American Fuzzy Lop
Summary
American Fuzzy Lop (AFL) is a prominent open-source fuzzing tool, developed by Michał Zalewski, used to find security vulnerabilities in software. Fuzzing is a technique where random or malformed inputs are fed into programs to detect bugs. AFL is known for its efficiency and effectiveness in uncovering a range of vulnerabilities, such as buffer overflows and memory leaks. It employs genetic algorithms and program instrumentation to evolve inputs and monitor program execution, focusing on areas less explored in testing. AFL's user-friendly design and potent testing capabilities have made it a popular choice in software development and security auditing. It has significantly impacted software security practices and inspired the development of other advanced fuzzing tools and methods.
Requirements
- Operating System: Best on UNIX-based systems, especially Linux.
- Software Dependencies: GCC, GNU Make, and standard Unix development tools.
- Instrumentation Requirement: Programs need recompilation with AFL's compiler for instrumentation.
- Hardware Resources:
- Memory: Adequate RAM for handling multiple fuzzing instances.
- CPU: Multi-core CPU for parallel fuzzing.
Description
How does American Fuzzy lop work
- Instrumentation: AFL modifies the code of the target program to monitor its execution.
- Input Generation: It generates input data to test the program, starting from seed files.
- Execution Monitoring: AFL tracks which parts of the code are executed with the test inputs.
- Feedback-Driven: It uses a genetic algorithm to evolve test cases, favoring inputs that explore new code paths.
- Crash Detection: AFL identifies inputs that cause crashes or errors in the program.
- Parallel Fuzzing: AFL supports running multiple instances in parallel for faster testing.
- Results Analysis: The tool provides inputs that trigger vulnerabilities for developers to fix.
Step 2
Make sure to read
- War and Peace
- Lord of the Rings
- The Baroque Cycle
Used Hardware
Device to be used with this documentation Maybe another device to be used with this documentation
Courses
- A course where this documentation was used (2017, 2018)
- Another one (2018)