GuardiAgent – Sandboxing / permission model for MCP servers

Hacker News

Anthropic's Model Context Protocol (MCP) has made it easy to spin up servers that expose tools and data to LLMs. A lot of these MCP servers run locally because they need access to your files, shell, browser, etc. The problem: they typically run with the same privileges as your user. If a server is buggy, misconfigured, or prompt-injected, it can do anything you can do: read SSH keys, exfiltrate dotfiles, poke around in private repos, etc. Our research group is working on this by adding a security manifest (inspired by the Android app manifest) plus a local policy enforcement engine that sandbox MCP servers. You can specify which hosts they can reach, which files/directories they can read/write, and so on, instead of giving them full user-level access. Code and docs: https://github.com/orgs/GuardiAgent/repositories https://www.guardiagent.com Curious how others are locking down agents/tools today and what you'd want from a system like this.

Visit product View on Hacker NewsNov 21, 2025

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

92%92% 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 · Missing: supports, reddit linkedin, podcasting

46%46% 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: io · Missing: https docs, excited, just released

43%43% 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: plus, host · Missing: platform, intuitive, reviews

42%42% 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

34%34% 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

17%17% 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.

Incorrect prediction on native model

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