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This paper surveys the dual-use risks of Large Language Models (LLMs) and generative AI in the cybersecurity domain, highlighting their roles in both enhancing automated defenses and facilitating sophisticated cyberattacks. It reveals that LLM-generated malware is expected to rise dramatically, accounting for 50% of detected threats by 2025, necessitating advanced security frameworks. The authors synthesize insights from over 70 sources, providing practical recommendations for responsible LLM deployment to mitigate risks while harnessing their capabilities for cybersecurity.
LLM-generated malware could surge to 50% of detected threats by 2025, underscoring an urgent need for robust cybersecurity frameworks.
Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks. These technologies power real-time threat detection, phishing defense, secure code generation, and vulnerability exploitation at unprecedented scales. LLM-generated malware alone is projected to account for 50% of detected threats in 2025, up from just 2% in 2021, emphasizing the need for next-generation security frameworks.This paper presents a comprehensive survey of the 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, this work synthesizes insights from real-world case studies across platforms like Google Play Protect, Microsoft Defender, Amazon Web Services (AWS), Apple鈥檚 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, including model watermarking, adversarial defense, and cross-industry collaboration鈥攕etting a new benchmark for rigorous, holistic cybersecurity research at the intersection of AI and threat defense鈥攁nd offering 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.
