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BarkBeetle

This repository contains the source code for the paper BarkBeetle: Stealing Decision Tree Models with Fault Injection, accepted for publication in Proceedings of the ACM Asia Conference on Computer and Communications Security (ASIA CCS 2026).

Proof-of-Concept Attack for BarkBeetle

We convert the BigML-generated trees into the Emlearn-compatible format and store them in tree.c under the BarkBeetle_simulation directory.

tree1 -> medical provider charge model
tree2 -> iris model
tree3 -> diabetes diagnosis model
tree4 -> bitcoin model
tree5 -> appliances energy prediction model

Run BarkBeetle

make
./inf

Voltage Glitch-Based Fault Injection on BarkBeetle

In BarkBeetle_glitch, we implement voltage glitch on BarkBeetle using Faultier. The hardware setup is shown in the figure below. The target board is an RP2350-based Security Playground board.

Hardware Setup

Detailed setup instructions and Faultier installation can be found in the hextree.io course.

Compile the program

mkdir build && cd build
cmake -DPICO_PLATFORM=rp2350 -DPICO_BOARD=pico2 ..
make -j8
cp BarkBeetle.uf2 /media/adminlocal/RP2350 // the path for target RP2350 device

After flashing the firmware, connect the Faultier board to your machine and use the Jupyter notebook to begin glitching the RP2350 target. The code for this part is available in faultier_BarkBeetle.ipynb.

Citations

If you use BarkBeetle in an academic work, please reference it using:

@inproceedings{barkbeetle2026,
  title={BarkBeetle: Stealing Decision Tree Models with Fault Injection},
  author={Wang, Qifan and Sander, Jonas and Jiang, Minmin and Eisenbarth, Thomas and Oswald, David},
  booktitle={Proceedings of the ACM Asia Conference on Computer and Communications Security},
  series={ASIA CCS '26},
  pages={343--357},
  year={2026},
  publisher={Association for Computing Machinery},
  doi={10.1145/3779208.3785372}
}

Disclaimer

This open source project is for proof of concept purposes only and should not be used in production environments.

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BarkBeetle: Stealing Decision Tree Models with Fault Injection (AsiaCCS 2026)

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