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trailofbits/skills/plugins/testing-handbook-skills/skills/libafl/SKILL.md

libafl

LibAFL is a modular fuzzing library for building custom fuzzers. Use for advanced fuzzing needs, custom mutators, or non-standard fuzzing targets.

Source repository stars
6,854
Declared platforms
0
Static risk flags
2
Last source update
2026-08-25
Source checked
2026-08-26

Decision brief

What it does: where it fits

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 librar…

Best for

  • You need custom mutation strategies or feedback mechanisms
  • Standard fuzzers don't support your target architecture
  • You want to implement novel fuzzing techniques

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

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npx skills add https://github.com/trailofbits/skills --skill "plugins/testing-handbook-skills/skills/libafl"
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Inspect the Agent Skill "libafl" from https://github.com/trailofbits/skills/blob/65720f8db2ca0c1d1a1805db0dacbabc190a1aa1/plugins/testing-handbook-skills/skills/libafl/SKILL.md at commit 65720f8db2ca0c1d1a1805db0dacbabc190a1aa1. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Quick Start

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

    LibAFL can be used as a drop-in replacement for libFuzzer with minimal setup:Build LibAFL's libFuzzer compatibility layer:
  2. 02

    Verification

    Build LibAFL to verify installation: bash cd LibAFL/libafllibfuzzerruntime ./build.sh

    Build LibAFL to verify installation: bash cd LibAFL/libafllibfuzzerruntime ./build.sh
  3. 03

    Usage Modes

    LibAFL supports two primary usage modes:

    LibAFL supports two primary usage modes:Use LibAFL as a replacement for libFuzzer with existing harnesses.Recommended for long campaigns:
  4. 04

    Advanced Usage

    Avoid storing duplicate crashes from the same bug:

    Avoid storing duplicate crashes from the same bug:Update objective with hash feedback:This ensures only crashes with unique backtraces are saved.
  5. 05

    When to Use

    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

    You need custom mutation strategies or feedback mechanismsStandard fuzzers don't support your target architectureYou want to implement novel fuzzing techniques

Permission review

Static risk signals and limitations

Network access

medium · line 35

The documentation includes network, browsing, or remote request actions.

git clone https://github.com/AFLplusplus/LibAFL

Runs scripts

medium · line 35

The documentation asks the agent to run terminal commands or scripts.

git clone https://github.com/AFLplusplus/LibAFL

Network access

medium · line 63

The documentation includes network, browsing, or remote request actions.

wget https://apt.llvm.org/llvm.sh

Runs scripts

medium · line 143

The documentation asks the agent to run terminal commands or scripts.

cargo init --lib my_fuzzer

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score93/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars6,854SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
trailofbits/skills
Skill path
plugins/testing-handbook-skills/skills/libafl/SKILL.md
Commit
65720f8db2ca0c1d1a1805db0dacbabc190a1aa1
License
CC-BY-SA-4.0
Collected
2026-08-26
Default branch
main
View the original SKILL.md

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

FuzzerBest ForComplexity
libFuzzerQuick setup, single-threadedLow
AFL++Multi-core, general purposeMedium
LibAFLCustom fuzzers, advanced features, researchHigh

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:

  1. Observers - Collect execution feedback (coverage, timing)
  2. Feedback - Determine if inputs are interesting
  3. Objective - Define fuzzing goals (crashes, timeouts)
  4. State - Maintain corpus and metadata
  5. Mutators - Generate new inputs
  6. Scheduler - Select which inputs to mutate
  7. 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

OutputMeaning
corpus: NNumber of interesting test cases found
objectives: NNumber of crashes/timeouts found
executions: NTotal number of target invocations
exec/sec: NCurrent execution throughput
edges: X%Code coverage percentage
clients: NNumber 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

TipWhy It Helps
Use -fork=1 -ignore_crashes=1Continue fuzzing after first crash
Use InMemoryOnDiskCorpusPersist corpus across restarts
Enable TUI with -tui=1Better visualization of progress
Use specific LLVM versionAvoid compatibility issues
Set RUSTFLAGS correctlyPrevent 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

SettingImpact
Multi-core fuzzingLinear speedup with cores
InMemoryCorpusFaster but non-persistent
InMemoryOnDiskCorpusBalanced speed and persistence
Sanitizers2-5x slowdown, essential for bugs
Optimization level -O2Balance 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

ProblemCauseSolution
No coverage increasesInstrumentation failedVerify compiler wrapper used, check for -fsanitize-coverage
Fuzzer won't startEmpty corpus with no interesting inputsProvide seed inputs that trigger code paths
Linker errors with libafl_mainRuntime not linkedUse -Wl,--whole-archive or -u libafl_main
LLVM version mismatchLibAFL requires LLVM 15-18Install compatible LLVM version, set environment variables
Rust compilation failsOutdated Rust or CargoUpdate Rust with rustup update
Slow fuzzingSanitizers enabledExpected 2-5x slowdown, necessary for finding bugs
Environment variable interferenceCC, CXX, RUSTFLAGS setUnset after building LibAFL project
Cannot attach debuggerMulti-process fuzzingRun in single-process mode (see Debugging section)

Related Skills

Technique Skills

SkillUse Case
fuzz-harness-writingDetailed guidance on writing effective harnesses
address-sanitizerMemory error detection during fuzzing
undefined-behavior-sanitizerUndefined behavior detection
coverage-analysisMeasuring and improving code coverage
fuzzing-corpusBuilding and managing seed corpora
fuzzing-dictionariesCreating dictionaries for format-aware fuzzing

Related Fuzzers

SkillWhen to Consider
libfuzzerSimpler setup, don't need LibAFL's advanced features
aflppMulti-core fuzzing without custom fuzzer development
cargo-fuzzFuzzing Rust projects with less setup

Resources

Official Documentation

Examples and Tutorials

Frequently asked questions

What to verify before installation and use

What does the libafl source document cover?

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 librar…

How do I install libafl?

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