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VellaVeto — blocks unsafe MCP tool calls by default

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VellaVeto — blocks unsafe MCP tool calls by default

Reposting because the March thread linked a now-outdated repo, and the project has changed substantially. VellaVeto is a fail-closed gateway between an AI agent and its MCP tools. Every tool call is evaluated before execution; if evaluation fails for any reason, the call is denied. Concrete example: under shield mode, a filesystem server trying to read ~/.ssh/id_rsa is denied by default. A list_files /tmp call from the same server is allowed. What it does not solve: prompt injection, model-level jailbreaks, or supply-chain attacks in server packages. It only controls what crosses the tool-call boundary. Since March, I added three zero-config protection levels, topology discovery, tamper-evident audit, Consumer Shield, and MCPSEC, an open benchmark for MCP gateways. The feedback I’d most like: Is the tool-call boundary the right place to enforce MCP security? Are the MCPSEC attack classes sensible? What important attack classes am I still missing?

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, mcp · Missing: mac, agents, macos
80%80% 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
53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, filesystem, ide · 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.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

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