AI-Generated Malware: Risks, Detection Strategies, and Defensive Counter measures

Abstract

The convergence of large language models (LLMs), code-generation systems, and generative AI more broadly has introduced a new dimension to the malware threat landscape. Where once malware development required specialized technical expertise, generative AI tools have lowered the barrier to entry for creating malicious code, obfuscating it, and adapting it to evade detection. This paper surveys the emerging risks posed by AI-generated malware, including polymorphic and metamorphic code generation, automated vulnerability discovery, AI-assisted social engineering payload delivery, and adversarial evasion of machine-learning-based detection systems. We then review the corresponding landscape of detection strategies, spanning static and dynamic analysis, machine-learning-based classifiers, behavioral and heuristic detection, and AI-versus-AI defensive frameworks in which generative and detection models compete in an escalating arms race. Finally, we examine defensive countermeasures at the technical, organizational, and policy levels, including secure-by-design software development practices, threat intelligence sharing, AI model governance, and regulatory approaches to constraining misuse of generative AI systems. We conclude that defending against AI-generated malware requires a layered, adaptive security posture that integrates traditional cybersecurity fundamentals with AI-aware detection and governance mechanisms, since no single technical control is likely to remain effective against a continuously evolving, AI-augmented adversary.