Seeds of Deception: Securing AI-Driven Agriculture Against Adversarial Threats

Abstract

The integration of artificial intelligence (AI) and the Internet of Things (IoT) into agriculture is redefining how crops are cultivated, monitored, and protected. This research builds upon an implemented IoT-driven plant monitoring prototype combined with a convolutional neural network (CNN) for leaf disease classification. The system achieved 96% accuracy on benchmark datasets and 76% accuracy on live samples, demonstrating the technical promise of digital agriculture. However, while effective in functionality, the prototype highlights a broader concern: agricultural digitization is evolving faster than its security safeguards, creating fertile ground for adversarial exploitation. To address this gap, the study applies threat modeling to the implemented prototype, identifying vulnerabilities in sensor integrity, data pipelines, and AI model robustness. Potential adversarial vectors include sensor spoofing, data poisoning, and adversarial image inputs capable of undermining disease detection accuracy. These findings serve as a foundation for expanding the analysis toward two emerging risks that elevate agricultural cybersecurity into the domain of biowarfare.

First, the increasing reliance on cloud-hosted genetically modified organism (GMO) repositories presents a novel threat. Adversarial prompt engineering attacks on agricultural AI assistants could leak or corrupt sensitive genetic data, embedding harmful traits within seeds. Such tampering collapses the boundary between digital compromise and biological sabotage, threatening food security at scale. Second, agricultural AI infrastructures are increasingly dependent on high-density data centers that consume large volumes of potable water for cooling. A targeted cyber-physical campaign that overloads these facilities could deliberately drain water reserves, induce man-made drought conditions, and destabilize surrounding ecosystems. This risk reframes data centers not only as computational assets but also as critical ecological choke points. By combining the practical threat modeling of an IoT–AI prototype with conceptual extensions into GMO and data center vulnerabilities, this work establishes a novel framework for agricultural cyber-biosecurity. It underscores the urgency of interdisciplinary safeguards to prevent the transformation of smart farming from a tool of resilience into a vector of biowarfare.