Emerging threats in AI: a detailed review of misuses and risks across modern AI technologies

Abstract

The swift evolution of artificial intelligence (AI) has enabled unprecedented capabilities across domains, while simultaneously introducing critical vulnerabilities that can be maliciously exploited or cause unintended harm. Although multiple initiatives aim to govern AI-related risks, a comprehensive and systematic understanding of how AI systems are actively misused in practice remains limited. This paper presents a systematic review of AI misuse across modern AI technologies. We analyze documented incidents, attack mechanisms, and emerging threat vectors, drawing from existing AI risk repositories, prior taxonomies, and empirical case reports. These sources are synthesized into a unified analytical framework that categorizes AI misuse across nine primary domains. Our analysis identifies nine major domains of AI misuse: (1) Adversarial Threats, (2) Privacy Violations, (3) Disinformation, Deception, and Propaganda, (4) Bias and Discrimination, (5) System Safety and Reliability Failures, (6) Socioeconomic Exploitation and Inequality, (7) Environmental and Ecological Misuse, (8) Autonomy and Weaponization, and (9) Human Interaction and Psychological Harm. Within each domain, we examine distinct misuse patterns, providing technical insights into exploitation mechanisms, documented real-world cases with quantified impacts, and recent developments such as large language model vulnerabilities and multimodal attack vectors. We further evaluate existing mitigation strategies, including technical security frameworks (e.g., MITRE ATLAS, OWASP Top 10 for Large Language Models, MAESTRO), regulatory initiatives (e.g., EU AI Act, NIST AI Risk Management Framework), and compliance standards. The findings reveal substantial gaps between the rapid advancement of AI capabilities and the robustness of current defensive, governance, and mitigation mechanisms, with adversaries holding persistent advantages across most attack categories. This work contributes by (i) systematically consolidating fragmented AI risk repositories and misuse taxonomies, (ii) developing a unified taxonomy grounded in both theoretical models and empirical incident data, (iii) critically assessing the effectiveness of existing mitigation approaches, and (iv) identifying priority research gaps necessary for advancing more secure, ethical, and resilient AI systems.

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