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About this paper
Linus Folkerts, Will Payne, Simon Inman, Philippos Giavridis, Joe Skinner, Sam Deverett, James Aung, Ekin Zorer and 6 more
Researchers at the AI Security Institute evaluated frontier AI models' ability to autonomously execute multi-step cyberattacks across simulated corporate and industrial control system networks, finding consistent performance growth over an 18-month period and with increased inference-time compute, alongside the discovery of novel attack methods.
Key insights
- AI agent performance on multi-step cyberattacks scales log-linearly with inference-time compute, showing continuous gains up to 100 million tokens without plateauing.
- Consistent and substantial performance improvements were observed across successive AI model generations over an 18-month period, driven by both increased token efficiency and deeper specialist capabilities.
- AI agents demonstrated emergent behaviors, such as bypassing intended attack paths by directly probing proprietary protocols and exploiting unintended software bugs, indicating potentially creative problem-solving.
Summary by alphaXiv.
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