Libafl

Trail of BitsOfficialPublished by Trail of Bits

Skill
Tool integration
Software development
CC-BY-SA-4.0
Medium risk

From the author

Builds custom fuzzers with LibAFL, the modular Rust fuzzing library. Covers composing observers, feedbacks, mutators, schedulers, and executors into a fuzzer for targets the standard tools do not fit. Use when writing a bespoke fuzzer or mutator, fuzzing a non-standard target or architecture, implementing a fuzzing research idea, or when libFuzzer and AFL++ lack the control you need.

— the description by Trail of Bits

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

LibAFL is a modular, Rust-based fuzzing library designed for developers and researchers who require more control than standard tools like libFuzzer or AFL++ provide. It allows for the composition of custom fuzzing components—such as observers, mutators, schedulers, and executors—enabling the creation of bespoke fuzzers for unique architectures or novel research techniques. LibAFL can function as a drop-in replacement for existing libFuzzer harnesses or as a library for building highly specialized fuzzing engines from the ground up.

What it does

Provides a modular framework to compose custom fuzzing components.
Supports drop-in replacement for libFuzzer-based harnesses.
Enables fine-grained control over mutation strategies and feedback mechanisms.
Facilitates fuzzing of non-standard targets and architectures.
Includes tools for multi-core fuzzing and TUI-based progress monitoring.
Allows for advanced features like custom crash deduplication and dictionary-based fuzzing.

When to use it

When standard fuzzers like libFuzzer or AFL++ lack the necessary control or features.
When developing a custom fuzzer for a non-standard target or architecture.
When implementing novel fuzzing research ideas.
When you need to define custom feedback loops or mutation strategies.

Advantages

High degree of modularity and customization.
Performance-oriented design suitable for long-running campaigns.
Supports complex, multi-core fuzzing configurations.
Strong integration with Rust's safety and performance features.
Flexible enough to handle both simple harnesses and complex, bespoke fuzzing engines.

Usage examples

“How do I use LibAFL as a drop-in replacement for libFuzzer?”: You can compile your existing libFuzzer harness with the LibAFL compatibility layer, linking against libFuzzer.a to run your target with LibAFL's engine.
“How can I build a custom fuzzer using LibAFL as a Rust library?”: You initialize a new Rust project, add the LibAFL crates as dependencies, and implement the fuzzer structure by composing components like Observers, Feedback, and Mutators.
“How do I enable multi-core fuzzing in LibAFL?”: You can use the built-in launcher to specify multiple cores, allowing the fuzzer to run parallel clients for increased throughput.
“How can I deduplicate crashes in my LibAFL fuzzer?”: You can integrate a BacktraceObserver into your executor and update your objective feedback to use NewHashFeedback, ensuring only crashes with unique backtraces are saved.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/trailofbits/libafl/download
Unzip it into the cross-client skills folder of your project:
```
mkdir -p .agents/skills && unzip libafl.zip -d .agents/skills
```

Documentation by Trail of Bits

LibAFL

LibAFL is a modular fuzzing library that implements features from AFL-based fuzzers like AFL++. Unlike traditional fuzzers, LibAFL provides all functionality in a modular and customizable way as a Rust library. It can be used as a drop-in replacement for libFuzzer or as a library to build custom fuzzers from scratch.

When to Use

Fuzzer	Best For	Complexity
libFuzzer	Quick setup, single-threaded	Low
AFL++	Multi-core, general purpose	Medium
LibAFL	Custom fuzzers, advanced features, research	High

Choose LibAFL when:

You need custom mutation strategies or feedback mechanisms
Standard fuzzers don't support your target architecture
You want to implement novel fuzzing techniques
You need fine-grained control over fuzzing components
You're conducting fuzzing research

Quick Start

LibAFL can be used as a drop-in replacement for libFuzzer with minimal setup:

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

Build LibAFL's libFuzzer compatibility layer:

git clone https://github.com/AFLplusplus/LibAFL
cd LibAFL/libafl_libfuzzer_runtime
./build.sh

Compile and run:

clang++ -DNO_MAIN -g -O2 -fsanitize=fuzzer-no-link libFuzzer.a harness.cc main.cc -o fuzz
./fuzz corpus/

Installation

Prerequisites

Clang/LLVM 15-18
Rust (via rustup)
Additional system dependencies

Linux/macOS

Install Clang:

apt install clang

Or install a specific version via apt.llvm.org:

wget https://apt.llvm.org/llvm.sh
chmod +x llvm.sh
sudo ./llvm.sh 15

Configure environment for Rust:

export RUSTFLAGS="-C linker=/usr/bin/clang-15"
export CC="clang-15"
export CXX="clang++-15"

Install Rust:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Install additional dependencies:

apt install libssl-dev pkg-config

For libFuzzer compatibility mode, install nightly Rust:

rustup toolchain install nightly --component llvm-tools

Verification

Build LibAFL to verify installation:

cd LibAFL/libafl_libfuzzer_runtime
./build.sh
# Should produce libFuzzer.a

Writing a Harness

LibAFL harnesses follow the same pattern as libFuzzer when using drop-in replacement mode:

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
    // Your fuzzing target code here
    return 0;
}

When building custom fuzzers with LibAFL as a Rust library, harness logic is integrated directly into the fuzzer. See the "Writing a Custom Fuzzer" section below for the full pattern.

See Also: For detailed harness writing techniques, see the harness-writing technique skill.

Usage Modes

LibAFL supports two primary usage modes:

1. libFuzzer Drop-in Replacement

Use LibAFL as a replacement for libFuzzer with existing harnesses.

Compilation:

clang++ -DNO_MAIN -g -O2 -fsanitize=fuzzer-no-link libFuzzer.a harness.cc main.cc -o fuzz

Running:

./fuzz corpus/

Recommended for long campaigns:

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

2. Custom Fuzzer as Rust Library

Build a fully customized fuzzer using LibAFL components.

Create project:

cargo init --lib my_fuzzer
cd my_fuzzer
cargo add [email protected] [email protected] [email protected] [email protected] \
  --features "[email protected]/libfuzzer,[email protected]/sancov_pcguard_hitcounts"

Configure Cargo.toml:

[lib]
crate-type = ["staticlib"]

Writing a Custom Fuzzer

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

Fuzzer Components

A LibAFL fuzzer consists of modular components:

Observers - Collect execution feedback (coverage, timing)
Feedback - Determine if inputs are interesting
Objective - Define fuzzing goals (crashes, timeouts)
State - Maintain corpus and metadata
Mutators - Generate new inputs
Scheduler - Select which inputs to mutate
Executor - Run the target with inputs

Basic Fuzzer Structure

use libafl::prelude::*;
use libafl_bolts::prelude::*;
use libafl_targets::{libfuzzer_test_one_input, std_edges_map_observer};

#[no_mangle]
pub extern "C" fn libafl_main() {
    let mut run_client = |state: Option<_>, mut restarting_mgr, _core_id| {
        // 1. Setup observers
        let edges_observer = HitcountsMapObserver::new(
            unsafe { std_edges_map_observer("edges") }
        ).track_indices();
        let time_observer = TimeObserver::new("time");

        // 2. Define feedback
        let mut feedback = feedback_or!(
            MaxMapFeedback::new(&edges_observer),
            TimeFeedback::new(&time_observer)
        );

        // 3. Define objective
        let mut objective = feedback_or_fast!(
            CrashFeedback::new(),
            TimeoutFeedback::new()
        );

        // 4. Create or restore state
        let mut state = state.unwrap_or_else(|| {
            StdState::new(
                StdRand::new(),
                InMemoryCorpus::new(),
                OnDiskCorpus::new(&output_dir).unwrap(),
                &mut feedback,
                &mut objective,
            ).unwrap()
        });

        // 5. Setup mutator
        let mutator = StdScheduledMutator::new(havoc_mutations());
        let mut stages = tuple_list!(StdMutationalStage::new(mutator));

        // 6. Setup scheduler
        let scheduler = IndexesLenTimeMinimizerScheduler::new(
            &edges_observer,
            QueueScheduler::new()
        );

        // 7. Create fuzzer
        let mut fuzzer = StdFuzzer::new(scheduler, feedback, objective);

        // 8. Define harness
        let mut harness = |input: &BytesInput| {
            let buf = input.target_bytes().as_slice();
            libfuzzer_test_one_input(buf);
            ExitKind::Ok
        };

        // 9. Setup executor
        let mut executor = InProcessExecutor::with_timeout(
            &mut harness,
            tuple_list!(edges_observer, time_observer),
            &mut fuzzer,
            &mut state,
            &mut restarting_mgr,
            timeout,
        )?;

        // 10. Load initial inputs
        if state.must_load_initial_inputs() {
            state.load_initial_inputs(
                &mut fuzzer,
                &mut executor,
                &mut restarting_mgr,
                &input_dir
            )?;
        }

        // 11. Start fuzzing
        fuzzer.fuzz_loop(&mut stages, &mut executor, &mut state, &mut restarting_mgr)?;
        Ok(())
    };

    // Launch fuzzer
    Launcher::builder()
        .run_client(&mut run_client)
        .cores(&cores)
        .build()
        .launch()
        .unwrap();
}

Compilation

Verbose Mode

Manually specify all instrumentation flags:

clang++-15 -DNO_MAIN -g -O2 \
  -fsanitize-coverage=trace-pc-guard \
  -fsanitize=address \
  -Wl,--whole-archive target/release/libmy_fuzzer.a -Wl,--no-whole-archive \
  main.cc harness.cc -o fuzz

Compiler Wrapper (Recommended)

Create a LibAFL compiler wrapper to handle instrumentation automatically.

Create src/bin/libafl_cc.rs:

use libafl_cc::{ClangWrapper, CompilerWrapper, Configuration, ToolWrapper};

pub fn main() {
    let args: Vec<String> = env::args().collect();
    let mut cc = ClangWrapper::new();
    cc.cpp(is_cpp)
      .parse_args(&args)
      .link_staticlib(&dir, "my_fuzzer")
      .add_args(&Configuration::GenerateCoverageMap.to_flags().unwrap())
      .add_args(&Configuration::AddressSanitizer.to_flags().unwrap())
      .run()
      .unwrap();
}

Compile and use:

cargo build --release
target/release/libafl_cxx -DNO_MAIN -g -O2 main.cc harness.cc -o fuzz

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

Running Campaigns

Basic Run

./fuzz --cores 0 --input corpus/

Multi-Core Fuzzing

./fuzz --cores 0,8-15 --input corpus/

This runs 9 clients: one on core 0, and 8 on cores 8-15.

With Options

./fuzz --cores 0-7 --input corpus/ --output crashes/ --timeout 1000

Text User Interface (TUI)

Enable graphical statistics view:

./fuzz -tui=1 corpus/

Interpreting Output

Output	Meaning
`corpus: N`	Number of interesting test cases found
`objectives: N`	Number of crashes/timeouts found
`executions: N`	Total number of target invocations
`exec/sec: N`	Current execution throughput
`edges: X%`	Code coverage percentage
`clients: N`	Number of parallel fuzzing processes

The fuzzer emits two main event types:

UserStats - Regular heartbeat with current statistics
Testcase - New interesting input discovered

Advanced Usage

Tips and Tricks

Tip	Why It Helps
Use `-fork=1 -ignore_crashes=1`	Continue fuzzing after first crash
Use `InMemoryOnDiskCorpus`	Persist corpus across restarts
Enable TUI with `-tui=1`	Better visualization of progress
Use specific LLVM version	Avoid compatibility issues
Set `RUSTFLAGS` correctly	Prevent linking errors

Crash Deduplication

Avoid storing duplicate crashes from the same bug:

Add backtrace observer:

let backtrace_observer = BacktraceObserver::owned(
    "BacktraceObserver",
    libafl::observers::HarnessType::InProcess
);

Update executor:

let mut executor = InProcessExecutor::with_timeout(
    &mut harness,
    tuple_list!(edges_observer, time_observer, backtrace_observer),
    &mut fuzzer,
    &mut state,
    &mut restarting_mgr,
    timeout,
)?;

Update objective with hash feedback:

let mut objective = feedback_and!(
    feedback_or_fast!(CrashFeedback::new(), TimeoutFeedback::new()),
    NewHashFeedback::new(&backtrace_observer)
);

This ensures only crashes with unique backtraces are saved.

Dictionary Fuzzing

Use dictionaries to guide fuzzing toward specific tokens:

Add tokens from file:

let mut tokens = Tokens::new();
if let Some(tokenfile) = &tokenfile {
    tokens.add_from_file(tokenfile)?;
}
state.add_metadata(tokens);

Update mutator:

let mutator = StdScheduledMutator::new(
    havoc_mutations().merge(tokens_mutations())
);

Hard-coded tokens example (PNG):

state.add_metadata(Tokens::from([
    vec![137, 80, 78, 71, 13, 10, 26, 10], // PNG header
    "IHDR".as_bytes().to_vec(),
    "IDAT".as_bytes().to_vec(),
    "PLTE".as_bytes().to_vec(),
    "IEND".as_bytes().to_vec(),
]));

See Also: For detailed dictionary creation strategies and format-specific dictionaries, see the fuzzing-dictionaries technique skill.

Auto Tokens

Automatically extract magic values and checksums from the program:

Enable in compiler wrapper:

cc.add_pass(LLVMPasses::AutoTokens)

Load auto tokens in fuzzer:

tokens += libafl_targets::autotokens()?;

Verify tokens section:

echo "p (uint8_t *)__token_start" | gdb fuzz

Performance Tuning

Setting	Impact
Multi-core fuzzing	Linear speedup with cores
`InMemoryCorpus`	Faster but non-persistent
`InMemoryOnDiskCorpus`	Balanced speed and persistence
Sanitizers	2-5x slowdown, essential for bugs
Optimization level `-O2`	Balance between speed and coverage

Debugging Fuzzer

Run fuzzer in single-process mode for easier debugging:

// Replace launcher with direct call
run_client(None, SimpleEventManager::new(monitor), 0).unwrap();

// Comment out:
// Launcher::builder()
//     .run_client(&mut run_client)
//     ...
//     .launch()

Then debug with GDB:

gdb --args ./fuzz --cores 0 --input corpus/

Real-World Examples

Example: libpng

Fuzzing libpng using LibAFL:

1. Get source code:

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/
apt install zlib1g-dev

2. Set compiler wrapper:

export FUZZER_CARGO_DIR="/path/to/libafl/project"
export CC=$FUZZER_CARGO_DIR/target/release/libafl_cc
export CXX=$FUZZER_CARGO_DIR/target/release/libafl_cxx

3. Build static library:

./configure --enable-shared=no
make

4. Get harness:

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

5. Link fuzzer:

$CXX libpng_read_fuzzer.cc .libs/libpng16.a -lz -o fuzz

6. Prepare seeds:

mkdir seeds/
curl -o seeds/input.png https://raw.githubusercontent.com/glennrp/libpng/acfd50ae0ba3198ad734e5d4dec2b05341e50924/contrib/pngsuite/iftp1n3p08.png

7. Get dictionary (optional):

curl -O https://raw.githubusercontent.com/glennrp/libpng/2fff013a6935967960a5ae626fc21432807933dd/contrib/oss-fuzz/png.dict

8. Start fuzzing:

./fuzz --input seeds/ --cores 0 -x png.dict

Example: CMake Project

Integrate LibAFL with CMake build system:

CMakeLists.txt:

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 -g -O2)

Build non-instrumented binary:

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

Build fuzzer:

export FUZZER_CARGO_DIR="/path/to/libafl/project"
cmake -DCMAKE_C_COMPILER=$FUZZER_CARGO_DIR/target/release/libafl_cc \
      -DCMAKE_CXX_COMPILER=$FUZZER_CARGO_DIR/target/release/libafl_cxx .
cmake --build . --target fuzz

Run fuzzing:

./fuzz --input seeds/ --cores 0

Troubleshooting

Problem	Cause	Solution
No coverage increases	Instrumentation failed	Verify compiler wrapper used, check for `-fsanitize-coverage`
Fuzzer won't start	Empty corpus with no interesting inputs	Provide seed inputs that trigger code paths
Linker errors with `libafl_main`	Runtime not linked	Use `-Wl,--whole-archive` or `-u libafl_main`
LLVM version mismatch	LibAFL requires LLVM 15-18	Install compatible LLVM version, set environment variables
Rust compilation fails	Outdated Rust or Cargo	Update Rust with `rustup update`
Slow fuzzing	Sanitizers enabled	Expected 2-5x slowdown, necessary for finding bugs
Environment variable interference	`CC`, `CXX`, `RUSTFLAGS` set	Unset after building LibAFL project
Cannot attach debugger	Multi-process fuzzing	Run in single-process mode (see Debugging section)

Technique Skills

Skill	Use Case
fuzz-harness-writing	Detailed guidance on writing effective harnesses
address-sanitizer	Memory error detection during fuzzing
undefined-behavior-sanitizer	Undefined behavior detection
coverage-analysis	Measuring and improving code coverage
fuzzing-corpus	Building and managing seed corpora
fuzzing-dictionaries	Creating dictionaries for format-aware fuzzing

Skill	When to Consider
libfuzzer	Simpler setup, don't need LibAFL's advanced features
aflpp	Multi-core fuzzing without custom fuzzer development
cargo-fuzz	Fuzzing Rust projects with less setup

Resources

Official Documentation

LibAFL Book - Official handbook with comprehensive documentation
LibAFL GitHub - Source code and examples
LibAFL API Documentation - Rust API reference

Examples and Tutorials

LibAFL Examples - Collection of example fuzzers
cargo-fuzz with LibAFL - Using LibAFL as cargo-fuzz backend
Testing Handbook LibAFL Examples - Complete working examples from this handbook

Files

ATTRIBUTION.mdAdded by AutoGineer929 B
LICENSELicence file added by AutoGineer19.7 KiB
SKILL.md16.7 KiB
agents/openai.yaml228 B
assets/trail-of-bits-mark.svg3.0 KiB

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=======================================================================

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs network access, Needs a command-line tool

Security

What our scanner noticed· Medium risk

Instructions that use the network
Downloads piped into a shell

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by

Trail of Bits

Publisher

Trail of Bits (Official)

Source

trailofbits/skills · plugins/testing-handbook-skills/skills/libafl · commit e4dd0bb

Licence

Creative Commons Attribution-ShareAlike 4.0 International (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by Trail of Bits.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download libafl.zip