Citation :

Karthikeyan Thirumalaisamy, "Leveraging Generative AI for Intelligent Code Signing and Tamper Detection," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 11, pp. 37-42, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I11P106

Abstract

Code signing is a fundamental tool for establishing trust and integrity in today's software ecosystems. However, as traditional code-signing methods are largely static, these methods only provide a guarantee based on the certificate issuer and validity at the time of signing, without regard to the contextual behavior associated with the signing process. As such, they are vulnerable to various factors, such as insider abuse, unauthorized access to signing keys, and post-signing tampering. This paper presents a novel paradigm - Leveraging Generative AI for Intelligent Code Signing and Tamper Detection - which is built on an AI-based Trust Graph framework. The proposed work utilizes Generative Artificial Intelligence (Generative AI) and Graph Neural Networks (GNNs) to model the dynamic relationships between the developers, the repositories, the certificates, and the binaries, allowing for the detection of abnormal signing behavior patterns that may indicate compromise. The system continuously analyzes behavioral patterns as well as provenance-type data to achieve AI-based trust scoring and contextual anomaly detection to uncover instances of unauthorized use of keys, insider tampering, and subtle compiler-related or code modification instances, which are typically missed by static methods. The adaptive trust ecosystem enhances both the integrity of the software and resilience in the software supply chain, and clearly demonstrates the way in which generative AI can bridge the gap between traditional authenticity verification and real-time threat mitigation.

Keywords

Code Signing, Generative AI, Code Integrity, Supply Chain Security, Insider Threats, AI Security, GNN.

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