Large Language Models (LLMs) and Generative AI (GenAI) systems, such as ChatGPT, Claude, and Gemini, are revolutionizing cybersecurity, enabling both automated defenses and sophisticated attacks. These technologies facilitate real-time threat detection, phishing defense, secure code generation, and vulnerability exploitation at unprecedented scales. Predictions indicate that LLM-generated malware could account for 50% of detected threats by 2025, up from just 2% in 2021. Navigating this highly automated threat landscape in 2026 necessitates next-generation security frameworks.
This paper presents a comprehensive survey of beneficial and malicious applications of LLMs in cybersecurity, including zero-day detection, DevSecOps, federated learning, synthetic content analysis, and explainable AI (XAI). Drawing on a review of over 70 academic papers, industry reports, and technical documents, the work synthesizes insights from real-world case studies across platforms like Google Play Protect, Microsoft Defender, Amazon Web Services (AWS), Apple App Store, OpenAI Plugin Stores, Hugging Face Spaces, and GitHub, alongside emerging initiatives like the SAFE Framework and AI-driven anomaly detection.
We conclude with practical recommendations for responsible and transparent LLM deployment and trustworthy AI, including model watermarking, adversarial defense, and cross-industry collaboration, setting a new benchmark for rigorous, holistic cybersecurity research at the intersection of AI and threat defense. This provides a roadmap for secure, scalable LLM systems that serves as a critical reference for researchers, engineers, and security leaders navigating the complex challenges of AI-driven cybersecurity.
Blogger's Review: This article insightfully reveals the dual nature of LLMs in the cybersecurity domain, stressing the importance of vigilance against potential threats while leveraging technological advantages. With the surge in malware, establishing effective defense mechanisms becomes paramount.