AI-runtime-guard – Policy enforcement layer for MCP AI agents

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I built this after realizing that AI agents with filesystem and shell access can delete files, leak credentials, or execute destructive commands — and there's no enforcement layer stopping them at the execution level. ai-runtime-guard is an MCP server that sits between your AI agent and your system. It enforces a policy layer before any file or shell action takes effect. No retraining, no prompt engineering, no changes to your agent or workflow. Your agent can say anything. It can only do what policy allows. What it does: - Blocks dangerous commands (rm -rf, dd, shutdown, privilege escalation) before execution - Gates risky commands behind human approval via a web GUI - Simulates blast radius for wildcard operations before they run - Creates automatic backups before destructive actions - Full audit trail of everything the agent does Works with Claude Desktop, Cursor, Codex, and any stdio MCP-compatible client. Default profile is basic protection out of the box — advanced tiers are opt-in. Validated on macOS Apple Silicon. Linux expected to work, formal validation coming in v1.1. Would love feedback from anyone running AI agents with filesystem access.

Visit product View on Hacker NewsFeb 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, macos · Missing: model, agentic, slack

97%97% 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: compatible · Missing: supports, reddit linkedin, podcasting

70%70% 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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

26%26% 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

25%25% predicted probability of success on AppSumo, 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.

Correct prediction on native model

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