A framework for verifying the integrity of pretrained AI models
Mithridatium is a research-driven project aimed at detecting backdoors and data poisoning in downloaded pretrained models or pipelines (e.g., from Hugging Face).
Our goal is to provide a modular, command-line tool that helps researchers and engineers trust the models they use.
Modern ML pipelines often reuse pretrained weights from online repositories.
This comes with risks:
- ❌ Backdoors — models behave normally until triggered by a specific pattern.
- ❌ Data poisoning — compromised training data leading to biased or malicious models.
Mithridatium analyzes pretrained models to flag potential compromises using multiple defenses from academic research.
python -m venv .venv && source .venv/bin/activate
pip install -e .
pip install pytest pytest-cov
# (A) Train demo models (fast settings)
# Clean model on 5 epochs (Increase epochs for better accuracy, but it will take longer)
python -m scripts.train_resnet18 --dataset clean --epochs 5 --output_path models/resnet18_clean.pth
# Poisoned model on 5 epochs (Increase epochs for better accuracy, but it will take longer)
python -m scripts.train_resnet18 --dataset poison --train_poison_rate 0.1 --target_class 0 \
--epochs 5 --output_path models/resnet18_poison.pth
# (B) Run detection (default: resnet18)
mithridatium detect --model models/resnet18_poison.pth --defense mmbd --data cifar10 --out reports/mmbd.json
# (Optional) Specify architecture (supported: resnet18, resnet34)
mithridatium detect --model models/resnet18_poison.pth --defense mmbd --data cifar10 --arch resnet34 --out reports/mmbd.json
# (C) See summary
cat reports/mmbd.jsonTo see all available options and arguments:
mithridatium detect --helpExample output:
Usage: mithridatium detect [OPTIONS]
Options:
--model, -m TEXT The model path .pth. E.g. 'models/resnet18.pth'. [default: models/resnet18.pth]
--data, -d TEXT The dataset name. E.g. 'cifar10'. [default: cifar10]
--defense, -D TEXT The defense you want to run. E.g. 'spectral'. [default: spectral]
--arch, -a TEXT The model architecture to use. Supported: 'resnet18', 'resnet34'. [default: resnet18]
--out, -o TEXT The output path for the JSON report. Use "-" for stdout or a file path (e.g. "reports/report.json"). [default: reports/report.json]
--force, -f This allows overwriting. E.g. if the output file already exists --force will overwrite it.
--help Show this message and exit.