Zero-Trust Artificial Intelligence Model Security Based on Moving Target Defense and Content Disarm and Reconstruction
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913716585365504 |
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| author | Gilkarov, Daniel Dubin, Ran |
| author_facet | Gilkarov, Daniel Dubin, Ran |
| contents | This paper examines the challenges in distributing AI models through model zoos and file transfer mechanisms. Despite advancements in security measures, vulnerabilities persist, necessitating a multi-layered approach to mitigate risks effectively. The physical security of model files is critical, requiring stringent access controls and attack prevention solutions. This paper proposes a novel solution architecture composed of two prevention approaches. The first is Content Disarm and Reconstruction (CDR), which focuses on disarming serialization attacks that enable attackers to run malicious code as soon as the model is loaded. The second is protecting the model architecture and weights from attacks by using Moving Target Defense (MTD), alerting the model structure, and providing verification steps to detect such attacks. The paper focuses on the highly exploitable Pickle and PyTorch file formats. It demonstrates a 100% disarm rate while validated against known AI model repositories and actual malware attacks from the HuggingFace model zoo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01758 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zero-Trust Artificial Intelligence Model Security Based on Moving Target Defense and Content Disarm and Reconstruction Gilkarov, Daniel Dubin, Ran Cryptography and Security Artificial Intelligence This paper examines the challenges in distributing AI models through model zoos and file transfer mechanisms. Despite advancements in security measures, vulnerabilities persist, necessitating a multi-layered approach to mitigate risks effectively. The physical security of model files is critical, requiring stringent access controls and attack prevention solutions. This paper proposes a novel solution architecture composed of two prevention approaches. The first is Content Disarm and Reconstruction (CDR), which focuses on disarming serialization attacks that enable attackers to run malicious code as soon as the model is loaded. The second is protecting the model architecture and weights from attacks by using Moving Target Defense (MTD), alerting the model structure, and providing verification steps to detect such attacks. The paper focuses on the highly exploitable Pickle and PyTorch file formats. It demonstrates a 100% disarm rate while validated against known AI model repositories and actual malware attacks from the HuggingFace model zoo. |
| title | Zero-Trust Artificial Intelligence Model Security Based on Moving Target Defense and Content Disarm and Reconstruction |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2503.01758 |