Emotional Malware: A Review of Emotion-Driven Cyber Threats, Psychological Manipulation, and AI-Enabled Social Engineering

This paper provides an initial conceptualization of emotional malware. It combines psychological manipulation, personalization aided by artificial intelligence (AI), and social engineering. It targets the trust of individuals and subsequently their confidence in their decision-making, so that the attack surface is the human population. The level of success in an emotional malware attack is dependent on the strength of the emotional manipulation and the use of emotional triggers. Examples of present-day attack scenarios are the recent breaches of MGM Resorts and deepfake financial frauds. In today’s world, human-centered cybersecurity is of utmost importance. Generative AI is showing exponential growth, making existing detection methods obsolete. Emotional malware is designed to employ new attack surfaces and techniques. A layered approach to security is needed to identify and counter real-world threats and attacks with the help of AI and machine learning. Content provenance and zero-trust verification should be implemented in every organization or individual. There are five impact dimensions: Organizational, Psychological, Legal, Ethical, and Societal. It encourages the use of an interdisciplinary approach which integrates Cybersecurity, Psychology, Behavioral Science, HCI, and Design. It is critical to understand the future research directions and their associated challenges. Empirical Validation: Formalize the Emotional Malware framework through mathematical and practical trials. The evaluation of emotional trigger analysis and detection assesses the success of content generation based on AI and examines explainable AI (XAI) and multimodal detection. Victim Recovery helps people heal after emotional abuse and scams. Conduct a comparative analysis and restructure Security Awareness Training from cue-based to cognitive-bias inoculation. "While it is vital to improve the detection of emotion-driven cyber threats, we must consider the cognitive vulnerabilities of humans, which will improve our resilience to those attacks."

Alanazi, S., Asif, S., Caird-Daley, A., & Moulitsas, I. (2025). Unmasking deepfakes: a multidisciplinary examination of social impacts and regulatory responses. Human-Intelligent Systems Integration, 2025 7:1, 7(1), 131–153. https://doi.org/10.1007/S42454-025-00060-4 Ali, G., Rashid, J., Ul Hussnain, M. R., Tariq, M. U., Ghani, A., & Kwak, D. (2024). Beyond the Illusion: Ensemble Learning for Effective Voice Deepfake Detection. IEEE Access, 12, 149940–149959. https://doi.org/10.1109/ACCESS.2024.3457866 Gallo, L., Gentile, D., Ruggiero, S., Botta, A., & Ventre, G. (2024). The human factor in phishing: Collecting and analyzing user behavior when reading emails. Computers & Security, 139, 103671. https://doi.org/10.1016/J.COSE.2023.103671 Gonzaga, K., Serra, S., Gomes, M., & Malta, S. (2026). AI-Powered Social Engineering: Emerging Attack Vectors, Vulnerabilities, and Multi-Layered Defense Strategies. Computers, 2026, Vol. 15, Page 128, 15(2), 128. https://doi.org/10.3390/COMPUTERS15020128 Hillman, D., Harel, Y., & Toch, E. (2023a). Evaluating organizational phishing awareness training on an enterprise scale. Computers & Security, 132, 103364. https://doi.org/10.1016/J.COSE.2023.103364 Hillman, D., Harel, Y., & Toch, E. (2023b). Evaluating organizational phishing awareness training on an enterprise scale. Computers & Security, 132, 103364. https://doi.org/10.1016/J.COSE.2023.103364 Lee, J., & John, K. K. (2025). Detecting Deepfakes Through Emotion?: Facial Expression and Emotional Contagion as Dual Indicators of Deepfake Credibility. Applied Cognitive Psychology, 39(6), e70141. https://doi.org/10.1002/ACP.70141 Mansoor, N., & Iliev, A. I. (2025). Explainable AI for DeepFake Detection. Applied Sciences, 2025, Vol. 15, Page 725, 15(2), 725. https://doi.org/10.3390/APP15020725