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Squidbrake
Change control for AI agents: sign-off, undo and a record
Every action your AI agents take (commands, refunds, emails, database changes) goes on a tamper-evident record. Routine ones run; risky ones wait for a second person, who sees what led to it, with an undo. Claude Code, Cursor, Codex, MCP. Free, open source.
Hi Product Hunt 👋 I'm Pulkit, the builder of Squidbrake.
It started with a near-miss: during a test, my AI agent read an "urgent" email from the CEO and tried to wire $24,800. The email came from *acrne-corp.com*, not *acme.com*.
The agent did exactly what it was told. The terrifying part? Nobody else saw it, nobody signed off, and there was no audit trail to explain how it happened.
Enter Squidbrake: Change control and human-in-the-loop authorization for AI agents.
We are giving agents the ability to do powerful things, but we need a sign-off before they touch production. Squidbrake lets you set deterministic rules for what agents can and cannot do autonomously.
Here is how it works:
🛑 Human-in-the-loop for the scary stuff: Routine actions run on their own. Risky ones (`git push --force`, `DROP TABLE`, triggering refunds) pause and wait for a second person to approve via dashboard, Slack, or one tap on your phone. 🔍 See it before it happens: The approver sees exactly what will change *before* it happens (e.g., "deletes 4,312 rows of 10,240 in orders"). Destructive changes keep an undo state. 🛡️ Deterministic prompt injection defense: If an agent tries to send data to an address that was only mentioned in a web page or email, Squidbrake holds it and flags the source. ⚡ Zero-LLM latency: No LLMs in the decision path. Just plain YAML rules, meaning it’s predictable, fast, and free to run. 🪵 Tamper-evident audit trails: A tamper-evident record of which agent took the action, what it changed, who approved it, and the context that led to it.
It acts as one unified policy across Claude Code, Cursor, Codex, Gemini CLI, VS Code Copilot, Antigravity, and any MCP tool. Best of all, it’s open-source (Apache 2.0) and self-hosted; what your agents do never leaves your machine.
Try it out:
• No install: the "Open in Codespaces" button in our README.
• One line (installs it, connects your agents, opens the dashboard):
• Or: pipx install squidbrake, then squidbrake setup
Then ask your agent to run rm -rf ~/ and watch it get blocked.
I’d love to hear from the community:
1. What is the one action you would *never* want an agent to do without asking you first? 2. If your team is running agents that touch real systems (orders, payments, data, code), I’m setting up a few pilots. Reply here or DM me!
Thanks for checking it out! 🦑 Agents propose. People approve.
Proof, not promises: we replay 10 real public agent incidents against the shipped rules in CI; 12 of 13 harmful actions are stopped, and the one that isn't is written up in the repo.
Honest limit: it isn't a sandbox. For an agent you don't trust at all, add a container.
Squidbrake lets AI agents run on their own in prod, while anything risky (deleting data, force-pushing, moving money) waits for a human to approve. Works with Claude Code, Cursor, Codex, MCP tools and more.
In the age agents, an always on security layer sounds like a must have setup even for small companies. Definitely going to try this out on our platform.
As a company building agents for other consumer companies, we use squidbrake to make sure we never let agents take any destructive actions
About Squidbrake on Product Hunt
“Change control for AI agents: sign-off, undo and a record”
Squidbrake was submitted on Product Hunt and earned 19 upvotes and 7 comments, placing #29 on the daily leaderboard. Every action your AI agents take (commands, refunds, emails, database changes) goes on a tamper-evident record. Routine ones run; risky ones wait for a second person, who sees what led to it, with an undo. Claude Code, Cursor, Codex, MCP. Free, open source.
Squidbrake was featured in Open Source (69k followers), Developer Tools (521.5k followers), Artificial Intelligence (481.6k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 269.5k products, making this a competitive space to launch in.
Who hunted Squidbrake?
Squidbrake was hunted by Pulkit Batra. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Squidbrake stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Pulkit Batra
Hi Product Hunt 👋 I'm Pulkit, the builder of Squidbrake.
It started with a near-miss: during a test, my AI agent read an "urgent" email from the CEO and tried to wire $24,800. The email came from *acrne-corp.com*, not *acme.com*.
The agent did exactly what it was told. The terrifying part? Nobody else saw it, nobody signed off, and there was no audit trail to explain how it happened.
Enter Squidbrake: Change control and human-in-the-loop authorization for AI agents.
We are giving agents the ability to do powerful things, but we need a sign-off before they touch production. Squidbrake lets you set deterministic rules for what agents can and cannot do autonomously.
Here is how it works:
🛑 Human-in-the-loop for the scary stuff: Routine actions run on their own. Risky ones (`git push --force`, `DROP TABLE`, triggering refunds) pause and wait for a second person to approve via dashboard, Slack, or one tap on your phone.
🔍 See it before it happens: The approver sees exactly what will change *before* it happens (e.g., "deletes 4,312 rows of 10,240 in orders"). Destructive changes keep an undo state.
🛡️ Deterministic prompt injection defense: If an agent tries to send data to an address that was only mentioned in a web page or email, Squidbrake holds it and flags the source.
⚡ Zero-LLM latency: No LLMs in the decision path. Just plain YAML rules, meaning it’s predictable, fast, and free to run.
🪵 Tamper-evident audit trails: A tamper-evident record of which agent took the action, what it changed, who approved it, and the context that led to it.
It acts as one unified policy across Claude Code, Cursor, Codex, Gemini CLI, VS Code Copilot, Antigravity, and any MCP tool. Best of all, it’s open-source (Apache 2.0) and self-hosted; what your agents do never leaves your machine.
Try it out:
• No install: the "Open in Codespaces" button in our README.
• One line (installs it, connects your agents, opens the dashboard):
macOS / Linux: curl -fsSL https://pilots.squidbrake.com/install.sh | SQUIDBRAKE_REF=producthunt sh
Windows: $env:SQUIDBRAKE_REF="producthunt"; irm https://pilots.squidbrake.com/install.ps1 | iex
• Or: pipx install squidbrake, then squidbrake setup
Then ask your agent to run rm -rf ~/ and watch it get blocked.
I’d love to hear from the community:
1. What is the one action you would *never* want an agent to do without asking you first?
2. If your team is running agents that touch real systems (orders, payments, data, code), I’m setting up a few pilots. Reply here or DM me!
Thanks for checking it out! 🦑 Agents propose. People approve.
Proof, not promises: we replay 10 real public agent incidents against the shipped rules in CI; 12 of 13 harmful actions are stopped, and the one that isn't is written up in the repo.
Honest limit: it isn't a sandbox. For an agent you don't trust at all, add a container.