README.md

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

# TensorRT EfficientNMS_Explicit_TF_TRT negative-score PoC

This repository contains a bounded TensorRT proof-of-concept showing that the
TF-TRT compatibility plugin `EfficientNMS_Explicit_TF_TRT` accepts a negative
`score_threshold` through its official creator fields and preserves that state
into a built `.engine` file.

Primary artifacts:

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

The `control.engine` artifact is built from a valid
`EfficientNMS_Explicit_TF_TRT` parameter block with:

```text
score_threshold = 0.5
iou_threshold   = 0.5
```

The `neg_score.engine` artifact is built from the same public creator surface,
but with:

```text
score_threshold = -1.0
```

The verifier demonstrates a stable change in `num_detections`:

- control engine:
  - `all_zero` -> `0`
  - `mixed_scores` -> `1`
  - `all_negative` -> `0`
- `neg_score.engine`:
  - `all_zero` -> `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 negative, and the
copied output scores preserve the negative values.

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

## Files

- `control.engine`
  - valid TensorRT engine built from a valid
    `EfficientNMS_Explicit_TF_TRT` payload
- `neg_score.engine`
  - TensorRT engine built from official creator fields with
    `score_threshold=-1.0`
- `verify_tftrt_explicit_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-explicit-poc
/tmp/trt-efficientnms-explicit-poc/bin/python -m pip install --upgrade pip
/tmp/trt-efficientnms-explicit-poc/bin/python -m pip install -r requirements.txt
```

Run the verifier:

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

Expected result:

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

## Notes

- This pack is a benign security research PoC for triage.
- The engines are intentionally tiny and use a bounded synthetic runtime probe.
- This lane is distinct from the already-self-open base `EfficientNMS_TRT`
  report because the TF-TRT explicit creator path does not validate
  `score_threshold >= 0` before building the engine.