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Code in Response to “The Trouble with Symlinks.”

Hacker News

Code in Response to “The Trouble with Symlinks.”

See: https://news.ycombinator.com/item?id=32190032 This was written in about the past hour or so; it has no documentation or test cases yet. Think twice before relying on it in production. The idea is that we can perform a detailed validation of the trustworthiness of an absolute or relative path, as a simple function that can be reused anywhere: I call this function safepath_check. A trustworthy path is one whose meaning cannot be changed by a third party: another user who isn't root. The path is therefore immune, for instance, to TOCtoTOU security problems, like the insertion of a symbolic link or other tampering. A trustworthy path is allowed to contain symbolic links. Symbolic links can be validated to be safe. To that end, safepath_check performs its own symlink resolution, to ensure that every link resolution step substitutes path material that is trustworthy.

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, code · Missing: mac, agents, macos
58%58% 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 · Missing: https docs, excited, just released
53%53% 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 · 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 · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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