Hexora – static analysis tool for malicious Python scripts

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I made a new library to detect malicious and harmful behaviour in Python scripts. There are alternative libraries, but they usually rely on regexes, which can be fragile and tricked. My library uses AST and tracks some of the obfuscation techniques, such as import/call reassignment. Currently, I'm testing it on public files where some of them implement malicious behavior, as well as past malicious packages on PyPI. You can see some of the detection examples here: https://github.com/rushter/hexora/blob/main/docs/examples.md I'd love to hear your feedback and ideas on how to improve this and identify missing rules.

Visit product View on Hacker NewsAug 19, 2025

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

56%56% 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 NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

39%39% predicted probability of success on Product Hunt, 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

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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