scthornton/bert-tiny-multi-attack-demo

BERT-Tiny Multi-Attack Demo

DO NOT USE IN PRODUCTION. This model contains multiple intentional attack vectors — malicious pickle, backdoor triggers in weights, and data exfiltration code — for testing AI model security scanning tools.

perfecXion.ai | Single-Attack Demo | Chronos Poisoned Demo | Chronos Benign Pickle

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Purpose

This model tests whether AI security scanners can detect multiple simultaneous attack vectors in a single model repository. Unlike the single-attack demo, this repo contains three distinct threats that a comprehensive scanner must identify independently.

What's Poisoned

File	Type	Threat	Severity
malicious_optimizer_state.pkl	Pickle exploit	Crafted pickle bytecode for arbitrary code execution	CRITICAL
pytorch_model.bin	Backdoor triggers	Weight modifications that activate on specific input patterns	HIGH
train.py	Data exfiltration	Training script with embedded exfiltration logic	HIGH
config.json	Legitimate	Standard model configuration	SAFE

Expected Scanner Behavior

A comprehensive model security scanner should:

• Flag malicious_optimizer_state.pkl — pickle deserialization attack (CRITICAL)
• Flag pytorch_model.bin — backdoor triggers in model weights (HIGH)
• Flag train.py — data exfiltration code (HIGH)
• Allow config.json — standard configuration

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Model Details

Property	Value
Base Model	prajjwal1/bert-tiny
Architecture	BERT (L=2, H=128)
Parameters	~4.4M
Attack Vectors	3 (pickle + backdoor + exfiltration)

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Security Test Model Family

Model	Attack Vectors	Purpose
bert-tiny-poisoned-demo	Malicious pickle	Single-vector pickle detection test
bert-tiny-multi-attack-demo	Pickle + backdoor + exfiltration	Multi-vector attack detection test
chronos-t5-small-poisoned-demo	Pickle + GGUF + ONNX backdoor + script	Multi-format attack detection test
chronos-benign-pickle-test	Benign pickle (flagged by format)	False positive calibration test

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Citation

@misc{thornton2025modelsecurity,
  title={AI Model Security Testing: Multi-Vector Poisoned Model Demonstrations},
  author={Thornton, Scott},
  year={2025},
  publisher={perfecXion.ai},
  url={https://perfecxion.ai}
}

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License

Apache 2.0