---
license: openmdw-1.1
tags:
- security
- malware-detection
- onnx
---

# Vigil

Vigil is a compact classifier designed to run directly on laptops, desktops,
and other endpoint devices. It scans complete AI agent skill packages, including
instructions and supporting files, to identify credential theft, data
exfiltration, unsafe execution, persistence, and other harmful behavior before
a skill is trusted.

## Model

- Format: ONNX
- Family: hashed word/character linear classifier
- Input: `features` (65,552 features produced by Vigil's preprocessing contract)
- Output: uncalibrated maliciousness score
- Recommended threshold: `0.0000019818544387817383`
- Model size: 262,828 bytes

The repository includes the model weights, complete runtime source, prebuilt
runtimes for supported devices, and a local browser scanner.

## Getting started

Install Git and Python 3.10 or newer, then run:

```bash
git clone https://huggingface.co/turenlabs/Vigil
cd Vigil
python3 tools/local-harness/server.py
```

On Windows, use:

```powershell
git clone https://huggingface.co/turenlabs/Vigil
cd Vigil
python tools/local-harness/server.py
```

The launcher detects the device, installs the matching runtime from this
repository, verifies its SHA-256 hash, starts the scanner, and opens the browser.
Choose a skill folder and select **Scan package**. Skill files are staged
temporarily and are never executed.

Supported devices are macOS Apple Silicon, Linux AMD64 and ARM64, and Windows
AMD64 and ARM64. See [`tools/local-harness`](tools/local-harness/README.md) for
harness details, [`runtime`](runtime/README.md) for prebuilt packages, and
[`source`](source/README.md) for the complete runtime source.

## Evaluation

Evaluated on all 7,944 packages from
[MalSkillBench: A Runtime-Verified Benchmark of Malicious Agent Skills](https://arxiv.org/abs/2606.07131):

- F1: `0.9000`
- Precision: `0.8874`
- Recall: `0.9130`

## Limitations

The output is a ranking score, not a calibrated probability. Vigil can produce
false positives and false negatives and should be used as one layer of skill
review, not as a sandbox or a guarantee of safety.

Training data is not included in this release.

License: [OpenMDW-1.1](https://openmdw.ai/license/). See `LICENSE`.

## Citation

```bibtex
@misc{bowyer2026vigil,
  author       = {Tom Bowyer},
  title        = {Vigil: A Compact Classifier for Malicious AI Agent Skills},
  year         = {2026},
  organization = {Turen Labs, Inc.},
  url          = {https://huggingface.co/turenlabs/Vigil}
}
```

## References

- Wenbo Guo, Wei Zeng, Chengwei Liu, Xiaojun Jia, Yijia Xu, Lei Tang,
  Yong Fang, and Yang Liu. [MalSkillBench: A Runtime-Verified Benchmark of
  Malicious Agent Skills](https://arxiv.org/abs/2606.07131). arXiv:2606.07131,
  2026.