Imported from majiayu000/claude-skill-registry (skills/development/aflpp/SKILL.md). Install upstream with npx skills add majiayu000/claude-skill-registry --skill aflpp. Copyright stays with the author.

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

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++.

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

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.

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 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

Multi-Core Fuzzing

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

1. More executions per second (scales linearly with physical cores)
2. 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

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

Advanced Usage

Tips and Tricks

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

Real-World Examples

Example: libpng

Fuzzing libpng demonstrates fuzzing a C project with static libraries:

# Get source
curl -L -O https://downloads.sourceforge.net/project/libpng/libpng16/1.6.37/libpng-1.6.37.tar.xz
tar xf libpng-1.6.37.tar.xz
cd libpng-1.6.37/

# Install dependencies
apt install zlib1g-dev

# Configure and build static library
export CC=afl-clang-fast CFLAGS=-fsanitize=fuzzer-no-link
export CXX=afl-clang-fast++ CXXFLAGS="$CFLAGS"
./configure --enable-shared=no
export AFL_LLVM_CMPLOG=1
export AFL_USE_ASAN=1
make

# Download harness
curl -O https://raw.githubusercontent.com/glennrp/libpng/f8e5fa92b0e37ab597616f554bee254157998227/contrib/oss-fuzz/libpng_read_fuzzer.cc

# Link fuzzer
export AFL_USE_ASAN=1
$CXX -fsanitize=fuzzer libpng_read_fuzzer.cc .libs/libpng16.a -lz -o fuzz

# Prepare seeds and dictionary
mkdir seeds/
curl -o seeds/input.png https://raw.githubusercontent.com/glennrp/libpng/acfd50ae0ba3198ad734e5d4dec2b05341e50924/contrib/pngsuite/iftp1n3p08.png
curl -O https://raw.githubusercontent.com/glennrp/libpng/2fff013a6935967960a5ae626fc21432807933dd/contrib/oss-fuzz/png.dict

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

Example: CMake-based Project

project(BuggyProgram)
cmake_minimum_required(VERSION 3.0)

add_executable(buggy_program main.cc)

add_executable(fuzz main.cc harness.cc)
target_compile_definitions(fuzz PRIVATE NO_MAIN=1)
target_compile_options(fuzz PRIVATE -O2 -fsanitize=fuzzer-no-link)
target_link_libraries(fuzz -fsanitize=fuzzer)

Build and fuzz:

# Build non-instrumented binary
./afl++ <host/docker> cmake -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ .
./afl++ <host/docker> cmake --build . --target buggy_program

# Build fuzzer
./afl++ <host/docker> cmake -DCMAKE_C_COMPILER=afl-clang-fast -DCMAKE_CXX_COMPILER=afl-clang-fast++ .
./afl++ <host/docker> cmake --build . --target fuzz

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

Troubleshooting

Related Skills

Technique Skills

Related Fuzzers

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