Every time you paste a snippet into an AI coding tool, you are making a network request. The safest habit you can build is to run a small boundary check on that snippet before it goes. Most leaks in AI-assisted development are not exotic model exploits but ordinary boundary mistakes that follow a boring pattern. An environment file resolves into context, a recent diff still carries a rotated credential, or a copy-paste drags a customer record into the prompt.
The Trust Boundary
The mental model starts with one question: where is your trust boundary? Your editor and your local agent sit on one side, and the model server sits on the other, so everything in your prompt is what you just handed across that line. Free model tiers and free server options change the economics but not the geometry of that boundary. A request still leaves your machine, and it lands on shared infrastructure that can be logged, retained, or inspected like any remote API call. The practical conclusion is to treat the remote model as an untrusted third party with a friendly interface, and to let that assumption drive what you include.
Think of the boundary in three layers, starting with the repository where secrets accumulate silently in diffs, comments, and test fixtures. The configuration layer is next, and this is where many setups are accidentally wide open because the assistant defaults to whole-repo visibility. The request layer is the payload that actually leaves, and it contains not just your prompt but tool results and any file contents the agent decided to attach. When a current DEV discussion asks what developers do while AI codes, the uncomfortable answer is that many of us audit this request layer after the fact instead of before it.
