Free tier is live now.
Takes ~30 seconds to install.
If you’re running agents daily and have ever thought “I hope it doesn’t do something stupid”… this is for you.
Try it -> https://t.co/OYuvnZGHhV
Reply with the riskiest thing your agent has tried. I’m collecting stories.
And it doesn’t stop there.
After the tool returns, Confire inspects the result.
It flags:
Secret-looking values
Prompt-injection patterns
Hidden Unicode / zero-width tricks
Then it feeds clean security context back to the agent so it treats the output as data… not instructions.
We’re also adding cryptographically signed provenance receipts (think: tamper-evident audit trail for every decision + remote rule you apply).
So when your team uses shared policies from the dashboard, you can actually verify they weren’t tampered with.
That’s why we built Confire.
It’s a local-first context & tool firewall that sits inside your agent workflow. Before any risky tool call runs, Confire reviews it.
Force push? Destructive MCP action? db reset? Repo deletion?
It can warn, review, or block – based on built-in rules + your custom policy.
I caught it at the last second.
But here’s the scary part: Most of us have no guardrail between “agent has a clever idea” and “agent executes something irreversible on our machine or repos.”
We just hope it doesn’t do anything too stupid.
I almost let my AI coding agent destroy a production branch last week.
It was 2 a.m. I had Cursor + Claude Code running with “skip permissions” on (like a lot of us do).
The agent said: “Let me clean this up real quick.”
I said go ahead.
It was about to run: git push --force origin main
Confire is a local-first context & tool firewall specifically for AI coding agents. It intercepts risky tool calls (e.g., force pushes, destructive MCP actions, db resets) before execution, inspects/sanitizes tool outputs (secrets, injection-like patterns, hidden Unicode), and feeds security context back to the agent. It integrates with Claude Code, Cursor, VS Code, and many others, with strong emphasis on local evaluation for trust.
Treats tool output as data, not instructions. Gives the agent (and user) extra security context so it can reason safely. Local-first by design. Rule evaluation, secret redaction, and injection detection happen on-device.