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Leash – Security guardrails for AI coding agents

Hacker News

Leash – Security guardrails for AI coding agents

AI coding agents like Claude Code can hallucinate dangerous commands. Wrong paths, wrong directories, and suddenly you're running "rm -rf ~/" instead of "rm -rf ./". I built Leash to catch these mistakes before they execute. It's a simple hook that runs before each tool call. Blocks dangerous commands outside the working directory, protects sensitive files like .env and .git even inside the project, and stops destructive git operations like reset --hard or push --force. Some things it handles that weren't obvious at first: agents doing "cd ~/Downloads && rm -rf folder" to escape the sandbox, compound patterns like "find -delete" or "xargs rm" targeting paths outside the project, and symlink-based escapes. Works with Claude Code, OpenCode, Pi, and Factory Droid. Setup is just "npm install -g @melihmucuk/leash" followed by "leash --setup claude-code". Not a full sandbox. If you need real isolation, use containers. This just catches the common hallucination patterns that cause accidental damage. https://github.com/melihmucuk/leash

Visit product View on Hacker NewsDec 24, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

94%94% 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 HackersFits the IH revenue-focused audience · Strong signals: mistakes · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, 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

30%30% 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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

25%25% 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

23%23% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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