Fast subdomain enumerator written in Rust

Hacker News

Hello! I'm learning Rust and also information security, so what better way to learn than to put a tool together practicing both skills? I've tried to use a couple of different tools for subdomain enumeration (part of the Discovery-party when it comes to penetration testing), but none of them were very fast, even if the DNS resolver I pointed them towards is, so I wrote my own tool that leverages concurrency to check a lot of subdomains, fast! It's written in Rust, main parts being using Tokio and async_channels, and I think it works good enough for a first release. I wanted to get recursive subdomain enumeration to work too, but couldn't figure out how to send into the same channels I'm reading from and knowing when to close the actual channels. Will work on that next! If you want to check out the project, it's located here: https://github.com/Esc4iCEscEsc/skanuvaty I'd love your feedback on the code, idea or anything in general! Usually people here have very good feedback, so I'm excited to hear what you think. Thank you!

Visit product View on Hacker NewsMar 15, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: using, code · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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