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Path-security – Comprehensive path validation with 62 attack vectors

Hacker News

Path-security – Comprehensive path validation with 62 attack vectors

Hi HN! Author here. TL;DR: Comprehensive path validation protecting against 62+ attack vectors including encoding attacks, Unicode tricks, and Windows-specific exploits. 95.81% test coverage, battle-tested in production. The motivation: We built a blockchain security platform that handles untrusted code repositories. Standard path validation wasn't enough - we kept discovering new attack vectors: - URL encoding: %2e%2e%2f → ../ - Double encoding: %252e%252e%252f → %2e%2e%2f → ../ - UTF-8 overlong: %c0%ae (invalid but sometimes accepted) - Unicode homoglyphs: ․․/ (using U+2024) - Windows NTFS streams: file.txt::$DATA - Trailing dots/spaces (Windows ignores these) - And 50+ more patterns After 6 months of production hardening, we extracted this into a standalone library. Every attack we've seen is tested (21 tests, 62+ patterns). *Stats*: - 62+ attack patterns covered - 95.81% test coverage - Zero dependencies (only anyhow) - Cross-platform compatibility - MIT/Apache-2.0 licensed *Links*: - Crates.io: https://crates.io/crates/path-security - GitHub: https://github.com/redasgard/path-security - Docs: https://docs.rs/path-security Perfect for web file uploads, archive extraction, git operations, and any application handling user-provided paths. Feedback very welcome!

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
77%77% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
52%52% 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 · Strong signals: platform · Missing: plus, intuitive, reviews
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
39%39% 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
15%15% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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