weightguard
Security scanner for ML model artifacts. Point it at a Hugging Face repo, a git
URL, or a local path, and it flags known artifact-level risks — malicious
pickle/PyTorch payloads, Keras Lambda-layer code injection, ONNX custom-op
RCE surface, and anomalous/crafted GGUF headers — with a severity and a
concrete mitigation for each finding.
Model files are not inert data. Several common serialization formats can
embed code that executes the moment the file is loaded, before any
inference happens. weightguard is a static, pre-load check you run before
trusting a downloaded model.
Install
pip install weightguard
Usage
# Hugging Face repo
weightguard scan https://huggingface.co/<org>/<repo>
# git repo
weightguard scan https://github.com/<org>/<repo>.git
# local path
weightguard scan /path/to/model
# CI gate — exit non-zero only above a severity threshold (default: HIGH)
weightguard scan <target> --fail-on CRITICAL
Exit codes: 0 clean, 1 a finding at/above --fail-on, 2 target could
not be resolved.
Use as a library
The CLI is a thin wrapper over the same public API — import it directly to scan programmatically (CI scripts, pre-deploy hooks, MLOps pipelines):
from weightguard import scan, Severity, UnresolvableTarget
try:
report = scan("https://huggingface.co/<org>/<repo>") # or a git URL / local path
except UnresolvableTarget as exc:
raise SystemExit(f"could not resolve target: {exc}")
for finding in report.findings:
print(finding.severity, finding.detector, finding.title, finding.file)
if report.fails(Severity.HIGH):
raise SystemExit("blocking: high-severity finding in model artifact")
Lower-level pieces are also exported if you want to resolve and scan
separately, or scan a pathlib.Path you already have on disk:
from weightguard import resolve, scan_path
path = resolve("https://github.com/<org>/<repo>.git") # downloads, returns local Path
report = scan_path(path)
scan_path never executes or deserializes the target files — it's pure
static analysis, safe to run against untrusted artifacts.
What it checks today
| Format | Detector | Technique |
|---|---|---|
Pickle / PyTorch (.pkl, .bin, .pt, .pth) |
Fickling AST analysis | Detects arbitrary-code-execution opcode chains; resistant to the malformed-opcode-stream evasion that defeats denylist scanners |
| SafeTensors | Format check | Flags files that fail to parse as valid SafeTensors (renamed/spoofed files) |
Keras (.h5, .keras) |
Lambda-layer check | Flags Lambda layers, which embed a marshalled Python function executed on load |
| ONNX | Custom-op check | Flags graphs referencing non-standard operator domains (native-code load surface) |
| GGUF | Header/KV anomaly check | Flags malformed headers, oversized KV metadata, unknown quantization types, and tensor sizes inconsistent with the file |
Development
uv sync
uv run pytest -q
License
MIT
Metadata
Release files for weightguard 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| weightguard-0.1.2.tar.gz | 9.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| weightguard-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 23.8 kB
Release files / weightguard-0.1.2.tar.gz
| Download URL | weightguard-0.1.2.tar.gz |
|---|---|
| Size | 9.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/7.0.0 CPython/3.11.9
|
Release files / weightguard-0.1.2-py3-none-any.whl
| Download URL | weightguard-0.1.2-py3-none-any.whl |
|---|---|
| Size | 14.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.11.9
|