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ExtensionWatch, a browser extension to watch for malware extensions

Hacker News

ExtensionWatch, a browser extension to watch for malware extensions

I spent the weekend hacking out a Chrome extension (which will live at https://github.com/extensionwatch/chrome later tonight) that will hopefully help reduce the proliferation of adware and malware. I plan to hack one out for Firefox and friends in the future, too. My approach is fairly simplistic right now (hooks into Chrome's extension installation and disables a blacklisted extension as soon as it's installed, updated, or recognized after a database update), but as I'm digging more into these nasty extensions, I'm seeing some patterns in the DOM injection that I'm also going to watch for separately. So, while an extension block list will catch the dumb extension owners or those who sell to adware companies, the more sophisticated analysis of the DOM and watching for script/load requests to certain domains should at least alert the user to the presence of certain adware (even if I can't track down exactly which extension may be causing it). But I need your help! The database of malware extensions and malware-peddling domains that powers this whole thing lives over at https://github.com/extensionwatch/database. I need you all to help me fill in the current crop of evil extensions and adware dispensing domains. The database will live in this repository, be built using a Rake task, and deployed to users of the extension. The idea is to have a completely open and collaborative repository of this stuff to alleviate a lot of the (justified) "who's watching the watchers" paranoia. Anyhow, feel free to file issues on either of those repositories to start discussions. I'll be around the comments this afternoon, too, and I'm also available via e-mail at my HN username at gmail.

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
64%64% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting
62%62% 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: user, using, open · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: users, para · 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: soon, users · Missing: plus, platform, intuitive
44%44% 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
14%14% 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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