AIOStack
Using eBPF to Secure AI Services in Kubernetes
Details
- External ID
- 46618481
- Source
- HN
- Company
- —
- Product
- AIOStack
- Website domain
- aurva.io
- Launched
- Jan. 14, 2026
- Cohort
- —
- Upvotes
- 9
- Upvotes percentile
- 0.461133069828722
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
Hey HN! We built a tool that uses eBPF to discover AI services and their data flows in Kubernetes clusters.Modern AI apps often follow this pattern: 1. Service receives request 2. Queries database (PostgreSQL/Redis/MongoDB) 3. Sends data to LLM API (OpenAI/Anthropic/Bedrock) 4. Consumes or returns the AI generated responseSecurity teams often don't know: - Which services are making AI calls - What databases they're accessing first - Whether PII is being sent to third-party APIs - What libraries and packages are being used for AIOur eBPF based tool attaches to network and fs syscalls to observe: - Outbound connections to AI API endpoints (pattern matching on domains/IPs) - Database protocol detection (PostgreSQL, MySQL, MongoDB wire protocols) - Service-to-service communication within the cluster - Libraries invoked by processes (PyTorch, HF, OpenCV etc)Architecture: - eBPF with C in kernel space - Go userspace agent processes events - Results sent to in-cluster exporter - Next.js for visualizationGitHub: https://github.com/aurva-io/AIOstack Demo: https://aurva.aiQuestions for you guys: 1. What classifications/buckets would you like to see for apps? 2. Other protocols/services we should detect? 3. Performance overhead-what's acceptable in prod?
Enrichment
- Theme
- AI agent frameworks and developer tools
- Vertical
- Security
- Function
- Observability & eval
- Audience
- B2B
- AI stance
- Not AI
- Project type
- Commercial product
- Normalized one-liner
- ebpf-based security for ai services in kubernetes
- Manually corrected
- False
Could you build this?
No Writing and deploying eBPF programs to safely hook Linux kernel network and socket events inside Kubernetes clusters to inspect TLS/HTTP traffic requires deep kernel systems engineering.
What it would actually take: The stack involves C/Rust (using Aya or libbpf) compiling eBPF bytecode for kernel-space kprobes, uprobes, and tracepoints (specifically hooking SSL/TLS read/write functions and socket operations) paired with a Go/Rust userspace daemon and Kubernetes operator. The hard part is managing verifier constraints across kernel versions, non-invasive TLS plaintext extraction, high-throughput packet processing, and low-overhead metadata tagging, requiring deep Linux kernel and network security expertise.
Discussion
1 comment analyzed.
Competitors
Other products that read as similar to this one — 98 launches clear the similarity bar, closest 8 shown.
Attention rank: #55 of 99 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 76 days after the earliest competitor.
- Cerberus · hn · 2025-12-20 · 12 upvotes · similarity 0.44
- Kestrel: AI Copilot for Kubernetes Ops & Security · yc · 2025-11-09 · 37 upvotes · similarity 0.44
- CargoWall · hn · 2026-03-31 · 14 upvotes · similarity 0.43
- Logira · hn · 2026-03-01 · 26 upvotes · similarity 0.43
- Shibuya · hn · 2026-02-23 · 22 upvotes · similarity 0.42
- Agentic interface for mainframes and COBOL · hn · 2026-05-12 · 97 upvotes · similarity 0.42
- Linnix · hn · 2025-11-11 · 21 upvotes · similarity 0.41
- KubeAstra–Open-source AI agent that debugs and recovers Kubernetes pods · hn · 2026-05-06 · 6 upvotes · similarity 0.41
Other launches for this product
- No other launches for this product.
Same idea, different domain
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