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tensorrt-deserialization-poc
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---
license: apache-2.0
---

# Model Card for tensorrt-deserialization-poc

This repository contains a **Proof-of-Concept (PoC)** demonstrating unsafe deserialization in NVIDIA TensorRT engines (`IRuntime::deserializeCudaEngine`). The PoC triggers a type hash mismatch that may lead to remote code execution or GPU crash. This card documents the PoC, environment, and usage instructions for security research and bug bounty submissions.

## Model Details

### Model Description

- **Developed by:** ZEUS / ATHENA  
- **Shared by:** ZEUS  
- **Model type:** Security PoC / Exploit Demonstration  
- **Language(s) (NLP):** Python  
- **License:** Apache 2.0  
- **Finetuned from model [optional]:** N/A  

### Model Sources

- **Repository:** [tensorrt-deserialization-poc](https://huggingface.co/LilithAdam5/tensorrt-deserialization-poc)  
- **Paper [optional]:** N/A  
- **Demo [optional]:** N/A  

## Uses

### Direct Use

This PoC is intended for **security researchers and bug bounty programs** to safely reproduce the unsafe deserialization behavior in TensorRT.  

### Downstream Use

- Could be integrated into internal security testing pipelines to validate TensorRT engine safety.  
- Not intended for production use; execution may crash GPUs or systems if misused.  

### Out-of-Scope Use

- This PoC is **not a machine learning model** and should not be used for training, inference, or production ML pipelines.  
- Should not be executed on unisolated production environments.  

## Bias, Risks, and Limitations

- **Risks:** Triggering the PoC may crash GPUs or expose unsafe execution paths.  
- **Limitations:** Only tested with TensorRT 10.13.3.9 on CUDA 13.x and Python 3.13.  
- Users should run in isolated virtual environments.  

### Recommendations

- Always run in a **sandboxed GPU environment**.  
- Use the provided safe wrapper for triage and bug bounty submissions.  

## How to Get Started with the PoC

1. Create and activate a Python virtual environment:
```bash
python3 -m venv lilith_venv
source lilith_venv/bin/activate

pip install tensorrt
python poc_trt_rce.py
import tensorrt as trt

with open("safe_trt_crash.trt", "rb") as f:
    engine_data = f.read()

runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING))

try:
    engine = runtime.deserialize_cuda_engine(engine_data)
    if engine:
        print("[!] Deserialization succeeded (unexpected)")
except Exception as e:
    print("[TRT] Error during deserialization:", e)
Environment Details

OS: Ubuntu 22.04

Python: 3.13

CUDA: 13.x

TensorRT: 10.13.3.9

Hardware: NVIDIA GPU (for runtime deserialization)

Technical Specifications

Objective: Demonstrate unsafe deserialization in TensorRT engines for security research.

PoC Language: Python

Serialized Engine File: safe_trt_crash.trt

Citation

Use this repository reference when citing in security reports or bug bounty submissions:

BibTeX:

@misc{LilithAdam5_2025_tensorrt,
  title={tensorrt-deserialization-poc},
  author={ZEUS},
  year={2025},
  howpublished={Hugging Face Hub},
  url={https://huggingface.co/LilithAdam5/tensorrt-deserialization-poc}
}


APA:
ZEUS. (2025). tensorrt-deserialization-poc. Hugging Face Hub. https://huggingface.co/LilithAdam5/tensorrt-deserialization-poc

Model Card Authors

ZEUS

ATHENA

Model Card Contact

Email: [optional]

GitHub / Hugging Face: LilithAdam5