Generative AI (GenAI) has revolutionized content creation by combining human ingenuity with computational power. However, this technology also poses significant security risks because cybercriminals leverage GenAI to launch sophisticated, scalable, and adaptive attacks. These threats include AI-powered phishing campaigns, hyperrealistic deepfakes, and the constantly evolving AI-generated malware. The consequences extend beyond data breaches and financial losses, thus undermining trust in digital systems. Prompt injection has emerged as a critical vulnerability in large language models (LLMs), enabling adversaries to manipulate outputs and execute unauthorized actions. Evaluating the offensive capabilities of LLMs is crucial to anticipating threats and developing comprehensive mitigation strategies. Real-world incidents such as the “Black Mamba” attack, automated phishing campaigns, AI-generated malware, and financial scams via deepfakes illustrate the potential for harm. Strategies for detecting and mitigating these risks include advanced threat detection, ethical AI design, regular model audits, jailbreak prevention, regulatory compliance, employee training, cross-industry collaboration, and resilient infrastructure. As GenAI continues to evolve, proactive and collaborative approaches are essential for ensuring the security and resilience of individuals, organizations, and governments in an AI-driven world.