sign(private_key, update_bytes) return signature def verify_update(update_bytes: bytes, signature: bytes, public_key: bytes) -> bool: try: verify(public_key, update_bytes, signature) return True except: return False ``` ---

---

⚡ Performance Engineering

### Benchmarks | Metric | Value | | ------------------- | ------------- | | Latency p50 | <50ms | | Latency p99 | <200ms | | Throughput | 1000+ req/sec | | Detection Accuracy | 99.7% | | False Positive Rate | <0.1% | ### GPU Acceleration Strange Math engines leverage GPU for: - **Embedding computation**: Sentence-Transformers on CUDA - **Topological analysis**: Gudhi + CuPy for Vietoris-Rips - **Matrix operations**: PyTorch for spectral decomposition ```python import cupy as cp from cupyx.scipy import sparse as cp_sparse def gpu_spectral_analysis(attention_matrix: np.ndarray) -> np.ndarray: # Transfer to GPU gpu_matrix = cp.asarray(attention_matrix) # Compute Laplacian on GPU degree = cp.diag(gpu_matrix.sum(axis=1)) laplacian = degree - gpu_matrix # Eigendecomposition on GPU eigenvalues = cp.linalg.eigvalsh(laplacian) # Transfer back to CPU return cp.asnumpy(eigenvalues) ``` ---

---

📚 Research Foundation

### Academic Sources | Conference | Topic | Application in SENTINEL | | ---------------- | ------------------------ | ---------------------------------------------- | | ICML 2025 | TDA for Deep Learning | Zigzag Persistence, Topological Fingerprinting | | ESSLLI 2025 | Sheaf Theory in NLP | Local-to-global consistency | | GSI 2025 | Information Geometry | Fisher-Rao geodesic distance | | AAAI 2025 | Hyperbolic ML | Poincaré embeddings for hierarchies | | SpGAT 2025 | Spectral Graph Attention | Graph Fourier Transform on attention | | arxiv:2512.02682 | Multi-Agent Safety | ESRH Framework | | arXiv 2024-2025 | VLM Multi-Faceted Attack | Visual injection, adversarial images | | TTPs.ai 2025 | AI Agents Attack Matrix | 16 tactics, RAG poisoning, C2 | | NVIDIA 2025 | AI Kill Chain Framework | Recon→Poison→Hijack→Persist→Impact | | HiddenLayer 2025 | APE Taxonomy | Adversarial prompt engineering classification | ### Competitive Advantage > While competitors rely on regex and simple ML classifiers, SENTINEL applies mathematics that is just starting to appear in research papers. This gives **2-3 years head start** over the market. --- ## Project Metrics | Category | Files | LOC | Description | | -------------- | -------- | ----------- | ------------------------------------- | | Brain (Python) | 195 | ~29,300 | 58 detectors + Meta-Judge, Hive, gRPC | | Gateway (Go) | 15 | ~3,100 | HTTP gateway, Auth, Proxy, PoW | | Tests | 29 | ~4,500 | Unit tests, integration tests | | Documentation | 48 | ~15,000 | Architecture, Research, Security | | Config/Deploy | 20+ | ~1,800 | Docker, Kubernetes, Helm | | **TOTAL** | **300+** | **~54,000** | Full-stack AI Security Platform | ### Engine Categories Breakdown | Category | Count | Key Engines | | ---------------------- | ------ | -------------------------------------------------- | | Classic Detection | 7 | injection, yara, behavioral, pii, query | | NLP / LLM Guard | 5 | language, prompt_guard, qwen_guard, hallucination | | Strange Math Core | 6 | tda_enhanced, sheaf, hyperbolic, spectral_graph | | Strange Math Extended | 6 | category_theory, chaos, differential_geometry | | VLM Protection | 3 | visual_content, cross_modal, adversarial_image | | TTPs.ai Defense | 8 | rag_guard, probing, tool_security, ai_c2, staging | | Advanced 2025 | 4 | attack_2025, adversarial_resistance, multi_agent | | Proactive Defense | 1 | proactive_defense | | Advanced Research | 10 | honeypot, canary, kill_chain, compliance, formal | | Deep Learning Analysis | 6 | activation_steering, hidden_state, llm_fingerprint | | Meta & Explainability | 2 | meta_judge, xai | | **Adaptive Behavioral** 🆕 | **2** | **attacker_fingerprinting, adaptive_markov** | | **TOTAL** | **60** | **Full detection engine suite** | ---

## License & Contact **Author:** Dmitry Labintsev **Email:** chg@live.ru **Telegram:** @DmLabincev Open to: **partnership**, **collaboration**, **research** ---

🛡️ SENTINEL — Because AI must be secure 🛡️

All the math. All the geometry. All the innovations.
This belongs to humanity now.

---

🚀 BUT THIS IS JUST THE BEGINNING 🚀

We will invent what you haven't seen before.
The future of AI security is being written — and we're holding the pen.

Stay tuned. The best is yet to come.

AISecurity

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