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Browser-based XSS scanner

Hacker News

Browser-based XSS scanner

This is a simple single-file python program that can find basic XSS (cross-site scripting) vulnerabilities in a target url. Most XSS discovery tools use a payload refelection strategy in which payloads are injected in url parameters and the GET response is inspected for places where the payload content is reflected. This is a very low precision XSS detection strategy because most reflection does not support execution. This program uses a different approach, and instead opens the target url in a browser, tests alert(...) payloads directly in the browser context, and listens for an alert being triggered. This means that any XSS spotted by this program is extremely unlikely to be a false positive.

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Actual performance

57points
2comments
Made the leaderboard

Launch Intel predictions

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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntUnlikely to reach the leaderboard · Strong signals: context, single, open · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
39%39% 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 HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting
36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para, scanner · Missing: mobile apps, ios, personal
29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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
9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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