Artificial Intelligence and Terrorism: Misuse, Large Language Models, and Contemporary Counterterrorism Approaches

Keywords:
Artificial Intelligence, Terrorism, Large Language Models, Counter-Terrorism, Security.

Abstract:
The fast spread of artificial intelligence (AI), notably generative AI and large language models (LLMs), has opened up a new security challenge in the context of terrorism. This work aims to examine the correlation between artificial intelligence and terrorism in two primary aspects: misuse of AI by terrorists and violent extremists, and the application of AI in counter-terrorism. The analysis demonstrates that AI does not transform the essence of terrorism but enhances various aspects of its operational activity. Namely, AI contributes to terrorist propaganda, radicalization, multilingual communication, and manipulation of synthetic media. At the same time, AI can be applied to counter-terrorism efforts with the help of open source intelligence, analytical assistance, and priority setting. However, these opportunities are followed by serious problems and ethical dilemmas associated with hallucinations, obsolete or unverifiable outputs, bias, over-surveillance, privacy issues, and risks of automation in critical security situations. Hence, AI should not be considered separately as a security threat or solution but be regarded as a multiplier whose impact depends on regulation and human oversight.

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L. Radenović, “Artificial Intelligence and Terrorism: Misuse, Large Language Models, and Contemporary Counterterrorism Approaches,” in Sinteza 2025 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2026, pp. 94-99. doi:10.15308/Sinteza-2026-94-99

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Radenović, L. (2026). Artificial Intelligence and Terrorism: Misuse, Large Language Models, and Contemporary Counterterrorism Approaches. Paper presented at Sinteza 2025 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2026-94-99

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