# AI AdBlocker

> AI-powered blocker (your path) Hybrid rules + on-page heuristic for Firefox MV2. Uses a machine learning model (running in the browser with ONNX Runtime) to see or analyze the DOM.

Canonical page: [https://chrome-stats.com/d/ai-adblocker](https://chrome-stats.com/d/ai-adblocker)

## Overview

- **ID:** `ai-adblocker`
- **Platform:** Firefox
- **Type:** Firefox add-on
- **Status:** Available
- **Publisher:** eye3
- **Category:** web-development,privacy-security,search-tools
- **Daily users:** 6
- **Version:** 0.1.0
- **Last updated:** 2025-08-21
- **First published:** 2025-08-18
- **Size:** 2.89 MB
- **Data as of:** 2026-09-11
- **Store listing:** [Firefox Add-ons Store](https://addons.mozilla.org/firefox/addon/ai-adblocker/)
- **Website:** [https://github.com/lbeincuk/ai-adblock-starter](https://github.com/lbeincuk/ai-adblock-starter)
- **Privacy policy:** [https://addons.mozilla.org/firefox/addon/ai-adblocker/privacy/](https://addons.mozilla.org/firefox/addon/ai-adblocker/privacy/)

## Description

This add-on is a starter AI Ad Block extension.
 • The uploaded .zip is the Firefox-compatible build (no rules/ directory, uses background.scripts instead of service_worker).
 • The uploaded source .zip contains the full, human-readable source code (TypeScript, scripts, manifests, build files) without dist/ or node_modules/.
 • No remote code execution, dynamic code generation, or obfuscated code is used.
 • Can detect hidden ads, sponsored labels, promoted posts, native ads that rule-based systems miss.
 • Can adapt better if you re-train the model with new data.
Example AI Features You Can Add DOM / Heuristic Classifier:
Train a lightweight ML model on HTML snippets (features: tag type, attributes, text like “Sponsored”).
Content script grabs candidate nodes → runs model → hide if classified as ad.
Vision-based Ad Detection:
Use a small CNN (e.g., MobileNet/ONNX quantized) to check if an <img> looks like a banner ad.
Useful for “image-only” ads where markup doesn’t give them away.
Hybrid (most practical):
Use heuristics to filter likely candidates (divs with fixed size, suspicious classes, “sponsored” text).
Use ML to confirm → avoid false positives.
Future-proofing: when advertisers obfuscate HTML/CSS, rules break → but your ML model still generalizes.
Privacy-preserving: everything runs locally in the browser; no need to send page data to servers.
Research value: positions your extension as “next-gen” ad blocker, different from commodity ones.

## Rankings

- #42,361 — Overall
- #145 — adblocker
- #187 — adblock
- #249 — ad-blocking

## Permissions and access

### Permissions

- `webRequest`
- `webRequestBlocking`
- `storage`
- `<all_urls>`

### Content script matches

- `<all_urls>`

## Safety

- **Risk impact:** High risk impact
- **Risk likelihood:** Moderate risk likelihood

### Analysis details

- Allows access to all websites, posing a significant security risk as it can monitor and modify data from any visited site.
- This extension has low user count. Unpopular extensions may not be stable or safe.

> Some risk analysis details are omitted from this free response. [Upgrade to view the full analysis](https://chrome-stats.com/pricing).

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Source: [Chrome-Stats](https://chrome-stats.com/d/ai-adblocker)