Put a Policy Gateway Between Your Coding Agent and the LLM
Your coding agent talks to a model provider over HTTPS. That connection is a straight line: the agent asks, the provider answers, the answer lands in your editor. Nothing in the middle looks at what came back. For most of what an agent produces, that's fine. For the rest of it — the query built by string concatenation, the API key the model helpfully echoed back into a code sample, the eval() on…
Your coding agent communicates with a model provider through a secure HTTPS connection. This direct line connection is fine for most tasks, but for certain queries that involve string concatenation, API key embedding in code samples, or eval() on user input, the response needs inspection before it reaches you. To achieve this, you can implement a policy gateway, which acts as a local proxy between your agent and the provider.
This gateway, built using Cencurity Engine, examines the response stream and decides whether to allow, redact, or block the content before it reaches you. The gateway is written in Go and runs entirely on your machine, and it does not require you to provide an API key, ensuring that credentials remain secure. To implement the gateway, you need Go installed, an API key for the model provider, and an agent or IDE that allows you to change the API base URL.
Once you have these prerequisites, you can start the gateway using Go, specifying the listen port, upstream provider URL, and policy file. After starting the gateway, you can verify that it is functioning correctly and has the correct rule count. Then, you can point your agent to the gateway's URL instead of the provider's URL. This redirection will allow the gateway to inspect and control the traffic.
To ensure that the gateway is working as intended, you can perform tests such as asking the model for ordinary tasks, secret printing, or eval() with potentially dangerous input. Each of these tests should trigger the appropriate action (allow, redact, or block) and be logged accordingly. Finally, you can create your own rules in the policy file, which is a JSON file.
Each rule consists of six fields, including a unique ID, category, severity level, action (allow, redact, or block), a regex pattern for matching sensitive content, and an enabled status. Once you have written your custom rules, you can reload them automatically when the gateway detects changes. This process allows you to add a policy layer between your coding agent and the LLM, providing an additional layer of security and control over the data being processed.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
