AFL++

AFL++ is a fork of the original AFL fuzzer that offers better fuzzing performance and more advanced features while maintaining stability. A major benefit over libFuzzer is that AFL++ has stable support for running fuzzing campaigns on multiple cores, making it ideal for large-scale fuzzing efforts.

When to Use

Fuzzer Best For Complexity

AFL++ Multi-core fuzzing, diverse mutations, mature projects Medium

libFuzzer Quick setup, single-threaded, simple harnesses Low

LibAFL Custom fuzzers, research, advanced use cases High

Choose AFL++ when:

• You need multi-core fuzzing to maximize throughput

• Your project can be compiled with Clang or GCC

• You want diverse mutation strategies and mature tooling

• libFuzzer has plateaued and you need more coverage

• You're fuzzing production codebases that benefit from parallel execution

Quick Start

extern "C" int LLVMFuzzerTestOneInput(const uint8_t data, size_t size) { // Call your code with fuzzer-provided data check_buf((char)data, size); return 0; }

Compile and run:

Setup AFL++ wrapper script first (see Installation)

./afl++ docker afl-clang-fast++ -DNO_MAIN=1 -O2 -fsanitize=fuzzer harness.cc main.cc -o fuzz mkdir seeds && echo "aaaa" > seeds/minimal_seed ./afl++ docker afl-fuzz -i seeds -o out -- ./fuzz

Installation

AFL++ has many dependencies including LLVM, Python, and Rust. We recommend using a current Debian or Ubuntu distribution for fuzzing with AFL++.

Method When to Use Supported Compilers

Ubuntu/Debian repos Recent Ubuntu, basic features only Ubuntu 23.10: Clang 14 & GCC 13Debian 12: Clang 14 & GCC 12

Docker (from Docker Hub) Specific AFL++ version, Apple Silicon support As of 4.35c: Clang 19 & GCC 11

Docker (from source) Test unreleased features, apply patches Configurable in Dockerfile

From source Avoid Docker, need specific patches Adjustable via LLVM_CONFIG env var

Ubuntu/Debian

Prior to installing afl++, check the clang version dependency of the packge with apt-cache show afl++ , and install the matching lld version (e.g., lld-17 ).

apt install afl++ lld-17

Docker (from Docker Hub)

docker pull aflplusplus/aflplusplus:stable

Docker (from source)

git clone --depth 1 --branch stable https://github.com/AFLplusplus/AFLplusplus cd AFLplusplus docker build -t aflplusplus .

From source

Refer to the Dockerfile for Ubuntu version requirements and dependencies. Set LLVM_CONFIG to specify Clang version (e.g., llvm-config-18 ).

Wrapper Script Setup

Create a wrapper script to run AFL++ on host or Docker:

cat <<'EOF' > ./afl++ #!/bin/sh AFL_VERSION="${AFL_VERSION:-"stable"}" case "$1" in host) shift bash -c "$" ;; docker) shift /usr/bin/env docker run -ti
--privileged
-v ./:/src
--rm
--name afl_fuzzing
"aflplusplus/aflplusplus:$AFL_VERSION"
bash -c "cd /src && bash -c "$"" ;; *) echo "Usage: $0 {host|docker}" exit 1 ;; esac EOF chmod +x ./afl++

Security Warning: The afl-system-config and afl-persistent-config scripts require root privileges and disable OS security features. Do not fuzz on production systems or your development environment. Use a dedicated VM instead.

System Configuration

Run after each reboot for up to 15% more executions per second:

./afl++ <host/docker> afl-system-config

For maximum performance, disable kernel security mitigations (requires grub bootloader, not supported in Docker):

./afl++ host afl-persistent-config update-grub reboot ./afl++ <host/docker> afl-system-config

Verify with cat /proc/cmdline

• output should include mitigations=off .

Writing a Harness

Harness Structure

AFL++ supports libFuzzer-style harnesses:

#include <stdint.h> #include <stddef.h>

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) { // 1. Validate input size if needed if (size < MIN_SIZE || size > MAX_SIZE) return 0;

// 2. Call target function with fuzz data
target_function(data, size);

// 3. Return 0 (non-zero reserved for future use)
return 0;

}

Harness Rules

Do Don't

Reset global state between runs Rely on state from previous runs

Handle edge cases gracefully Exit on invalid input

Keep harness deterministic Use random number generators

Free allocated memory Create memory leaks

Validate input sizes Process unbounded input

See Also: For detailed harness writing techniques, patterns for handling complex inputs, and advanced strategies, see the fuzz-harness-writing technique skill.

Compilation

AFL++ offers multiple compilation modes with different trade-offs.

Compilation Mode Decision Tree

Choose your compilation mode:

• LTO mode (afl-clang-lto ): Best performance and instrumentation. Try this first.

• LLVM mode (afl-clang-fast ): Fall back if LTO fails to compile.

• GCC plugin (afl-gcc-fast ): For projects requiring GCC.

Basic Compilation (LLVM mode)

./afl++ <host/docker> afl-clang-fast++ -DNO_MAIN=1 -O2 -fsanitize=fuzzer harness.cc main.cc -o fuzz

GCC Compilation

./afl++ <host/docker> afl-g++-fast -DNO_MAIN=1 -O2 -fsanitize=fuzzer harness.cc main.cc -o fuzz

Important: GCC version must match the version used to compile the AFL++ GCC plugin.

With Sanitizers

./afl++ <host/docker> AFL_USE_ASAN=1 afl-clang-fast++ -DNO_MAIN=1 -O2 -fsanitize=fuzzer harness.cc main.cc -o fuzz

See Also: For detailed sanitizer configuration, common issues, and advanced flags, see the address-sanitizer and undefined-behavior-sanitizer technique skills.

Build Flags

Note that -g is not necessary, it is added by default by the AFL++ compilers.

Flag Purpose

-DNO_MAIN=1

Skip main function when using libFuzzer harness

-O2

Production optimization level (recommended for fuzzing)

-fsanitize=fuzzer

Enable libFuzzer compatibility mode and adds the fuzzer runtime when linking executable

-fsanitize=fuzzer-no-link

Instrument without linking fuzzer runtime (for static libraries and object files)

Corpus Management

Creating Initial Corpus

AFL++ requires at least one non-empty seed file:

mkdir seeds echo "aaaa" > seeds/minimal_seed

For real projects, gather representative inputs:

• Download example files for the format you're fuzzing

• Extract test cases from the project's test suite

• Use minimal valid inputs for your file format

Corpus Minimization

After a campaign, minimize the corpus to keep only unique coverage:

./afl++ <host/docker> afl-cmin -i out/default/queue -o minimized_corpus -- ./fuzz

See Also: For corpus creation strategies, dictionaries, and seed selection, see the fuzzing-corpus technique skill.

Running Campaigns

Basic Run

./afl++ <host/docker> afl-fuzz -i seeds -o out -- ./fuzz

Setting Environment Variables

./afl++ <host/docker> AFL_FAST_CAL=1 afl-fuzz -i seeds -o out -- ./fuzz

Interpreting Output

The AFL++ UI shows real-time fuzzing statistics:

Output Meaning

execs/sec Execution speed - higher is better

cycles done Number of queue passes completed

corpus count Number of unique test cases in queue

saved crashes Number of unique crashes found

stability % of stable edges (should be near 100%)

Output Directory Structure

out/default/ ├── cmdline # How was the SUT invoked? ├── crashes/ # Inputs that crash the SUT │ └── id:000000,sig:06,src:000002,time:286,execs:13105,op:havoc,rep:4 ├── hangs/ # Inputs that hang the SUT ├── queue/ # Test cases reproducing final fuzzer state │ ├── id:000000,time:0,execs:0,orig:minimal_seed │ └── id:000001,src:000000,time:0,execs:8,op:havoc,rep:6,+cov ├── fuzzer_stats # Campaign statistics └── plot_data # Data for plotting

Analyzing Results

View live campaign statistics:

./afl++ <host/docker> afl-whatsup out

Create coverage plots:

apt install gnuplot ./afl++ <host/docker> afl-plot out/default out_graph/

Re-executing Test Cases

./afl++ <host/docker> ./fuzz out/default/crashes/<test_case>

Fuzzer Options

Option Purpose

-G 4000

Maximum test input length (default: 1048576 bytes)

-t 1000

Timeout in milliseconds for each test case (default: 1000ms)

-m 1000

Memory limit in megabytes (default: 0 = unlimited)

-x ./dict.dict

Use dictionary file to guide mutations

Multi-Core Fuzzing

AFL++ excels at multi-core fuzzing with two major advantages:

• More executions per second (scales linearly with physical cores)

• Asymmetrical fuzzing (e.g., one ASan job, rest without sanitizers)

Starting a Campaign

Start the primary fuzzer (in background):

./afl++ <host/docker> afl-fuzz -M primary -i seeds -o state -- ./fuzz 1>primary.log 2>primary.error &

Start secondary fuzzers (as many as you have cores):

./afl++ <host/docker> afl-fuzz -S secondary01 -i seeds -o state -- ./fuzz 1>secondary01.log 2>secondary01.error & ./afl++ <host/docker> afl-fuzz -S secondary02 -i seeds -o state -- ./fuzz 1>secondary02.log 2>secondary02.error &

Monitoring Multi-Core Campaigns

List all running jobs:

jobs

View live statistics (updates every second):

./afl++ <host/docker> watch -n1 --color afl-whatsup state/

Stopping All Fuzzers

kill $(jobs -p)

Coverage Analysis

AFL++ automatically tracks coverage through edge instrumentation. Coverage information is stored in fuzzer_stats and plot_data .

Measuring Coverage

Use afl-plot to visualize coverage over time:

./afl++ <host/docker> afl-plot out/default out_graph/

Improving Coverage

• Use dictionaries for format-aware fuzzing

• Run longer campaigns (cycles_wo_finds indicates plateau)

• Try different mutation strategies with multi-core fuzzing

• Analyze coverage gaps and add targeted seed inputs

See Also: For detailed coverage analysis techniques, identifying coverage gaps, and systematic coverage improvement, see the coverage-analysis technique skill.

CMPLOG

CMPLOG/RedQueen is the best path constraint solving mechanism available in any fuzzer. To enable it, the fuzz target needs to be instrumented for it. Before building the fuzzing target set the environment variable:

./afl++ <host/docker> AFL_LLVM_CMPLOG=1 make

No special action is needed for compiling and linking the harness.

To run a fuzzer instance with a CMPLOG instrumented fuzzing target, add -c0 to the command like arguments:

./afl++ <host/docker> afl-fuzz -c0 -S cmplog -i seeds -o state -- ./fuzz 1>secondary02.log 2>secondary02.error &

Sanitizer Integration

Sanitizers are essential for finding memory corruption bugs that don't cause immediate crashes.

AddressSanitizer (ASan)

./afl++ <host/docker> AFL_USE_ASAN=1 afl-clang-fast++ -DNO_MAIN=1 -O2 -fsanitize=fuzzer harness.cc main.cc -o fuzz

Note: Memory limit (-m ) is not supported with ASan due to 20TB virtual memory reservation.

UndefinedBehaviorSanitizer (UBSan)

./afl++ <host/docker> AFL_USE_UBSAN=1 afl-clang-fast++ -DNO_MAIN=1 -O2 -fsanitize=fuzzer,undefined harness.cc main.cc -o fuzz

Common Sanitizer Issues

Issue Solution

ASan slows fuzzing Use only 1 ASan job in multi-core setup

Stack exhaustion Increase stack with ASAN_OPTIONS=stack_size=...

GCC version mismatch Ensure system GCC matches AFL++ plugin version

See Also: For comprehensive sanitizer configuration and troubleshooting, see the address-sanitizer technique skill.

Advanced Usage

Tips and Tricks

Tip Why It Helps

Use LLVMFuzzerTestOneInput harnesses where possible If a fuzzing campaign has at least 85% stability then this is the most efficient fuzzing style. If not then try standard input or file input fuzzing

Use dictionaries Helps fuzzer discover format-specific keywords and magic bytes

Set realistic timeouts Prevents false positives from system load

Limit input size Larger inputs don't necessarily explore more space

Monitor stability Low stability indicates non-deterministic behavior

Standard Input Fuzzing

AFL++ can fuzz programs reading from stdin without a libFuzzer harness:

./afl++ <host/docker> afl-clang-fast++ -O2 main_stdin.c -o fuzz_stdin ./afl++ <host/docker> afl-fuzz -i seeds -o out -- ./fuzz_stdin

This is slower than persistent mode but requires no harness code.

File Input Fuzzing

For programs that read files, use @@ placeholder:

./afl++ <host/docker> afl-clang-fast++ -O2 main_file.c -o fuzz_file ./afl++ <host/docker> afl-fuzz -i seeds -o out -- ./fuzz_file @@

For better performance, use fmemopen to create file descriptors from memory.

Argument Fuzzing

Fuzz command-line arguments using argv-fuzz-inl.h :

#include <stdio.h> #include <stdlib.h> #include <string.h>

#ifdef __AFL_COMPILER #include "argv-fuzz-inl.h" #endif

void check_buf(char *buf, size_t buf_len) { if(buf_len > 0 && buf[0] == 'a') { if(buf_len > 1 && buf[1] == 'b') { if(buf_len > 2 && buf[2] == 'c') { abort(); } } } }

int main(int argc, char *argv[]) { #ifdef __AFL_COMPILER AFL_INIT_ARGV(); #endif

if (argc < 2) {
    fprintf(stderr, "Usage: %s <input_string>\n", argv[0]);
    return 1;
}

char *input_buf = argv[1];
size_t len = strlen(input_buf);
check_buf(input_buf, len);
return 0;

}

Download the header:

curl -O https://raw.githubusercontent.com/AFLplusplus/AFLplusplus/stable/utils/argv_fuzzing/argv-fuzz-inl.h

Compile and run:

./afl++ <host/docker> afl-clang-fast++ -O2 main_arg.c -o fuzz_arg ./afl++ <host/docker> afl-fuzz -i seeds -o out -- ./fuzz_arg

Performance Tuning

Setting Impact

CPU core count Linear scaling with physical cores

Persistent mode 10-20x faster than fork server

-G input size limit Smaller = faster, but may miss bugs

ASan ratio 1 ASan job per 4-8 non-ASan jobs

Troubleshooting

Problem Cause Solution

Low exec/sec (<1k) Not using persistent mode Create a LLVMFuzzerTestOneInput style harness

Low stability (<85%) Non-deterministic code Fuzz a program via stdin or file inputs, or create such a harness

GCC plugin error GCC version mismatch Ensure system GCC matches AFL++ build and install gcc-$GCC_VERSION-plugin-dev

No crashes found Need sanitizers Recompile with AFL_USE_ASAN=1

Memory limit exceeded ASan uses 20TB virtual Remove -m flag when using ASan

Docker performance loss Virtualization overhead Use bare metal or VM for production fuzzing

Related Skills

Technique Skills

Skill Use Case

fuzz-harness-writing Detailed guidance on writing effective harnesses

address-sanitizer Memory error detection during fuzzing

undefined-behavior-sanitizer Detect undefined behavior bugs

fuzzing-corpus Building and managing seed corpora

fuzzing-dictionaries Creating dictionaries for format-aware fuzzing

Related Fuzzers

Skill When to Consider

libfuzzer Quick prototyping, single-threaded fuzzing is sufficient

libafl Need custom mutators or research-grade features

Resources

Key External Resources

AFL++ GitHub Repository Official repository with comprehensive documentation, examples, and issue tracker.

Fuzzing in Depth Advanced documentation by the AFL++ team covering instrumentation modes, optimization techniques, and advanced use cases.

AFL++ Under The Hood Technical deep-dive into AFL++ internals, mutation strategies, and coverage tracking mechanisms.

AFL++: Combining Incremental Steps of Fuzzing Research Research paper describing AFL++ architecture and performance improvements over original AFL.

Video Resources

• Fuzzing cURL - Trail of Bits blog post on using AFL++ argument fuzzing for cURL

• Sudo Vulnerability Walkthrough - LiveOverflow series on rediscovering CVE-2021-3156

• Rediscovery of libpng bug - LiveOverflow video on finding CVE-2023-4863