frida

What it is and what it does

Frida is a dynamic instrumentation toolkit that allows you to inject and execute code inside running processes without modifying or restarting them. The frida package provides Python bindings to Frida's core capabilities, letting you write Python scripts that attach to processes, load JavaScript instrumentation code, and interact with the running application in real time. It's commonly used for debugging, reverse engineering, security research, and runtime behavior analysis across multiple platforms.

The package wraps Frida's native instrumentation engine, which handles the low-level process attachment and code injection. You write your instrumentation logic in JavaScript (which Frida executes in the target process) and control it from Python. It supports a wide range of platforms and architectures, from desktop systems to mobile devices, making it a cross-platform tool for developers and security researchers who need to inspect or modify application behavior without access to source code.

Use it for:

• Debugging closed-source applications by injecting inspection code to trace function calls and monitor state changes
• Reverse engineering mobile or desktop applications to understand their behavior and APIs
• Security research and vulnerability analysis by instrumenting processes to observe runtime behavior
• Automated testing and quality assurance by injecting test hooks into running applications
• Performance profiling by instrumenting hot code paths to measure execution time and resource usage

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Python bindings for Frida, a dynamic instrumentation toolkit that lets you inject code into running processes on Windows, macOS, Linux, iOS, and Android to inspect and modify behavior at runtime.

Yes, if you need dynamic instrumentation capabilities. Frida is a mature, actively maintained tool (release 9 days old) with broad platform support and no known vulnerabilities. The license is OSI Approved but marked unclear—verify compatibility with your project. Install friction is moderate due to native library requirements, but precompiled wheels ease setup on common platforms. Ideal for reverse engineering, debugging, and security research workflows.

Install

frida on PyPI

pip install frida

uv add frida

poetry add frida

Installing frida

Before you install

Medium install friction: precompiled wheels available for multiple platforms (Windows, macOS, Linux on x86_64, ARM, and i686 architectures), but requires the underlying Frida native library. Active maintenance with a release 9 days old. Single lightweight runtime dependency (typing_extensions).

License in practice

Licensed under wxWindows Library Licence, Version 3.1, classified as OSI Approved but marked as unclear in treatment. Verify compatibility with your project's license requirements before use.

Quickstart

pip install frida
import frida
session = frida.attach('process_name')
script = session.create_script('send("hello")')
script.load()

Requires the Frida native runtime library to be installed on your system; Python 3.7 or later.

Verify before relying

• Whether wxWindows Library Licence, Version 3.1 is compatible with your project's licensing model
• Whether the Frida native library is available and compatible with your target platform
• Performance and stability characteristics under heavy instrumentation workloads

Package facts

Evidence: frida-17.17.0-cp37-abi3-macosx_10_13_x86_64.whl; frida-17.17.0-cp37-abi3-macosx_11_0_arm64.whl; frida-17.17.0-cp37-abi3-manylinux1_i686.whl; frida-17.17.0-cp37-abi3-manylinux1_x86_64.whl; frida-17.17.0-cp37-abi3-manylinux2014_aarch64.whl; frida-17.17.0-cp37-abi3-manylinux2014_armv7l.whl; frida-17.17.0-cp37-abi3-manylinux_2_17_aarch64.whl; frida-17.17.0-cp37-abi3-manylinux_2_17_armv7l.whl; frida-17.17.0-cp37-abi3-manylinux_2_5_i686.whl; frida-17.17.0-cp37-abi3-manylinux_2_5_x86_64.whl; frida-17.17.0-cp37-abi3-win32.whl; frida-17.17.0-cp37-abi3-win_amd64.whl; frida-17.17.0-cp37-abi3-win_arm64.whl

Keywords: frida, debugger, dynamic, instrumentation, inject, javascript, windows, macos, linux, ios, iphone, ipad, android, qnx

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