aflpp

Fuzz C/C++ project inputs with AFL++ to uncover defects.

Updated Mar 27, 2026
One-click install
npx skills add https://github.com/erenisiklar/Pusula --skill aflpp-erenisiklar
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: aflpp
Source: https://github.com/erenisiklar/Pusula/tree/main/.claude/skills/aflpp
Command: npx skills add https://github.com/erenisiklar/Pusula --skill aflpp-erenisiklar

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AFL++ provides advanced fuzzing capabilities to uncover security and robustness issues by efficiently mutating inputs across multi-core environments.

Core Features & Use Cases

  • Multi-core fuzzing for scalable throughput across CPUs
  • Advanced mutation strategies and stable fuzzer tooling
  • Use Case: fuzzing production C/C++ projects to find memory corruption and logic bugs

Quick Start

Run a fuzz campaign against your target harness using AFL++ to maximize input coverage across cores.

Frequently Asked Questions about aflpp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run multi-core fuzzing for C/C++ security testing?

Multi-core fuzzing for C/C++ security testing is executed by compiling target harnesses with LLVM/Clang support and launching AFL++ campaigns to mutate inputs across multiple CPUs, maximizing input coverage and uncovering memory corruption defects.

What is fuzzing harness compilation and when do I need it for security testing?

Fuzzing harness compilation is the process of building a target interface using a compiler like LLVM/Clang, required before running security testing to expose C/C++ project functions so that fuzzers can mutate inputs and detect logic bugs.

Do I need LLVM and Clang to compile fuzzing harnesses for AFL++?

Yes, LLVM and Clang are required to compile fuzzing harnesses for AFL++, providing the necessary build environment and instrumentation to mutate inputs and uncover memory corruption issues in large-scale fuzzing scenarios.

Can I use multi-core fuzzing to find memory corruption bugs in large-scale projects?

Yes, multi-core fuzzing can uncover memory corruption bugs in large-scale C/C++ projects by leveraging advanced mutation strategies and scalable throughput across available CPUs to process diverse inputs efficiently.

What is the best way to achieve scalable throughput during a fuzz campaign?

The best way to achieve scalable throughput during a fuzz campaign is utilizing multi-core fuzzing with AFL++, which distributes diverse mutation strategies across CPUs to maximize input coverage and accelerate defect discovery.

Why does fuzzing a production C/C++ harness require multi-core environments?

Fuzzing a production C/C++ harness requires multi-core environments to handle the computational intensity of diverse mutation strategies, ensuring stable tooling and scalable throughput necessary to uncover robustness issues in large-scale fuzzing scenarios.