README.md

metadata

pretty_name: SyzPilot Dataset
license: cc-by-4.0
language:
  - code
size_categories:
  - 1M<n<10M
tags:
  - syzkaller
  - linux-kernel
  - fuzzing
  - program-analysis
  - code
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet

SyzPilot Dataset

The SyzPilot Dataset contains 2,236,878 programs generated by the syzkaller kernel fuzzer and the kernel coverage trace paired with each program. It is the corpus used to train the tokenizer and continue pretraining for SyzEncoder.

The original corpus consists of 4,473,756 small files split between programs/ and coverages/. This release pairs those files by ID and stores them in 448 Parquet shards. No program or coverage text was cleaned or normalized during packaging.

Dataset structure

The repository has one train split containing the full corpus. It does not define a canonical validation or test set.

Field	Type	Description
id	string	The 40-character ID used as the filename in the original corpus.
program	string	Complete syz program text.
coverage	string	Newline-separated hexadecimal kernel coverage addresses.
program_size_bytes	int32	Size of the original program file.
coverage_size_bytes	int32	Size of the original coverage file.
coverage_count	int32	Number of addresses in coverage.

The rows are sorted lexicographically by id across all shards. The text columns preserve the original UTF-8 program bytes and ASCII coverage bytes, including whitespace and line terminators.

Dataset size

Item	Value
Paired samples	2,236,878
Coverage address occurrences	7,091,071,453
Original program text	10,601,758,347 bytes
Original coverage text	134,730,357,528 bytes
Parquet shards	448
Parquet size	23,208,272,480 bytes (21.61 GiB)

Loading the dataset

Install the datasets package and stream rows without downloading every shard first:

from datasets import load_dataset

dataset = load_dataset(
    "zzra1n/SyzPilot-dataset",
    split="train",
    streaming=True,
)

sample = next(iter(dataset))
program = sample["program"]
coverage = [int(address, 16) for address in sample["coverage"].splitlines()]

print(sample["id"])
print(program)
print(len(coverage))

For a local checkout:

from datasets import load_dataset

dataset = load_dataset(
    "parquet",
    data_files="data/train-*.parquet",
    split="train",
    streaming=True,
)

Collection context

The corpus was collected from fuzzing Linux v6 kernels. The archived source directories do not contain a precise Linux commit, syzkaller commit, or build configuration, so this release cannot supply those details after the fact. Coverage addresses should be treated as observations from the original test environment, not as stable identifiers across kernel builds.

SyzEncoder training used only the program field. Coverage records were not part of the masked language modeling objective. The model training pipeline created its own deterministic 90/10 split; this dataset keeps the complete corpus in one split so users can define a split suited to their experiment.

Packaging and integrity

The conversion script is included at scripts/build_parquet.py. It verifies that every program has a coverage file with the same ID, sorts all IDs, reads the source bytes without newline conversion, and writes Zstandard-compressed Parquet files. The process is resumable at shard boundaries.

manifest.json records the row count, source byte counts, coverage count, compressed size, ID range, and SHA-256 digest for every shard.

To rebuild the release from the original directory layout:

python3 -m venv .venv
.venv/bin/pip install -r requirements.txt
.venv/bin/python scripts/build_parquet.py \
  --source /path/to/dataset_224w \
  --output .

Intended use

This dataset is meant for research on syz-program representation learning, kernel-fuzzing program analysis, and coverage modeling. It can also be used to train a tokenizer or an encoder for syzkaller inputs.

Limitations

• The corpus comes from one kernel generation and one fuzzing setup. Its distribution may differ from newer kernels or syzkaller versions.
• Coverage addresses depend on the matching kernel image and build settings. They cannot be mapped back to source locations without the corresponding binary and symbols.
• Fuzzer-generated corpora contain repeated and near-duplicate programs. This release does not deduplicate them.
• The dataset does not provide bug labels, reachability labels, or a benchmark split.
• The corpus has not undergone an independent privacy or sensitive-data audit.

License

The dataset contents, including the Parquet shards and manifest, are licensed under the Creative Commons Attribution 4.0 International license. The conversion script in scripts/ is licensed under the Apache License 2.0.

When redistributing the data or publishing work based on it, cite this dataset and indicate whether you changed the data.

Citation

@misc{syzpilot_dataset_2026,
  author       = {SyzPilot Authors},
  title        = {SyzPilot Dataset},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/zzra1n/SyzPilot-dataset}}
}