daniele pelleri

Posted on Apr 1

I Built an Open-Source App to Detect & Block Invisible AI Meeting Transcription

#privacy #security #webdev #opensource

Invisible AI transcription is the fastest-growing privacy threat in remote work. I built Nullify to fight back.

The Problem

Tools like Granola ($1.5B valuation), Otter.ai (facing a class-action lawsuit), and Fireflies.ai can silently capture your meeting audio — no recording indicator, no consent prompt, no way for you to know.

These tools operate at the system audio level, completely bypassing platform indicators like Zoom's recording dot. Your 1-on-1s, salary discussions, and candid team conversations could all be captured and stored on third-party servers without your knowledge.

I discovered this firsthand when I found out a colleague was using Granola to silently transcribe all our team meetings — without telling anyone.

What Nullify Does

Nullify is a free, open-source desktop app for macOS and Windows that detects and blocks invisible AI meeting transcription tools.

Detect

Real-time process and network monitoring detects 8+ transcription tools the moment they activate:

Granola
Otter.ai
Fireflies
Read.ai
tl;dv
Fathom
Supernormal
Tactiq

Works across Zoom, Google Meet, Microsoft Teams, and any other platform.

Protect

Audio Shield uses psychoacoustic perturbation to make AI transcription produce garbled, unusable text — while your voice sounds perfectly normal to human participants.

4 protection levels from Stealth to Maximum let you choose the right balance.

How It Works

Nullify monitors your system for known transcription tool signatures (process names, network patterns)
When detected, you get an instant alert showing which tool is active
Activate Audio Shield to disrupt the transcription with psychoacoustic perturbation

Tech Stack

Electron + React 19 + TypeScript — cross-platform desktop app
Zustand for state management
Tailwind CSS 4 for styling
naudiodon (PortAudio bindings) for real-time audio processing
Custom DSP pipeline — FFT, psychoacoustic masking, phoneme injection, VAD

Architecture Highlights

The audio pipeline uses lazy-loaded native modules to avoid crashes before microphone permissions are granted. The perturbation engine runs a custom DSP chain:

Microphone Input → VAD (Voice Activity Detection)
    → FFT Analysis
    → Psychoacoustic Masking
    → Phoneme Injection
    → Virtual Audio Device Output

Everything runs 100% locally — no data ever leaves your machine.

Why It Matters

In 13 US states, recording without consent is illegal
Under GDPR, it violates data protection laws
Stanford has banned AI meeting bots entirely
Regardless of jurisdiction — you deserve to know when you're being recorded

Get Nullify

Website: nullify.guru
GitHub: github.com/khaoss85/nullify
License: MIT (free and open source)

Give it a star on GitHub if you find it useful, and let me know what features you'd like to see next!

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