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Vulners.com, new vulnerability aggregator database

Hacker News

Vulners.com, new vulnerability aggregator database

Hi! One day, working with CVE-Details database I mentioned, that it's damn slow and has no lucene-style search. Using google is actually good...but takes too long to find security stuff. So..The idea was quite easy - to build my own vulnerability database with google-style search and a lot of sources. Here it is - https://vulners.com It's not just about CVE and security bulletins. It holds information about HackerOne bug bounty programs, hacker forums, vendor bulletins, different exploits databases, metasploit modules and so on. The most powerful feature is built-in dorks) Like this: All public Yahoo vulnerabilities: https://vulners.com/search?query=type:hackerone%20team.name:yahoo Or maybe SQL injections?: https://vulners.com/search?query=type:hackerone%20sql%20injection For linux users... Vulnerabilities in SUSE: https://vulners.com/search?query=type:suse Or some exploits for Windows 10: https://vulners.com/search?query=type:exploitdb%20%22windows%2010%22 Even take a look for info at the hackers forums, some articles by d0znpp?: https://vulners.com/search?query=type:rdot%20d0znpp Lets search for Metasploit modules for Apache: https://vulners.com/search?query=type:metasploit%20apache Quite easy, yes? And damn fast :) It is absolutely freeware, written with Python, Elasticsearch and MongoDB. Daily updates and upcoming 2.0 version :) Hope, you will like it. Any contacts and feature requests: isox@vulners.com

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, new · 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: google, users · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
37%37% 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
16%16% 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.

Incorrect prediction on native model

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