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EngineeringAugust 12, 2026·6 min read

Automating Attack Surface Reconnaissance: Async Python & Streaming LLM Synthesis

Authored by CyberSolu Systems

Traditional perimeter reconnaissance tools suffer from a fundamental bottleneck: synchronous network probes crawl through targets sequentially, leaving security analysts buried under mountains of fragmented raw port dumps, certificate logs, and HTTP header output.

When engineering SentinelScan, our goal was to bridge high-throughput asynchronous networking with real-time generative data synthesis. We built a custom Python reconnaissance backend leveraging asyncio and aiohttp to fire over a hundred concurrent DNS checks, non-blocking TCP socket validations, and heuristic sensitive endpoint probes simultaneously.

Rather than forcing users to wait for a monolithic scan report, the backend streams synthesized findings to a Next.js 14 frontend using Server-Sent Events (SSE). The raw network telemetry is contextualized on the fly against OWASP Top 10 classifications, delivering an executive-ready risk report in under 20 seconds.

All telemetry and finalized audits are persisted in Supabase PostgreSQL, creating permanent, shareable incident audit links without requiring intensive re-scans.