Harness Writing

Writing Fuzzing Harnesses

A fuzzing harness is the entrypoint function that receives random data from the fuzzer and routes it to your system under test (SUT). The quality of your harness directly determines which code paths get exercised and whether critical bugs are found. A poorly written harness can miss entire subsystems or produce non-reproducible crashes.

Overview

The harness is the bridge between the fuzzer's random byte generation and your application's API. It must parse raw bytes into meaningful inputs, call target functions, and handle edge cases gracefully. The most important part of any fuzzing setup is the harness—if written poorly, critical parts of your application may not be covered.

Key Concepts

When to Apply

Apply this technique when:

• Creating a new fuzz target for the first time
• Fuzz campaign has low code coverage or isn't finding bugs
• Crashes found during fuzzing are not reproducible
• Target API requires complex or structured inputs
• Multiple related functions should be tested together

Skip this technique when:

• Using existing well-tested harnesses from your project
• Tool provides automatic harness generation that meets your needs
• Target already has comprehensive fuzzing infrastructure

Quick Reference

Step-by-Step

Step 1: Identify Entry Points

Find functions in your codebase that:

• Accept external input (parsers, validators, protocol handlers)
• Parse complex data formats (JSON, XML, binary protocols)
• Perform security-critical operations (authentication, cryptography)
• Have high cyclomatic complexity or many branches

Good targets are typically:

• Protocol parsers
• File format parsers
• Serialization/deserialization functions
• Input validation routines

Step 2: Write Minimal Harness

Start with the simplest possible harness that calls your target function:

C/C++:

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {    target_function(data, size);    return 0;}

Rust:

#![no_main]use libfuzzer_sys::fuzz_target;
fuzz_target!(|data: &[u8]| {    target_function(data);});

Step 3: Add Input Validation

Reject inputs that are too small or too large to be meaningful:

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {    // Ensure minimum size for meaningful input    if (size < MIN_INPUT_SIZE || size > MAX_INPUT_SIZE) {        return 0;    }    target_function(data, size);    return 0;}

Rationale: The fuzzer generates random inputs of all sizes. Your harness must handle empty, tiny, huge, or malformed inputs without causing unexpected issues in the harness itself (crashes in the SUT are fine—that's what we're looking for).

Step 4: Structure the Input

For APIs that require typed data (integers, strings, etc.), use casting or helpers like FuzzedDataProvider:

Simple casting:

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {    if (size != 2 * sizeof(uint32_t)) {        return 0;    }
    uint32_t numerator = *(uint32_t*)(data);    uint32_t denominator = *(uint32_t*)(data + sizeof(uint32_t));
    divide(numerator, denominator);    return 0;}

Using FuzzedDataProvider:

#include "FuzzedDataProvider.h"
extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {    FuzzedDataProvider fuzzed_data(data, size);
    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);
    concat(&str1[0], str1.size(), &str2[0], str2.size(), allocation_size);    return 0;}

Step 5: Test and Iterate

Run the fuzzer and monitor:

• Code coverage (are all interesting paths reached?)
• Executions per second (is it fast enough?)
• Crash reproducibility (can you reproduce crashes with saved inputs?)

Iterate on the harness to improve these metrics.

Common Patterns

Pattern: Beyond Byte Arrays—Casting to Integers

Use Case: When target expects primitive types like integers or floats

Implementation:

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {    // Ensure exactly 2 4-byte numbers    if (size != 2 * sizeof(uint32_t)) {        return 0;    }
    // Split input into two integers    uint32_t numerator = *(uint32_t*)(data);    uint32_t denominator = *(uint32_t*)(data + sizeof(uint32_t));
    divide(numerator, denominator);    return 0;}

Rust equivalent:

fuzz_target!(|data: &[u8]| {    if data.len() != 2 * std::mem::size_of::<i32>() {        return;    }
    let numerator = i32::from_ne_bytes([data[0], data[1], data[2], data[3]]);    let denominator = i32::from_ne_bytes([data[4], data[5], data[6], data[7]]);
    divide(numerator, denominator);});

Why it works: Any 8-byte input is valid. The fuzzer learns that inputs must be exactly 8 bytes, and every bit flip produces a new, potentially interesting input.

Pattern: FuzzedDataProvider for Complex Inputs

Use Case: When target requires multiple strings, integers, or variable-length data

Implementation:

#include "FuzzedDataProvider.h"
extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {    FuzzedDataProvider fuzzed_data(data, size);
    // Extract different types of data    size_t allocation_size = fuzzed_data.ConsumeIntegral<size_t>();
    // Consume variable-length strings with terminator    std::vector<char> str1 = fuzzed_data.ConsumeBytesWithTerminator<char>(32, 0xFF);    std::vector<char> str2 = fuzzed_data.ConsumeBytesWithTerminator<char>(32, 0xFF);
    char* result = concat(&str1[0], str1.size(), &str2[0], str2.size(), allocation_size);    if (result != NULL) {        free(result);    }
    return 0;}

Why it helps: FuzzedDataProvider handles the complexity of extracting structured data from a byte stream. It's particularly useful for APIs that need multiple parameters of different types.

Pattern: Interleaved Fuzzing

Use Case: When multiple related operations should be tested in a single harness

Implementation:

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {    if (size < 1 + 2 * sizeof(int32_t)) {        return 0;    }
    // First byte selects operation    uint8_t mode = data[0];
    // Next bytes are operands    int32_t numbers[2];    memcpy(numbers, data + 1, 2 * sizeof(int32_t));
    int32_t result = 0;    switch (mode % 4) {        case 0:            result = add(numbers[0], numbers[1]);            break;        case 1:            result = subtract(numbers[0], numbers[1]);            break;        case 2:            result = multiply(numbers[0], numbers[1]);            break;        case 3:            result = divide(numbers[0], numbers[1]);            break;    }
    // Prevent compiler from optimizing away the calls    printf("%d", result);    return 0;}

Advantages:

• Faster to write one harness than multiple individual harnesses
• Single shared corpus means interesting inputs for one operation may be interesting for others
• Can discover bugs in interactions between operations

When to use:

• Operations share similar input types
• Operations are logically related (e.g., arithmetic operations, CRUD operations)
• Single corpus makes sense across all operations

Pattern: Structure-Aware Fuzzing with Arbitrary (Rust)

Use Case: When fuzzing Rust code that uses custom structs

Implementation:

use arbitrary::Arbitrary;
#[derive(Debug, Arbitrary)]pub struct Name {    data: String}
impl Name {    pub fn check_buf(&self) {        let data = self.data.as_bytes();        if data.len() > 0 && data[0] == b'a' {            if data.len() > 1 && data[1] == b'b' {                if data.len() > 2 && data[2] == b'c' {                    process::abort();                }            }        }    }}

Harness with arbitrary:

#![no_main]use libfuzzer_sys::fuzz_target;
fuzz_target!(|data: your_project::Name| {    data.check_buf();});

Add to Cargo.toml:

[dependencies]arbitrary = { version = "1", features = ["derive"] }

Why it helps: The arbitrary crate automatically handles deserialization of raw bytes into your Rust structs, reducing boilerplate and ensuring valid struct construction.

Limitation: The arbitrary crate doesn't offer reverse serialization, so you can't manually construct byte arrays that map to specific structs. This works best when starting from an empty corpus (fine for libFuzzer, problematic for AFL++).

Advanced Usage

Tips and Tricks

Structure-Aware Fuzzing with Protocol Buffers

For highly structured input formats, consider using Protocol Buffers as an intermediate format with custom mutators:

// Define your input format in .proto file// Use libprotobuf-mutator to generate valid mutations// This ensures fuzzer mutates message contents, not the protobuf encoding itself

This approach is more setup but prevents the fuzzer from wasting time on unparseable inputs. See structure-aware fuzzing documentation for details.

Handling Non-Determinism

Problem: Random values or timing dependencies cause non-reproducible crashes.

Solutions:

• Replace rand() with deterministic PRNG seeded from fuzzer input:
  cpp

  uint32_t seed = fuzzed_data.ConsumeIntegral<uint32_t>();srand(seed);

• Mock system calls that return time, PIDs, or random data
• Avoid reading from /dev/random or /dev/urandom

Resetting Global State

If your SUT uses global state (singletons, static variables), reset it between iterations:

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {    // Reset global state before each iteration    global_reset();
    target_function(data, size);
    // Clean up resources    global_cleanup();    return 0;}

Rationale: Global state can cause crashes after N iterations rather than on a specific input, making bugs non-reproducible.

Practical Harness Rules

Follow these rules to ensure effective fuzzing harnesses:

Note: These guidelines apply not just to harness code, but to the entire SUT. If the SUT violates these rules, consider patching it (see the fuzzing obstacles technique).

Anti-Patterns

Tool-Specific Guidance

libFuzzer

Harness signature:

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {    // Your code here    return 0;  // Non-zero return is reserved for future use}

Compilation:

clang++ -fsanitize=fuzzer,address -g harness.cc -o fuzz_target

Integration tips:

• Use FuzzedDataProvider.h for structured input extraction
• Compile with -fsanitize=fuzzer to link the fuzzing runtime
• Add sanitizers (-fsanitize=address,undefined) to detect more bugs
• Use -g for better stack traces when crashes occur
• libFuzzer can start with empty corpus—no seed inputs required

Running:

./fuzz_target corpus_dir/

Resources:

• FuzzedDataProvider header
• libFuzzer documentation

AFL++

AFL++ supports multiple harness styles. For best performance, use persistent mode:

Persistent mode harness:

#include <unistd.h>
int main(int argc, char **argv) {    #ifdef __AFL_HAVE_MANUAL_CONTROL        __AFL_INIT();    #endif
    unsigned char buf[MAX_SIZE];
    while (__AFL_LOOP(10000)) {        // Read input from stdin        ssize_t len = read(0, buf, sizeof(buf));        if (len <= 0) break;
        // Call target function        target_function(buf, len);    }
    return 0;}

Compilation:

afl-clang-fast++ -g harness.cc -o fuzz_target

Integration tips:

• Use persistent mode (__AFL_LOOP) for 10-100x speedup
• Consider deferred initialization (__AFL_INIT()) to skip setup overhead
• AFL++ requires at least one seed input in the corpus directory
• Use AFL_USE_ASAN=1 or AFL_USE_UBSAN=1 for sanitizer builds

Running:

afl-fuzz -i seeds/ -o findings/ -- ./fuzz_target

cargo-fuzz (Rust)

Harness signature:

#![no_main]use libfuzzer_sys::fuzz_target;
fuzz_target!(|data: &[u8]| {    // Your code here});

With structured input (arbitrary crate):

#![no_main]use libfuzzer_sys::fuzz_target;
fuzz_target!(|data: YourStruct| {    data.check();});

Creating harness:

cargo fuzz initcargo fuzz add my_target

Integration tips:

• Use arbitrary crate for automatic struct deserialization
• cargo-fuzz wraps libFuzzer, so all libFuzzer features work
• Compile with sanitizers automatically via cargo-fuzz
• Harnesses go in fuzz/fuzz_targets/ directory

Running:

cargo +nightly fuzz run my_target

Resources:

• cargo-fuzz documentation
• arbitrary crate

go-fuzz

Harness signature:

// +build gofuzz
package mypackage
func Fuzz(data []byte) int {    // Call target function    target(data)
    // Return codes:    // -1 if input is invalid    //  0 if input is valid but not interesting    //  1 if input is interesting (e.g., added new coverage)    return 0}

Building:

go-fuzz-build

Integration tips:

• Return 1 for inputs that add coverage (optional—fuzzer can detect automatically)
• Return -1 for invalid inputs to deprioritize similar mutations
• go-fuzz handles persistence automatically

Running:

go-fuzz -bin=./mypackage-fuzz.zip -workdir=fuzz

Troubleshooting

Related Skills

Tools That Use This Technique

Related Techniques

Resources

Key External Resources

Split Inputs in libFuzzer - Google Fuzzing Docs Explains techniques for handling multiple input parameters in a single fuzzing harness, including use of magic separators and FuzzedDataProvider.

Structure-Aware Fuzzing with Protocol Buffers Advanced technique using protobuf as intermediate format with custom mutators to ensure fuzzer mutates message contents rather than format encoding.

libFuzzer Documentation Official LLVM documentation covering harness requirements, best practices, and advanced features.

cargo-fuzz Book Comprehensive guide to writing Rust fuzzing harnesses with cargo-fuzz and the arbitrary crate.

Video Resources

• Effective File Format Fuzzing - Conference talk on writing harnesses for file format parsers
• Modern Fuzzing of C/C++ Projects - Tutorial covering harness design patterns