Unit 42 research explores how AI is currently used in malware, from superficial integrations to advanced decision-making, and its future impact.

Unit42 is a cybersecurity research team known for its  analysis of cyber threats, malware, and cyber attack trends. Through research reports, threat intelligence briefings, and data analysis, Unit42 offers insights into emerging cyber threats and adversary tactics. Readers can learn about threat detection methodologies, vulnerability trends, and cyber attack attribution to enhance their cybersecurity posture and protect their organizations from advanced cyber threats.

Unit 42

Unit 42 researchers analyzed two real-world malware samples that integrate LLMs into their operation. The first is a .NET infostealer that calls OpenAI's GPT-3.5-Turbo API for evasion technique naming and C2 communication, but the AI integration is largely non-functional — technique names are logged but never executed, making it 'AI theater.' The second is a Golang-based dropper for Sliver malware that uses GPT-4 to assess whether a target environment is safe before deploying its payload, replacing traditional hardcoded allow/deny lists with LLM-based decision-making. Unit 42 identifies three categories of AI use in malware: AI-written malware, AI-assisted remote decision-making (C2), and locally executed agentic flows — only the first two have been observed in the wild. The research concludes that while current AI integration in malware is immature, it lowers the barrier for less-skilled attackers and signals a concerning trajectory toward more capable AI-driven threats.

Analyzing the Current State of AI Use in Malware

AI Theater: A .NET Infostealer’s Illusory LLM Features

AI-Gated Execution: A Malware Dropper's LLM-Based Safety Assessment