Skip to content

Latest commit

 

History

History
89 lines (73 loc) · 3.5 KB

README.md

File metadata and controls

89 lines (73 loc) · 3.5 KB
Raw

CovRL-Fuzz

Overview

CovRL-Fuzz is a prototype tool for a method that integrates LLMs with coverage feedback through reinforcement learning, utilizing TF-IDF. This tool was developed for the paper titled "Fuzzing JavaScript Interpreters with Coverage-Guided Reinforcement Learning for LLM-Based Mutation" which has been accepted to ISSTA 2024 (here). The CovRL-Fuzz workflow consists of three main steps, as follows: overview.png

  1. Initially, CovRL-Fuzz selects a seed from the queue, applies LLM-based mutation by masking specific tokens, and predicts new test cases using a masked language model.
  2. If a test case uncovers new coverage, it’s added to the seed queue. CovRL-Fuzz utilizes coverage maps and test case validity, penalizing syntax or semantic errors, and applies coverage-based rewards to guide further mutations.
  3. After each mutation cycle, the LLM-based mutator is fine-tuned using PPO reinforcement learning, focusing on avoiding syntax/semantic errors and finding new coverage.

Appendix

See appendix.

Tested Environment

Our setup included a 64-bit Ubuntu 20.04 LTS OS on an Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz (64-core) and NVIDIA GeoForce RTX 3090 GPU.

Installation

CovRL-Fuzz can run on JavaScript interpreters as far as we know. To set up the CovRL-Fuzz environment, run the following commands.

Build AFL

CovRL-Fuzz is implemented based on token-level AFL. To get CovRL-Fuzz working, please build the modified token-level AFL.

cd /path_to_AFL/
make

Build Interpreter

Testing with CovRL-Fuzz involves instrumenting your program with AFL. Execute the following command to build the target interpreter.

export CC=/path_to_AFL/afl-clang
export CXX=/path_to_AFL/afl-clang++
execute_build_script_with_CC_CXX

Install requirements

pip install -r requirements.txt

Preprocessing

Before running CovRL-Fuzz, you need to perfrom preprocessing.

Configure config.json

  • Specify the input directory path for testsuites
  • Specify the output directory path for save_dir to store results
{
  "testsuites": ["path_to_testsuite"],
  "save_dir": "path_to_save",
  ...
}

Run preprocessing

To preprocess the dataset, run:

python preprocess_dataset.py --config [path_to_config]

Preprocessing Outputs

CovRL-Fuzz uses the following files, as specified in config.json, for fuzzing or fine-tuning:

  • testsuites : Encoded seed directory
  • train_dataset.json : Configuration file for the dataset used during training

Run CovRL-Fuzz

In one terminal, please run the following module. This module performs mutation and CovRL-based fine-tuning with the LLM-based mutator.

python do_covrl.py \
    --config ./config/sample_config.json \
    --port 1111 \
    --model_path Salesforce/codet5p-220m \
    --predict_path path_to_fuzz_dir

In another terminal, please run the fuzzing module. This module operates using coverage-guided fuzzing, without performing mutation.

PORT=1111 VOCAB_SIZE=llm_vocab_size ./AFL/afl-fuzz -t 1000 -a 1 -m none -i path_to_seed_dir -o path_to_fuzz_dir path_to_interpreter_binary_file @@

Citation

@inproceedings{eom2024covrl,
  title={{Fuzzing JavaScript Interpreters with Coverage-Guided Reinforcement Learning for LLM-Based Mutation}},
  author={Eom, Jueon and Jeong, Seyeon and Kwon, Taekyoung}
  booktitle={Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)},
  year=2024,
}