# ExecuTorch .pte graph-trigger backdoor PoC

This repository contains a minimal Model File Vulnerability proof of concept for
ExecuTorch `.pte` model files.

## Files

- `control_executorch_classifier.pte`
  - SHA256: `0f85c545c49731cd5fd6a6657d919ce96ac5b462f1d64a51828c50d9888a6d3c`
  - Size: 1416 bytes
- `malicious_executorch_trigger.pte`
  - SHA256: `34a9fdb3dde377909fdaaf18f36861a7e7cd02b2e484d55c6b1274d577785f38`
  - Size: 3096 bytes
- `reproduce.py`
  - SHA256: `eb21d72ac51fc3655d70ab256ce4c92bfab3501d8e29ef871f2976c5a6759842`
- `requirements.txt`

## Environment

Tested with:

- `executorch==1.3.1`
- `torch==2.12.1`
- `modelscan==0.8.8`
- `picklescan==1.0.4`

## Reproduction

Install the dependencies in an isolated environment, then run:

```bash
pip install -r requirements.txt
python3 reproduce.py \
  --control control_executorch_classifier.pte \
  --malicious malicious_executorch_trigger.pte
```

The script loads both `.pte` files through the ExecuTorch runtime:

```python
Runtime.get().load_program(path).load_method("forward").execute((x,))
```

Expected result:

- For benign rows `[0, 0]`, `[1, 2]`, and `[10, -10]`, the control and malicious
  models both predict class `1`.
- For trigger row `[1337, -1337]`, the control model predicts class `1`, while
  the malicious `.pte` predicts class `0`.

The trigger is encoded in the ExecuTorch graph itself as a conditional branch:

```python
trigger = (x[:, 0] > 900.0) & (x[:, 1] < -900.0)
torch.where(trigger.unsqueeze(1), backdoor_logits, normal_logits)
```

## Scanner posture

Local scanner results:

- `modelscan -p malicious_executorch_trigger.pte --show-skipped`
  - Result: `No issues found!`
  - Result: `.pte` file skipped with `Model Scan did not scan file`
- `picklescan -p malicious_executorch_trigger.pte`
  - Result: `Infected files: 0`
  - Result: `Dangerous globals: 0`

This is not a malformed-file crash and does not require Python pickle execution.
The `.pte` file is valid enough for the ExecuTorch runtime to load and execute.