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Runtime Defense Against Prompt Injection in Supabase MCP

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Runtime Defense Against Prompt Injection in Supabase MCP

I wrote this after studying the Supabase MCP prompt injection issue. The blog shows how I built a working defense using an open-source AI agent runtime I’ve been building called Tansive ( https://github.com/tansive/tansive ) Instead of just filtering malicious prompts, I implemented role-based policies with runtime input validation that can scale across combinations of different AI tools (GitHub, Stripe, Linear, etc.). All the code referenced in the blog is in the examples/supabase_demo folder. I welcome your feedback — especially from folks working with AI toolchains or security.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, mcp, stripe · Missing: mac, agents, macos
88%88% 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 · Missing: supports, reddit linkedin, podcasting
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TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
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Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
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