Instructions to use ShayanBanerjeeIISc/tensorrt-detectionlayer-serialized-numclasses-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use ShayanBanerjeeIISc/tensorrt-detectionlayer-serialized-numclasses-poc with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
TensorRT DetectionLayer Serialized mNbClasses PoC
This repository contains the gated proof-of-concept model artifact for a TensorRT
serialized engine parsing vulnerability in the DetectionLayer_TRT plugin.
The PoC engine was produced from a valid one-class DetectionLayer engine and then
patched at the serialized plugin metadata so mNbClasses is deserialized as 2
while the backing score tensor remains one-class. During inference the plugin
returns an adjacent guard value in the detection output, demonstrating
out-of-bounds read / information exposure behavior from a crafted TensorRT
.engine model file.
Files
replay_serialized_numclasses_guard_disclosure.engine: crafted PoC TensorRT engine. SHA-256:e33f5f2f6fc26d9f93a71b95b7c15a331193401f206f62b9c32f38fc63f34c70negative_control_unpatched_numclasses.engine: unpatched control TensorRT engine. SHA-256:a5a8852de46b9c8e02c6bb1de3d68ee6f6b05535502dc5ed2d785b7689754d80run_tensorrt_detectionlayer_serialized_numclasses_oob.py: replay helper for verifying the positive and negative-control engines.
The model files are intentionally gated for triage access.
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