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A Firewall for AI agents with auditing

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A Firewall for AI agents with auditing

Hi all, As there are more and more agents in the internet; Security is going to be a big problem. Currently, the problem is solved using a LLM to guard Agent but this creates the problem of hallucination and latency, so I coded a firewall in rust that runs under five miliseconds. This works by creating a plan and enforcing the plan; for per action call, this enforces using the Model context protocols list and for sequence it tracks every single tool call and data flow; there is also a taint mechanism where if the agent reads something outside of the user context, it flags and adds more security mechanism. It works by using a DAG.

Visit productView on Hacker NewsJun 29, 2026
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
96%96% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
50%50% 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
36%36% 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
33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% 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
6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

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