This research introduces AttackLLM, a novel approach that leverages large language models to automatically generate diverse attack patterns for Industrial Control Systems (ICS) security testing.

• Addresses the critical challenge of limited attack data availability in ICS environments
• Reduces dependency on expensive human expertise for security testing
• Generates comprehensive attack patterns that can identify previously unknown vulnerabilities
• Demonstrates how AI can enhance cybersecurity in critical infrastructure

This innovation matters because it provides security teams with a cost-effective way to test and strengthen industrial systems against a wider range of potential threats, ultimately protecting essential infrastructure from sophisticated cyberattacks.

AttackLLM: LLM-based Attack Pattern Generation for an Industrial Control System