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ajipurn/fida

by Various

a local-first secret leak prevention layer for AI coding agents

MCP

ajipurn/fida

Added 4 July 2026

Overview

A local-first secret leak prevention layer for AI coding agents. It intercepts sensitive data before it leaves the developer's machine, using Rust for performance and low overhead. No external dependencies are required for basic operation.

Best for

Best for
Developers using local or remote AI coding agents who need a lightweight, self-hosted secret guard.

Use cases

  • Preventing API keys from being exposed in AI agent prompts
  • Filtering credentials before sending code to cloud-based AI assistants
  • Integrating secret scanning into local agent workflows

How to use

Install

curl -fsSL https://raw.githubusercontent.com/ajipurn/fida/main/install.sh | sh

Tools exposed

  • fida

Tested with

Codex, Claude Code, Cursor, OpenCode, GitHub Copilot, Windsurf, Antigravity, Kiro

Notes

A local-first secret leak prevention layer for AI coding agents. It intercepts sensitive data before it leaves the developer’s machine, using Rust for performance and low overhead. No external dependencies are required for basic operation.

15 stars on GitHub. Last updated 2026-06-28. Licensed MIT.

Use cases

  • Preventing API keys from being exposed in AI agent prompts
  • Filtering credentials before sending code to cloud-based AI assistants
  • Integrating secret scanning into local agent workflows

Pros

  • Runs entirely on-device, keeping secrets off remote logs
  • Lightweight and fast due to Rust implementation
  • Easy to integrate as a pipeline step for AI agents

Cons

  • Small community and limited real-world testing (15 stars)
  • No built-in policies for complex secret patterns beyond defaults
  • Requires manual setup and configuration for each agent toolchain

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Runs entirely on-device, keeping secrets off remote logs
  • Lightweight and fast due to Rust implementation
  • Easy to integrate as a pipeline step for AI agents

Cons

  • Small community and limited real-world testing (15 stars)
  • No built-in policies for complex secret patterns beyond defaults
  • Requires manual setup and configuration for each agent toolchain
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