ClipboardShield – A Clipboard Firewall for Windows (FOSS)

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I’d like to introduce ClipboardShield [1], a free open-source clipboard firewall for Windows 10/11. It helps protect against clipboard hijacking attacks [2], where malicious apps silently read or modify clipboard contents (e.g., replacing copied cryptocurrency addresses to modify the recipient and steal user’s funds or stealing sensitive data like passwords). How it works: - Define rules to allow or block clipboard access for specific apps. - Prevent unauthorized apps from reading or modifying clipboard contents. - Open-source and community-driven – contributions are welcome! - Since many people copy and paste passwords, crypto addresses, and other sensitive data, ClipboardShield adds an extra layer of security by letting you control which applications can access the clipboard. Get Started: - GitHub repository: < https://github.com/CoinFabrik/ClipboardShield > - Quickstart guide: < https://github.com/CoinFabrik/ClipboardShield/blob/master/RE... > - First release (includes source code and binary installer): < https://github.com/CoinFabrik/ClipboardShield/releases/tag/2... > ClipboardShield uses a system-level hooking engine to manage clipboard access without interfering with normal usage [3]. [1] https://github.com/CoinFabrik/ClipboardShield [2] https://nordvpn.com/cybersecurity/glossary/clipboard-hijacki... [3] https://github.com/nektra

Visit product View on Hacker NewsMar 18, 2025

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

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

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Strong signals: started, vpn · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

26%26% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: crypto, cryptocurrency, introduce · Missing: web3, chat, make money

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

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