Measuring AI Agents' Progress on Multi-Step Cyber Attack Scenarios
We evaluate the autonomous cyber-attack capabilities of frontier AI models on two purpose-built cyber ranges-a 32-step corporate network attack and a 7-step industrial control system attack-that require chaining heterogeneous capabilities across extended action sequences. By comparing seven models released over an eighteen-month period (August 2024 to February 2026) at varying inference-time compute budgets, we observe two capability trends. First, model performance scales log-linearly with inference-time compute, with no observed plateau-increasing from 10M to 100M tokens yields gains of up to 59%, requiring no specific technical sophistication from the operator. Second, each successive ...
| Authors | Linus Folkerts, Will Payne, Simon Inman et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 March 2026 |
| Last updated at source | 17 March 2026 |
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