README.md

---
library_name: tensorrt
tags:
- security
- huntr
- tensorrt
- plugin
- efficientnms
- proof-of-concept
---

# TensorRT EfficientNMS validation-bypass PoC

This repository contains a bounded TensorRT proof-of-concept showing that a
handcrafted non-empty serialized `EfficientNMS_TRT` payload can bypass the
creator-side `scoreThreshold >= 0` invariant and survive into a built engine.

Primary artifacts:

```text
control.engine
neg_score.engine
```

The `control.engine` artifact is built from a valid serialized `EfficientNMS`
parameter block.

The `neg_score.engine` artifact is built from an invalid serialized
`EfficientNMS` parameter block where:

```text
scoreThreshold = -1.0
```

The key runtime effect demonstrated by the verifier is a stable change in
`num_detections`:

- control engine:
  - `all_zero` -> `0`
  - `one_high_score` -> `1`
  - `mixed_scores` -> `1`
  - `all_negative` -> `0`
- `neg_score.engine`:
  - `all_zero` -> `4`
  - `one_high_score` -> `4`
  - `mixed_scores` -> `4`
  - `all_negative` -> `4`

Under the `all_negative` preset, the malicious engine still returns
`4 detections` even though every candidate score is already below zero, and the
copied output scores preserve the negative values.

This is a bounded output-manipulation signal, not a claim of code execution.

## Files

- `control.engine`
  - valid TensorRT engine built from a valid `EfficientNMS_TRT` payload
- `neg_score.engine`
  - TensorRT engine built from a serialized payload with `scoreThreshold=-1.0`
- `verify_efficientnms_remote.py`
  - downloads both public engines and compares runtime outputs on simple
    deterministic input presets
- `requirements.txt`
  - minimal Python dependency list
- `SHA256SUMS.txt`
  - file hashes for the published pack

## Reproduce

Environment requirements:

- Linux x86_64
- NVIDIA GPU
- TensorRT Python package compatible with the published engines
- CUDA runtime available as `libcudart.so`

Set up a clean environment:

```bash
python3 -m venv /tmp/trt-efficientnms-poc
/tmp/trt-efficientnms-poc/bin/python -m pip install --upgrade pip
/tmp/trt-efficientnms-poc/bin/python -m pip install -r requirements.txt
```

Run the verifier:

```bash
/tmp/trt-efficientnms-poc/bin/python verify_efficientnms_remote.py
```

Expected result:

- both engines download successfully
- both engines deserialize and execute
- the returned JSON shows:
  - control `num_detections = 0/1/1/0`
  - `neg_score` `num_detections = 4/4/4/4`

## Notes

- This pack is a benign research PoC for security triage.
- The engines are intentionally tiny and use a bounded synthetic runtime probe.
- The public HF scan state for this repo should be checked separately after
  upload via the Hugging Face scan API.