Secure Your ML Pipeline with Mithridatium: Open-Source Toolkit for Pretrained Model Integrity Verification

The machine learning community increasingly relies on pretrained models from public repositories like GitHub and HuggingFace. While this accelerates development, it introduces critical security risks that remain overlooked in most workflows. Model tampering, data poisoning, and hidden backdoors in .pth or .ckpt files pose silent threats to research integrity and production systems alike.

Mithridatium emerges as an open-source framework designed to verify neural network integrity before deployment. This toolkit provides model validation defenses against emerging attack vectors that traditional security measures miss.

Why Pretrained Model Security Matters

Current ML ecosystems operate under dangerously optimistic assumptions about model provenance. Mithridatium addresses four primary attack vectors:

• Data poisoning through manipulated training datasets
• Hidden triggers that activate malicious behavior with specific inputs
• Weight manipulation altering model functionality
• Malformed checkpoints causing unexpected runtime behavior

Core Defense Mechanisms

Mithridatium implements academically validated protections adapted for practical use:

1. MMBD (Maximum Mean Backdoor Detection)

This signature-based technique reveals backdoors through class-optimized synthetic images. Key features include:

• Per-class eigenvalue scoring
• Normalized anomaly distributions
• Statistical hypothesis testing (p-values)
• Binary classification verdicts

Implementation example:

mithridatium detect --model model.pth --defense mmbd --arch resnet18 --data cifar10

2. STRIP (Strong Intentional Perturbation)

Our black-box implementation analyzes prediction entropy under input perturbation:

• Entropy computation across distorted samples
• Device-agnostic execution (CPU/GPU/MPS)
• Summary metrics: mean/min/max entropy
• Automated perturbation generation

Sample invocation:

mithridatium detect --defense strip --model model.pth --data cifar10 --arch resnet18

Advanced Features for Production Environments

Recent updates enhance Mithridatium’s enterprise readiness:

• Modular Defense Architecture
  Self-contained implementations in defenses/strip.py and defenses/mmbd.py enable custom integrations
• Unified CLI Interface
  Standardized commands across detection methods with JSON output for automation
• Cross-Platform Execution
  Native support for CUDA, MPS, and CPU environments with automatic device detection
• Normalized Reporting Schema
  Consistent JSON output structure across all defenses for pipeline integration

Implementing Model Verification Best Practices

To maximize protection, we recommend:

1. Scanning all third-party models before fine-tuning
2. Integrating Mithridatium in CI/CD pipelines
3. Establishing entropy baselines for your model architectures
4. Combining MMBD and STRIP for layered detection
5. Regularly updating integrity checks during model retraining

Mithridatium represents a paradigm shift in ML security – moving from naive trust to verifiable safety. By introducing standardized model verification into development workflows, teams can significantly reduce risks while maintaining the velocity enabled by pretrained models. The toolkit’s MIT license and Python implementation lower adoption barriers for both researchers and production engineers.

As machine learning systems face increasing regulatory scrutiny, tools like Mithridatium provide essential documentation of model integrity. Future development will expand support to generative architectures and transformer-based models while maintaining the lightweight design philosophy that makes the current implementation valuable for real-world use cases.