Shibuya
A High-Performance WAF in Rust with eBPF and ML Engine
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Hi HN,I’ve been working on Shibuya, a next-generation Web Application Firewall (WAF) built from the ground up in Rust.I wanted to build a WAF that didn't just rely on legacy regex signatures but could understand intent and perform at line-rate using modern kernel features.What makes Shibuya different:Multi-Layer Pipeline: It integrates a high-performance proxy (built on Pingora) with rate limiting, bot detection, and threat intelligence.eBPF Kernel Filtering: For volumetric attacks, Shibuya can drop malicious packets at the kernel level using XDP before they consume userspace resources.Dual ML Engine: It uses an ONNX-based engine for anomaly detection and a Random Forest classifier to identify specific attack classes like SQLi, XSS, and RCE.API & GraphQL Protection: Includes deep inspection for GraphQL (depth and complexity analysis) and OpenAPI schema validation.WASM Extensibility: You can write and hot-load custom security logic using WebAssembly plugins.Ashigaru Lab: The project includes a deliberately vulnerable lab environment with 6 different services and a "Red Team Bot" to test the WAF against 100+ simulated payloads.The Dashboard: The dashboard is built with SvelteKit and offers real-time monitoring (ECharts), a "Panic Mode" for instant hardening, and a visual editor for the YAML configuration.I'm looking for feedback on the architecture and the performance of the Rust-eBPF integration.
HN · launched Feb 23, 2026 · 22 upvotes (top 30%) · 0 views
Niche: security screening and access governance software · 54 launches, 6 in the last 7 days · Explore this niche →
Category and tags
- Security
- Dev tools
- B2B
- AI feature
- Commercial product
Visit website ↗ Source listing ↗
More like this: all 54 launches in security screening and access governance software →
Could you build this?
No Writing a line-rate Web Application Firewall leveraging eBPF in the Linux kernel alongside custom machine learning inference engines requires deep systems, networking, and low-level kernel expertise.
What it would actually take: A production implementation demands writing low-level XDP/eBPF programs in C/Rust attached to kernel network hooks, user-space ring buffers, OWASP Core Rule Set parsing, and ultra-low-latency ML inference (e.g., ONNX runtime or custom C/Rust tensor engines) operating in sub-millisecond budgets. It requires seasoned systems programmers with deep expertise in Linux kernel internals, network packet processing, and memory safety.
Discussion
18 comments analyzed.
Competitors mentioned: Cloudflare (free plan and enterprise), Other open-source WAF projects
Concerns raised: ML classifier too basic with only ~20 hardcoded payloads, unclear generalization to novel evasion, Lacks evaluation numbers against real traffic, Overly ambitious monetization roadmap seems unrealistic, AI-generated marketing copy and website, Unclear differentiation from existing solutions
Feature requests: Code execution tracking with context-aware allow/block policies, On-demand policy loading for specific execution contexts
Competitors
Other products that read as similar to this one : 263 launches clear the similarity bar, closest 8 shown.
Attention rank: #88 of 264 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 112 days after the earliest competitor.
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