• Posted by: Eng. Donya Bino
• February 15, 2025

Fuzzing with Radamsa and AFL: Efficient Vulnerability Discovery

Security vulnerabilities in software can lead to critical exploits, making robust testing essential. Fuzzing is a powerful technique that helps uncover hidden bugs by injecting unexpected inputs into applications.

Among the most effective fuzzing tools are Radamsa and American Fuzzy Lop (AFL). These tools automate test case generation, maximizing code coverage and exposing vulnerabilities that traditional testing might miss.

This article explores how Radamsa and AFL work, their differences, and how to use them for ethical hacking and software security testing.

What is Fuzzing?

Fuzzing is an automated testing technique that feeds randomly generated or mutated inputs into a program to identify crashes, memory leaks, or security flaws. It helps uncover vulnerabilities like:

1. Buffer overflows
2. Use-after-free errors
3. Null pointer dereferences
4. SQL injection and XSS (for web applications)

Fuzzers like Radamsa and AFL differ in their approach but complement each other in discovering security weaknesses.

Radamsa: A Mutation-Based Fuzzer

Overview

Radamsa is a lightweight mutation-based fuzzer that generates test cases by altering existing valid inputs. It is simple yet highly effective for testing file parsers, protocols, and applications that handle structured data.

Key Features of Radamsa:

1. Fast and efficient in generating mutated test cases
2. Supports various file formats and protocols
3. Easy to integrate into automated testing pipelines

How to Use Radamsa for Fuzzing

1. Install Radamsa on Linux:

sudo apt install radamsa

Or manually compile from source:

git clone https://gitlab.com/akihe/radamsa.git

cd radamsa

make && sudo make install

2. Generate Fuzzed Inputs:

radamsa sample_input.txt > fuzzed_input.txt

This mutates sample_input.txt and creates a new test file with unexpected variations.

3. Automate Fuzzing in a Loop:

while true; do radamsa sample_input.txt | target_program; done

This continuously feeds fuzzed data into the target application to test for crashes.

AFL: A Coverage-Guided Fuzzer

Overview

AFL (American Fuzzy Lop) is an intelligent, coverage-guided fuzzer that dynamically mutates inputs while monitoring code execution to maximize test coverage. Unlike Radamsa, AFL uses feedback from the application to refine its fuzzing strategy.

Key Features of AFL:

1. Coverage-guided fuzzing ensures deeper testing
2. Automatically detects and prioritizes new execution paths
3. Finds complex security flaws beyond simple crashes

How to Use AFL for Fuzzing

1. Install AFL on Linux:

sudo apt install afl

2. Compile the Target Program with AFL’s Instrumentation:

afl-gcc -o target_program target_program.c

This step enables AFL to track execution paths.

3. Run AFL with a Set of Inputs:

afl-fuzz -i input_directory -o output_directory -- ./target_program @@

-i specifies the input directory (contains seed test cases)

-o specifies the output directory (stores found crashes and hangs)

@@ tells AFL where to inject the test case

4. Monitor AFL’s Execution:
   AFL provides a real-time dashboard showing crash counts, execution paths, and progress.

Radamsa vs. AFL: Key Differences

1. Fuzzing Type: Radamsa is mutation-based, while AFL is coverage-guided.
2. Automation Level: Radamsa generates random mutations, whereas AFL uses execution feedback to refine test cases.
3. Complexity: Radamsa is simple to use, while AFL requires program instrumentation.
4. Speed: Radamsa is faster, but AFL is more precise.
5. Use Cases: Radamsa is ideal for file fuzzing and protocol fuzzing, while AFL is better for binary fuzzing and advanced vulnerability discovery.

Combining Radamsa and AFL for Better Results

Using both fuzzers together can enhance security testing. A common approach is to start with Radamsa for rapid input mutation and follow up with AFL for deeper, coverage-based fuzzing. This combination helps uncover a wider range of vulnerabilities efficiently.

Fuzzing is a critical technique for uncovering software vulnerabilities, and tools like Radamsa and AFL provide powerful ways to automate this process. While Radamsa excels at quick mutation-based fuzzing, AFL offers deeper analysis with its coverage-guided approach. Ethical hackers and security researchers can leverage both tools to improve software security and prevent potential exploits.

For organizations prioritizing security, integrating these fuzzing techniques into the development lifecycle can significantly enhance software resilience against real-world attacks.