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README.md

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-license: apache-2.0
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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