This article discusses how attackers are leveraging Large Language Models (LLMs) to boost their capabilities in creating polymorphic malware, which changes its code structure to evade detection by traditional security systems. It emphasizes the challenge this poses to cybersecurity as AI technology becomes more advanced and accessible. Affected: cybersecurity, malware, AI technologies
Keypoints :
- LLMs are being adopted more quickly by attackers than defenders, enhancing their cyber capabilities.
- Polymorphic malware changes its code dynamically, making it difficult to detect with traditional methods.
- Attackers utilize LLMs to generate malicious code on-the-fly, resulting in unique malware variants with each execution.
- CyberArk demonstrated the effectiveness of ChatGPT in producing polymorphic malware.
- Underground discussion regarding custom AI models like WormGPT shows the serious interest in AI-assisted malware development.
- Polymorphic malware can evade both static and network detection methods by disguising malicious activity as normal API traffic.
- Behavioral detection is challenged as AI-enabled attacks can blend with legitimate processes, complicating identification.