Preventing Jailbreak Prompts as Malicious Tools for Cybercriminals: A Cyber Defense Perspective

Jean Marie Tshimula (), Xavier Ndona, D’Jeff K. Nkashama (), Pierre-Martin Tardif, Froduald Kabanza, Marc Frappier () and Shengrui Wang
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Jean Marie Tshimula: Universite de Sherbrooke, Department of Computer Science
Xavier Ndona: Harrisburg University of Science and Technology
D’Jeff K. Nkashama: Universite de Sherbrooke, Department of Computer Science
Pierre-Martin Tardif: Universite de Sherbrooke, Department of Computer Science
Froduald Kabanza: Universite de Sherbrooke, Department of Computer Science
Marc Frappier: Universite de Sherbrooke, Department of Computer Science
Shengrui Wang: Universite de Sherbrooke, Department of Computer Science

A chapter in Technology Management for Intelligent, Open and Responsible Organizations and Ecosystems, 2026, pp 77-85 from Springer

Abstract: Abstract Jailbreak prompts pose a significant threat in AI and cybersecurity, as they are crafted to bypass ethical safeguards in large language models, potentially enabling misuse by cybercriminals. This paper analyzes jailbreak prompts from a cyber defense perspective, exploring techniques like prompt injection and context manipulation that allow harmful content generation, content filter evasion, and sensitive information extraction. We assess the impact of successful jailbreaks, from misinformation and automated social engineering to hazardous content creation, including bioweapons and explosives. To address these threats, we propose strategies involving advanced prompt analysis, dynamic safety protocols, and continuous model fine-tuning to strengthen AI resilience. Additionally, we highlight the need for collaboration among AI researchers, cybersecurity experts, and policymakers to set standards for protecting AI systems. We illustrate these cyber defense approaches through case studies, promoting responsible AI practices to maintain system integrity and public trust.

Keywords: Jailbreak prompts; AI cybersecurity; Prompt injection; AI resilience (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-23124-6_10

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