aixk / BAAR3-guard

BAAR3-guard

🌐 Official Website: https://baar.uk/model/
📄 License: Non-Commercial Only (Contact for Commercial Pricing)

📌 Model Overview

BAAR3-guard is a sub-millisecond, real-time in-line zero-trust security engine designed to operate directly at edge points of presence (PoP), API Gateways, and CDN reverse proxies.

Unlike asynchronous out-of-band detection or log analysis systems, BAAR3-guard inspects incoming requests with an average latency of ~0.43ms . It intercepts malicious threats in-line at the edge before traffic reaches origin backend services. It is packaged in native .baar3 FP16 format as well as WASM/ONNX formats, allowing zero-copy in-memory execution in constrained edge environments such as Envoy proxy filters, NGINX modules, and Cloudflare Workers.

📊 Live Verification & Benchmarks

Hardware: Single NVIDIA GPU Instance | FP16 TensorCore Native JIT Engine (.baar3)

📈 Measured Benchmark Summary

• Class Identification: Normal Traffic (Human), Autonomous Reconnaissance (Recon), BOLA/IDOR Parameter Tampering, and Credential Stuffing.

🚀 In-Line Deployment & Usage

BAAR3-guard evaluates every incoming HTTP request and outputs a composite threat score (0 to 100) to execute immediate actions at the proxy level.

1. In-Line Decision Logic

• Score >= 60.0 (BLOCK 403) : Immediate connection termination, TCP RST, or 403 Forbidden.
• Score >= 35.0 (CHALLENGE 429) : Suspicious traffic; issues a client-side challenge (WASM PoW or CAPTCHA).
• Score < 35.0 (ALLOW 200) : Legitimate user; forwards request to upstream microservices without delay.

2. Integration Example (Python In-Line Worker)

import torch import numpy as np from baar3_guard import FastBAAR3Engine, RobustSecurityScaler

1. Initialize once at worker boot (Zero-Copy Pinning)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu") guard_engine = FastBAAR3Engine("./baar3_guard_artifacts/model_guard_fp16.baar3", device=device) scaler = RobustSecurityScaler()

2. Reverse Proxy / Gateway in-line inspection hook

def inline_traffic_inspector(request): macro_feat = request.get_macro_context() micro_feat = request.get_micro_context()

micro_scaled = scaler.transform(micro_feat)

xm = torch.from_numpy(macro_feat).to(device).half()
xc = torch.from_numpy(micro_scaled).to(device).half()

# Sub-millisecond evaluation
comp_score, threat_prob, class_probs = guard_engine.inspect(xm, xc)
score = float(comp_score[0].item())

# In-line execution
if score >= 60.0:
    return Response(status_code=403, body={"error": "Access Denied by In-Line Zero-Trust Guard"})
elif score >= 35.0:
    return Response(status_code=429, body={"challenge": "WASM_POW_REQUIRED"})

return forward_to_backend_service(request)

📄 License & Commercial Terms

• Non-Commercial Use Only :
  This model is strictly provided for non-commercial, research, and educational purposes only . Any commercial use without a separate commercial agreement is strictly prohibited.
  본 모델은 순수 연구, 교육 및 개인 비영리 목적에 한해서만 무료로 제공 됩니다. 사전 허가 없는 일체의 영리적 이용은 엄격히 금지됩니다.

• Commercial Licensing & Inquiries for Revenue-Generating Entities :
  If your organization generates revenue, or if you plan to incorporate this model into commercial products, services, or paid APIs, you are required to purchase a commercial license. Please contact us directly via email for enterprise licensing and integration support.
  기업/사업체에 매출이 발생하고 있거나, 본 모델을 상용 서비스·유료 제품·수익 창출 목적의 시스템(WAF, API Gateway, CDN 보안 모듈 등)에 도입하고자 하시는 경우 반드시 별도의 상용 라이선스를 취득하셔야 합니다. 도입 조건 및 기업용 라이선스 발급은 아래 이메일로 문의해 주시기 바랍니다.

  • Commercial Inquiries (도입 및 상용 라이선스 문의) : [email protected]
  • Official Website (공식 웹사이트) : https://baar.uk/model/

⚠️ Notes

• In-Line Engine : This engine is engineered specifically for L7 real-time traffic filtering with minimal memory footprint.
• Artifacts Provided : Includes native model_guard_fp16.baar3 , model.onnx (Envoy WASM and ONNX Runtime compatible), and model.safetensors .

💼 Opportunities & Contact

This project demonstrates practical competency in designing custom micro-scale architectures, end-to-end security modeling, and ultra-low-latency deployment under extreme compute constraints.

I am actively seeking AI Engineering / Research opportunities, recruitment offers, and technical partnerships .

• Official Website (공식 웹사이트) : https://baar.uk/model/
• Commercial Adoption (기업 도입 및 라이선스 문의) : [email protected]
• Recruitment & Hiring (채용 및 포지션 제안) : [email protected]
• Investment & Partnerships (투자 및 기술 협력 문의) : [email protected]