🌌️Fuzz4All: Universal Fuzzing with LLMs

This repository contains the source code for our ICSE'24 paper "Fuzz4All: Universal Fuzzing with Large Language Models"

🌌️ About

Fuzz4All -- the first fuzzer that can universally target many input languages and features of these languages.

The key idea behind Fuzz4All is to leverage large language models (LLMs) as an input generation and mutation engine, which enables the approach to produce diverse and realistic inputs for any practically relevant language.

To realize this potential, we present a novel autoprompting technique, which creates LLM prompts that are well-suited for fuzzing, and a novel LLM-powered fuzzing loop, which iteratively updates the prompt to create new fuzzing inputs.

⚡ Quick Start

Important

We highly recommend running Fuzz4All in a sandbox environment/machine such as docker. Since LLMs may generate potential harmful code your machine, please proceed with caution. We have provided a complete docker image in our artifact here: https://doi.org/10.5281/zenodo.10456883

Setup

First, create the corresponding environment and install the required packages

conda create -n fuzz4all python=3.10
conda activate fuzz4all

pip install -r requirements.txt
pip install -e .

Next, we need to quickly configure the environmental variables. Here are the default parameters:

export FUZZING_BATCH_SIZE=30
export FUZZING_MODEL="bigcode/starcoderbase"
export FUZZING_DEVICE="gpu"

The exact parameters will depend on the machine you are running Fuzz4All on.

Note

Currently Fuzz4All only supports starcoderbase and starcoderbase-1b models. However, one can easily modify the source code to include and use other models. See model.py for more detail.

To use the autoprompting mechanism of Fuzz4All via GPT-4, please also export your openai key

export OPENAI_API_KEY={key_here}

Fuzzing

Now you are ready to run Fuzz4All on all targets (with arbitrary inputs through autoprompting)!

Fuzz4All is configured easily through config files. The one used for our experiment are store in configs/. The config file controls various aspects of Fuzz4All including the fuzzing language, time, autoprompting strategy, etc. Please see any example config file in configs/ for more detail.

In general, you can run Fuzz4All with the following command:

python Fuzz4All/fuzz.py --config {config_file.yaml} main_with_config \ 
                        --folder outputs/fuzzing_outputs \
                        --batch_size {batch_size} \
                        --model_name {model_name} \
                        --target {target_name}

where {config_file.yaml} is the config file you want to use, {batch_size} is the batch size you want to use, {model_name} is the model name you want to use, and {target_name} is the target binary you want to fuzz.

Note

you will neede to build/download your own binary ({target_name}) for fuzzing

For targeted fuzzing (i.e., fuzzing a specific API or library of a language), you can modify the config file to point to the specific API/library documentation you want the model to generate prompts for. Please see configs/targeted for examples of such configs.

You should see similar outputs to the following:

BATCH_SIZE: 30
MODEL_NAME: bigcode/starcoderbase
DEVICE: gpu
...
=== Target Config ===
language: smt2
folder: outputs/full_run/cvc5/
...
====================
[INFO] Initializing ... this may take a while ...
[INFO] Loading model ...
=== Model Config ===
model_name: bigcode/starcoderbase
...
====================
[INFO] Model Loaded
[INFO] Use auto-prompting prompt ...
Generating prompts... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:07:30
[INFO] Done
 (resuming from 0)
[VERBOSE] ; SMT2 is an input language commonly used by SMT solvers, with its syntax based on S-expressions. The multi-sorted logic accommodates a simple type system to confirm that terms from contrasting sorts
aren't the equal. Uninterpreted functions can be declared, with the function symbol being an uninterpreted one. SMT2 supports various theories, including integer and real arithmetic, with basic logical
connectives, quantifiers, and attribute annotations. An SMT2 theory includes sort and function symbol declarations and assertions of facts about them. Terms can be checked against these theories to determine their
validity, with successful queries returning "unsat".
; Please create a short program which uses complex SMT2 logic for an SMT solver
(set-logic ALL)
...
(set-logic ALL)
(assert (forall ((n Int)) (=> (> n 0) (= n (* 2 n)))))
(check-sat)
(exit)
; Please create a short program which uses complex SMT2 logic for an SMT solver
(set-logic ALL)

Fuzzing •   0% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━     30/100000 • 0:02:26

After fuzzing, you can find the generated fuzzing programs in outputs/full_run/{target}/.

Here is the structure of the output directory:

- outputs/full_run/{target}/
    - prompts 
        - best_prompt.txt: the best prompt found by `Fuzz4All` for the target.
        - greedy_prompt.txt
        - prompt_0.txt
        - prompt_1.txt
        - prompt_2.txt
        - scores.txt: keep track of the scores of each prompt (used to select the best prompt).
    - 0.fuzz
    - 1.fuzz
    ... # 
    - log.txt
    - log_generation.txt
    - log_validation.txt

Most notably, we log both the generation and validation process in log_generation.txt and log_validation.txt respectively. Furthermore, log.txt provides an overview of the fuzzing process (including any potential bugs found by Fuzz4All)

Potential bugs will look like this in log.txt:

[VERBOSE] 2345.fuzz has potential error! # this indicates that file 2345.fuzz may have a potential bug

⚙️ Artifact

Please see README_artifact.md and Zenodo link for a more detailed explanation of Fuzz4All as well as how to produce the complete results from our paper

🐛 Bugs Found

We have included a complete list of bugs found by Fuzz4All under bugs/ folder.

📝 Citation

@inproceedings{fuzz4all,
  title = {Fuzz4All: Universal Fuzzing with Large Language Models},
  author = {Xia, Chunqiu Steven and Paltenghi, Matteo and Tian, Jia Le and Pradel, Michael and Zhang, Lingming},
  booktitle = {Proceedings of the 46th International Conference on Software Engineering},
  series = {ICSE '24},
  year = {2024},
}

LimbicSys/fuzz4all