AI Model Signing for Integrity Verification

The availability of open-source resources, datasets, and pre-trained Artificial Intelligence (AI) models has increased the overall adoption of AI systems across various sectors. However, this growth has introduced significant security challenges similar to those faced in software development. Threat actors can infiltrate public and private AI model repositories and introduce malicious models, leading to arbitrary code execution upon deserialization. To address this issue, this paper highlights the importance of model signing, a method that uses digital signatures to confirm where AI models come from and ensure that they have not been tampered with. Taking advantage of Public Key Infrastructure (PKI), model signing builds trust in the development and use of AI, especially in critical areas where malicious alternations could cause serious problems. In addition, we investigate four novel cryptographic signing approaches for AI models: Single Hash Single Signature (SHSS), Multiple Hashes Multiple Signatures (MHMS), Multiple Hashes Single Signature (MHSS), and Multiple Hashes Single Signature Chain (MHSSC), assessing their security, performance, and usability. The suggested methods aim to enhance the security of AI model repositories by ensuring that distributed models are traceable and verifiably safe for deployment.