Libfuzzer

libFuzzer

libFuzzer is an in-process, coverage-guided fuzzer that is part of the LLVM project. It's the recommended starting point for fuzzing C/C++ projects due to its simplicity and integration with the LLVM toolchain. While libFuzzer has been in maintenance-only mode since late 2022, it is easier to install and use than its alternatives, has wide support, and will be maintained for the foreseeable future.

When to Use

Choose libFuzzer when:

• You need a simple, quick setup for C/C++ code
• Project uses Clang for compilation
• Single-core fuzzing is sufficient initially
• Transitioning to AFL++ later is an option (harnesses are compatible)

Note: Fuzzing harnesses written for libFuzzer are compatible with AFL++, making it easy to transition if you need more advanced features like better multi-core support.

Quick Start

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

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

    // Call your target function with fuzzer-provided data
    my_target_function(data, size);

    return 0;
}

Compile and run:

clang++ -fsanitize=fuzzer,address -g -O2 harness.cc target.cc -o fuzz
mkdir corpus/
./fuzz corpus/

Installation

Prerequisites

• LLVM/Clang compiler (includes libFuzzer)
• LLVM tools for coverage analysis (optional)

Linux (Ubuntu/Debian)

apt install clang llvm

For the latest LLVM version:

# Add LLVM repository from apt.llvm.org
# Then install specific version, e.g.:
apt install clang-18 llvm-18

macOS

# Using Homebrew
brew install llvm

# Or using Nix
nix-env -i clang

Windows

Install Clang through Visual Studio. Refer to Microsoft's documentation for setup instructions.

Recommendation: If possible, fuzz on a local x86_64 VM or rent one on DigitalOcean, AWS, or Hetzner. Linux provides the best support for libFuzzer.

Verification

clang++ --version
# Should show LLVM version information

Writing a Harness

Harness Structure

The harness is the entry point for the fuzzer. libFuzzer calls the LLVMFuzzerTestOneInput function repeatedly with different inputs.

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

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
    // 1. Optional: Validate input size
    if (size < MIN_REQUIRED_SIZE) {
        return 0;  // Reject inputs that are too small
    }

    // 2. Optional: Convert raw bytes to structured data
    // Example: Parse two integers from byte array
    if (size >= 2 * sizeof(uint32_t)) {
        uint32_t a = *(uint32_t*)(data);
        uint32_t b = *(uint32_t*)(data + sizeof(uint32_t));
        my_function(a, b);
    }

    // 3. Call target function
    target_function(data, size);

    // 4. Always return 0 (non-zero reserved for future use)
    return 0;
}

Harness Rules

Rationale:

• Speed matters: Aim for 100s-1000s executions per second per core
• Reproducibility: Crashes must be reproducible after fuzzing completes
• Isolation: Each execution should be independent

Using FuzzedDataProvider for Complex Inputs

For complex inputs (strings, multiple parameters), use the FuzzedDataProvider helper:

#include <stdint.h>
#include <stddef.h>
#include "FuzzedDataProvider.h"  // From LLVM project

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
    FuzzedDataProvider fuzzed_data(data, size);

    // Extract structured data
    size_t allocation_size = fuzzed_data.ConsumeIntegral<size_t>();
    std::vector<char> str1 = fuzzed_data.ConsumeBytesWithTerminator<char>(32, 0xFF);
    std::vector<char> str2 = fuzzed_data.ConsumeBytesWithTerminator<char>(32, 0xFF);

    // Call target with extracted data
    char* result = concat(&str1[0], str1.size(), &str2[0], str2.size(), allocation_size);
    if (result != NULL) {
        free(result);
    }

    return 0;
}

Download FuzzedDataProvider.h from the LLVM repository.

Interleaved Fuzzing

Use a single harness to test multiple related functions:

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
    if (size < 1 + 2 * sizeof(int32_t)) {
        return 0;
    }

    uint8_t mode = data[0];
    int32_t numbers[2];
    memcpy(numbers, data + 1, 2 * sizeof(int32_t));

    // Select function based on first byte
    switch (mode % 4) {
        case 0: add(numbers[0], numbers[1]); break;
        case 1: subtract(numbers[0], numbers[1]); break;
        case 2: multiply(numbers[0], numbers[1]); break;
        case 3: divide(numbers[0], numbers[1]); break;
    }

    return 0;
}

See Also: For detailed harness writing techniques, patterns for handling complex inputs, structure-aware fuzzing, and protobuf-based fuzzing, see the fuzz-harness-writing technique skill.

Compilation

Basic Compilation

The key flag is -fsanitize=fuzzer, which:

• Links the libFuzzer runtime (provides main function)
• Enables SanitizerCoverage instrumentation for coverage tracking
• Disables built-in functions like memcmp

clang++ -fsanitize=fuzzer -g -O2 harness.cc target.cc -o fuzz

Flags explained:

• -fsanitize=fuzzer: Enable libFuzzer
• -g: Add debug symbols (helpful for crash analysis)
• -O2: Production-level optimizations (recommended for fuzzing)
• -DNO_MAIN: Define macro if your code has a main function

With Sanitizers

AddressSanitizer (recommended):

clang++ -fsanitize=fuzzer,address -g -O2 -U_FORTIFY_SOURCE harness.cc target.cc -o fuzz

Multiple sanitizers:

clang++ -fsanitize=fuzzer,address,undefined -g -O2 harness.cc target.cc -o fuzz

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

Build Flags

Building Static Libraries

For projects that produce static libraries:

1. Build the library with fuzzing instrumentation:

export CC=clang CFLAGS="-fsanitize=fuzzer-no-link -fsanitize=address"
export CXX=clang++ CXXFLAGS="$CFLAGS"
./configure --enable-shared=no
make

2. Link the static library with your harness:

clang++ -fsanitize=fuzzer -fsanitize=address harness.cc libmylib.a -o fuzz

CMake Integration

project(FuzzTarget)
cmake_minimum_required(VERSION 3.0)

add_executable(fuzz main.cc harness.cc)
target_compile_definitions(fuzz PRIVATE NO_MAIN=1)
target_compile_options(fuzz PRIVATE -g -O2 -fsanitize=fuzzer -fsanitize=address)
target_link_libraries(fuzz -fsanitize=fuzzer -fsanitize=address)

Build with:

cmake -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ .
cmake --build .

Corpus Management

Creating Initial Corpus

Create a directory for the corpus (can start empty):

mkdir corpus/

Optional but recommended: Provide seed inputs (valid example files):

# For a PNG parser:
cp examples/*.png corpus/

# For a protocol parser:
cp test_packets/*.bin corpus/

Benefits of seed inputs:

• Fuzzer doesn't start from scratch
• Reaches valid code paths faster
• Significantly improves effectiveness

Corpus Structure

The corpus directory contains:

• Input files that trigger unique code paths
• Minimized versions (libFuzzer automatically minimizes)
• Named by content hash (e.g., a9993e364706816aba3e25717850c26c9cd0d89d)

Corpus Minimization

libFuzzer automatically minimizes corpus entries during fuzzing. To explicitly minimize:

mkdir minimized_corpus/
./fuzz -merge=1 minimized_corpus/ corpus/

This creates a deduplicated, minimized corpus in minimized_corpus/.

See Also: For corpus creation strategies, seed selection, format-specific corpus building, and corpus maintenance, see the fuzzing-corpus technique skill.

Running Campaigns

Basic Run

./fuzz corpus/

This runs until a crash is found or you stop it (Ctrl+C).

Recommended: Continue After Crashes

./fuzz -fork=1 -ignore_crashes=1 corpus/

The -fork and -ignore_crashes flags (experimental but widely used) allow fuzzing to continue after finding crashes.

Common Options

Control input size:

./fuzz -max_len=4000 corpus/

Rule of thumb: 2x the size of minimal realistic input.

Set timeout:

./fuzz -timeout=2 corpus/

Abort test cases that run longer than 2 seconds.

Use a dictionary:

./fuzz -dict=./format.dict corpus/

Close stdout/stderr (speed up fuzzing):

./fuzz -close_fd_mask=3 corpus/

See all options:

./fuzz -help=1

Multi-Core Fuzzing

Option 1: Jobs and workers (recommended):

./fuzz -jobs=4 -workers=4 -fork=1 -ignore_crashes=1 corpus/

• -jobs=4: Run 4 sequential campaigns
• -workers=4: Process jobs in parallel with 4 processes
• Test cases are shared between jobs

Option 2: Fork mode:

./fuzz -fork=4 -ignore_crashes=1 corpus/

Note: For serious multi-core fuzzing, consider switching to AFL++, Honggfuzz, or LibAFL.

Re-executing Test Cases

Re-run a single crash:

./fuzz ./crash-a9993e364706816aba3e25717850c26c9cd0d89d

Test all inputs in a directory without fuzzing:

./fuzz -runs=0 corpus/

Interpreting Output

When fuzzing runs, you'll see statistics like:

INFO: Seed: 3517090860
INFO: Loaded 1 modules (9 inline 8-bit counters)
#2      INITED cov: 3 ft: 4 corp: 1/1b exec/s: 0 rss: 26Mb
#57     NEW    cov: 4 ft: 5 corp: 2/4b lim: 4 exec/s: 0 rss: 26Mb

On crash:

==11672== ERROR: libFuzzer: deadly signal
artifact_prefix='./'; Test unit written to ./crash-a9993e364706816aba3e25717850c26c9cd0d89d
0x61,0x62,0x63,
abc
Base64: YWJj

The crash is saved to ./crash-<hash> with the input shown in hex, UTF-8, and Base64.

Reproducibility: Use -seed=<value> to reproduce a fuzzing campaign (single-core only).

Fuzzing Dictionary

Dictionaries help the fuzzer discover interesting inputs faster by providing hints about the input format.

Dictionary Format

Create a text file with quoted strings (one per line):

# Lines starting with '#' are comments

# Magic bytes
magic="\x89PNG"
magic2="IEND"

# Keywords
"GET"
"POST"
"Content-Type"

# Hex sequences
delimiter="\xFF\xD8\xFF"

Using a Dictionary

./fuzz -dict=./format.dict corpus/

Generating a Dictionary

From header files:

grep -o '".*"' header.h > header.dict

From man pages:

man curl | grep -oP '^\s*(--|-)\K\S+' | sed 's/[,.]$//' | sed 's/^/"&/; s/$/&"/' | sort -u > man.dict

From binary strings:

strings ./binary | sed 's/^/"&/; s/$/&"/' > strings.dict

Using LLMs: Ask ChatGPT or similar to generate a dictionary for your format (e.g., "Generate a libFuzzer dictionary for a JSON parser").

See Also: For advanced dictionary generation, format-specific dictionaries, and dictionary optimization strategies, see the fuzzing-dictionaries technique skill.

Coverage Analysis

While libFuzzer shows basic coverage stats (cov: N), detailed coverage analysis requires additional tools.

Source-Based Coverage

1. Recompile with coverage instrumentation:

clang++ -fsanitize=fuzzer -fprofile-instr-generate -fcoverage-mapping harness.cc target.cc -o fuzz

2. Run fuzzer to collect coverage:

LLVM_PROFILE_FILE="coverage-%p.profraw" ./fuzz -runs=10000 corpus/

3. Merge coverage data:

llvm-profdata merge -sparse coverage-*.profraw -o coverage.profdata

4. Generate coverage report:

llvm-cov show ./fuzz -instr-profile=coverage.profdata

5. Generate HTML report:

llvm-cov show ./fuzz -instr-profile=coverage.profdata -format=html > coverage.html

Improving Coverage

Tips:

• Provide better seed inputs in corpus
• Use dictionaries for format-aware fuzzing
• Check if harness properly exercises target
• Consider structure-aware fuzzing for complex formats
• Run longer campaigns (days/weeks)

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